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Rogue Board Ontology v1.3.0 — released 2026-05-20

211 board KPIs across 7 domains · I'mBoard · https://www.imboard.ai/ontology/explore

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All 211 KPIs from I'mBoard's typed board-metric ontology — across 7 domains, each defined, sourced, and benchmarked. 27 are industry-backed, 184 are editorial. Crawlable, printable, and built from the live registry.

Catalog version v1.3.0 · released 2026-05-20

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Fundraising 23 KPIs

Committed Amount

fundraising.committed_amount
currency Editorial
Description
Capital that investors have agreed to invest — including both soft commitments (verbal / handshake / IOI) and hard commitments (signed term sheet or executed subscription docs). Treat this as the round-progress odometer. Common pitfall: soft commitments are notoriously squishy — every published fundraising postmortem (per First Round Review and Bessemer founder essays) warns that founders over-count soft commits. Board-best-practice is to track soft vs hard separately or to define a haircut convention (e.g. 50% of soft) at the start of the round.
Formula
Sum of all investor commitments (soft + hard). Define and document the soft-vs-hard convention per round — some companies report only hard commitments to the board, others report a blended number with footnote.
Why it matters
Primary leading indicator for whether the round will close on target and on time. Pacing against `target_raise` and `planned_close_date` tells the board whether intervention is needed.
Interpretation guidance
Healthy rounds typically hit 50% committed at the round midpoint and 80%+ before final closing mechanics begin. A committed amount stuck below 30% past the midpoint usually signals a re-pricing or scope-cut conversation with the board.
Source
imboard Editorial
Related KPIs
fundraising.target_raise fundraising.total_received fundraising.round_completion_pct fundraising.investors_in_pipeline fundraising.minimum_close_amount

Founder Dilution

fundraising.founder_dilution
percentage (%) Industry-backed
Description
Percentage of founders' fully-diluted ownership that is given up in the new round, including any pre-close option-pool top-up (the "option pool shuffle" — option-pool expansion taken in the pre-money dilutes existing holders rather than new investors). Common pitfall: founders often quote the "investor dilution" (new money / post-money) and forget the option-pool top-up component. The Carta State of Private Markets quarterly reports publish stage-typical dilution ranges that boards should use as a sanity check.
Formula
founder_dilution_pct = (founder_shares_pre − founder_shares_post) / founder_shares_pre × 100. Includes both new-money dilution and any pre-close option-pool top-up borne in the pre-money. Per Carta State of Private Markets methodology.
Why it matters
Tracks founder skin-in-the-game over time — sustained ownership matters for long-term motivation and signaling to future investors. Boards balance dilution discipline against capital needs.
Interpretation guidance
Per Carta State of Private Markets benchmarks, typical per-round dilution for the priced round (excluding pool top-up) is 18–22% at seed, 18–22% at A, 12–18% at B, 10–15% at C+. Out-of-band dilution either signals weak negotiating position or a strategic priced-up next-round set-up.
Benchmark
p25 12 % · median 18 % · p75 24 %
Related KPIs
fundraising.pre_money_valuation fundraising.post_money_valuation fundraising.total_round_size

Fundraising Assumptions

fundraising.assumptions
text Editorial
Description
Explicit assumptions underlying the fundraising plan: valuation expectation, lead-investor probability, time-to-close, post-close runway, and what changes if any assumption breaks. Common pitfall: assumptions are made implicitly and only surface in the postmortem. Boards should require this section to be reviewed each update — a board update where assumptions never change suggests they are not being tested, not that they are correct.
Formula
Narrative — enumerated assumptions, each ideally with (1) the assumption, (2) the rationale, (3) what changes if it breaks.
Why it matters
Anchors the fundraising plan to falsifiable beliefs. Lets the board pre-agree on what would constitute a "this is not working, change the plan" trigger.
Interpretation guidance
Assumptions that diverge from `risk_factors` are a signal of inconsistency. Assumptions that have been broken without triggering plan change are the strongest red flag — pair with a board discussion of when to change course.
Source
imboard Editorial
Related KPIs
fundraising.strategy fundraising.risk_factors fundraising.target_raise fundraising.planned_close_date

Fundraising Risk Factors

fundraising.risk_factors
text Editorial
Description
Named risks that could prevent the round from closing as targeted — market conditions (general venture sentiment, sector-specific freeze), investor-side risk (anchor investor wobble, partner-meeting drop-off), company-side risk (a metric trending wrong direction, customer concentration concern surfaced in diligence), and timing risk (runway versus close date). Common pitfall: optimistic CEOs under-report risk factors. Boards should expect at least 2–3 named risks even in a healthy round — "no risks" is itself a risk signal.
Formula
Narrative — list of named risks, each ideally with a likelihood / mitigation pair.
Why it matters
Surfaces what could go wrong before it does — boards earn their seat by spotting risks the CEO is too close to see. Also a contract between CEO and board on what to watch.
Interpretation guidance
Watch for risks that persist across multiple updates with no mitigation movement — usually a sign the CEO needs board help. A new risk appearing late in the round (post-term-sheet) deserves immediate board attention.
Source
imboard Editorial
Related KPIs
fundraising.round_status fundraising.strategy fundraising.assumptions

Fundraising Strategy

fundraising.strategy
text Editorial
Description
Free-text narrative covering the planned fundraising approach for the current round: target investor types (lead profile, co-investors), timing, sequencing of the conversation, use of proceeds, milestones the round will get the company to, and the alternative scenarios if the primary plan slips. This is the "what is the CEO actually doing" section of the fundraising update. Common pitfall: strategy that does not name a target lead investor profile or use-of-proceeds milestone is not strategy — it is intent. Boards should push for specificity here.
Formula
Narrative — no calculation. Should cover (1) target investor profile, (2) sequencing / timing plan, (3) use of proceeds, (4) milestones funded, (5) downside scenarios.
Why it matters
Forces the CEO to articulate "what game we are playing" — boards offer better help when they understand the strategy, not just the numbers.
Interpretation guidance
A strategy that has not changed across consecutive board updates while the round has stalled is a red flag — typically requires a reframing conversation. A strategy that pivots every update is also a red flag — typically requires the board to push for commitment.
Source
imboard Editorial
Related KPIs
fundraising.round_status fundraising.target_raise fundraising.investors_in_pipeline fundraising.risk_factors fundraising.assumptions

Investors in Pipeline

fundraising.investors_in_pipeline
number Editorial
Description
Count of distinct investors actively engaged in the current round — defined as taken a first meeting and not yet declined or fully committed. Effectively a fundraising-funnel "qualified leads" number. Common pitfall: rosy pipelines that include investors who ghosted weeks ago — best practice (echoed across NfX, First Round Review, and Bessemer founder essays) is to age-out any investor with no contact in 14+ days. Track separately from total intros taken and from hard commitments to make the conversion math legible.
Formula
Count of distinct investors in active engagement (intro / first meeting / partner meeting / diligence). Excludes declined, closed, or stale (>14 days no contact) investors.
Why it matters
Healthy round dynamics rest on competitive tension — a thin pipeline means weaker negotiating position on price and terms. Board reads this to gauge whether the CEO needs help with intros.
Interpretation guidance
Rule of thumb across stages: ~40–60 first meetings → ~10–15 partner meetings → ~3–5 term sheets → 1 lead. Active pipeline (post first meeting, pre decline) below 8–10 at the round midpoint typically warrants the board opening their networks.
Source
imboard Editorial
Related KPIs
fundraising.committed_amount fundraising.round_status fundraising.strategy

Key Milestones

fundraising.key_milestones
text Editorial
Description
Container handle for the field-array of named fundraising milestones the board should track to the close — each entry tracks milestone name, type (e.g. term-sheet signing, IC presentation, close), target date, status (upcoming / in-progress / completed / at-risk), responsible party, and notes. The "what has to happen, by when, and who owns it" surface that turns the round narrative into a tracked plan. Renders via the CollapsibleFormItemCardGallery widget (the reused gallery pattern shared with sales pipeline deals and HR key hires / openings). Common pitfall: milestones carried forward from prior packs without status updates — these should be refreshed each period so the board sees real progress, not a stale wishlist.
Formula
Container — field-array of milestone items (name, type, status, targetDate, responsibleParty, notes). No aggregate calculation; the surface makes individual milestones and their owners visible at the board level.
Why it matters
Converts the round narrative into a tracked plan with owners and dates — the board can see at a glance which milestones are slipping and who to press. Without named-milestone visibility, the board only learns of a stalled round when the close date slips.
Interpretation guidance
Watch for milestones that have been "in-progress" across multiple quarterly packs without resolving, and for an at-risk or blocked milestone on the critical path to close (term sheet, lead commitment, IC presentation). Pair with `fundraising.round_status` and `fundraising.planned_close_date` — a clean milestone list alongside a slipping close date usually means the milestones are being optimistically maintained.
Source
imboard Editorial
Related KPIs
fundraising.round_status fundraising.planned_close_date fundraising.target_raise fundraising.committed_amount fundraising.risk_factors

Minimum Close Amount

fundraising.minimum_close_amount
currency Editorial
Description
Floor — the smallest amount of committed capital required to legally close the round (often set in the subscription agreement) or the strategically smallest amount management would accept before re-pricing or pausing. Common pitfall: a `target_raise` of $10M and a `minimum_close_amount` of $4M tells a very different story than a target of $10M and a minimum of $9M — boards should always see both. Per common practice (NVCA Model Documents allow flexibility here), the minimum is typically 50–75% of target at seed, 70–90% at A+.
Formula
Currency floor — typically defined in the subscription agreement or by management. Distinct from `target_raise` (the ask) and `committed_amount` (in-progress signal). Per NVCA Model Documents convention.
Why it matters
Defines the round's "this is enough to ship" line. Pacing relative to the minimum is the worst-case board view; pacing relative to target is the best-case view — both matter.
Interpretation guidance
Per common practice: minimum 50–75% of target at seed, 70–90% of target at A+. A minimum-equals-target round signals high commitment to the headline number; a minimum well below target signals optionality for a "step-down close." Triggered minimums (below floor) require management to revise scope and re-baseline runway.
Source
imboard Editorial
Related KPIs
fundraising.target_raise fundraising.committed_amount fundraising.round_completion_pct finance.runway_months

Minimum Valuation

fundraising.minimum_valuation
currency Editorial
Description
The lowest pre-money valuation management would accept to close the current round — the valuation walk-away floor. Distinct from the precise NVCA-defined `pre_money_valuation` (the single negotiated point that actually prices the round): this is the bottom of the acceptable band the team set going in. Common pitfall: teams anchor only on a target valuation and have no pre-agreed floor, so in a soft market they negotiate against themselves with no board-sanctioned line. Pair with `fundraising.target_valuation` to give the board the band, and read both against stage-relative ranges from quarterly Carta / PitchBook reports.
Formula
Currency floor — set by management as the valuation walk-away line for the round. Distinct from `pre_money_valuation` (the single negotiated price) and `target_valuation` (the valuation being run to). Typically expressed as pre-money to match `pre_money_valuation`.
Why it matters
Gives the board the worst-case price of the round before negotiations start — the line below which the team should pause, re-scope, or consider a bridge rather than accept a down-round-anchoring price. Without a pre-agreed floor, valuation discipline erodes in a soft market.
Interpretation guidance
Read as the bottom of the valuation band alongside `target_valuation`. A minimum well below stage-median pre-money (per quarterly Carta / PitchBook reports) signals the team is bracing for a hard market; a minimum equal to target signals high conviction (or inflexibility). A breached floor (a term sheet below the minimum) is a board-decision event, not a management one.
Source
imboard Editorial
Related KPIs
fundraising.target_valuation fundraising.pre_money_valuation fundraising.post_money_valuation fundraising.minimum_close_amount

Outstanding Convertible Amount

fundraising.convertible_outstanding
currency Industry-backed
Description
Total principal value of SAFEs and convertible notes outstanding that have not yet converted to equity. These convert at the next priced round, typically at a discount or valuation cap (per the standard Y Combinator SAFE templates and the National Venture Capital Association convertible-note model). Common pitfall: a SAFE stack quietly accumulating between rounds can convert into 15–25% dilution at the next priced round, surprising founders who modeled "we only sold 10% in this priced round" math. Boards should always see the fully-diluted cap table including SAFE conversion.
Formula
Sum of principal outstanding on all unconverted convertible instruments (SAFEs per the Y Combinator post-money SAFE template; convertible notes per the NVCA Model Documents). Pre-conversion — actual dilution depends on the next-round price and the SAFE caps/discounts.
Why it matters
Hidden dilution that hits at the next priced round. A material SAFE stack changes the math on what a "20% Series A" actually costs the founders.
Interpretation guidance
When `convertible_outstanding` is more than ~10% of the company's next-likely post-money valuation, the board should require a fully-diluted cap-table walk-through at the next priced round modeling exercise. Highest-cap and lowest-cap SAFE conversion paths should both be modeled.
Related KPIs
fundraising.pre_money_valuation fundraising.post_money_valuation fundraising.founder_dilution

Planned Close Date

fundraising.planned_close_date
date Editorial
Description
Calendar date by which the round is expected to close (final wires received, definitive documents signed). Compared against `finance.runway_months` to detect a fundraising-against-the-clock situation. Common pitfall: planned close dates routinely slip 30–90 days in practice (collected founder postmortems on First Round Review) — boards should ask for both an "expected" and a "no-deal" date and watch the gap to actual runway exhaustion.
Formula
Calendar date. Not derived — set by management. Compare to today + `finance.runway_months` to surface runway-versus-close risk.
Why it matters
Single most-important fundraising deadline — drives urgency, board cadence, and bridge-financing decisions. Slippage here is the leading indicator that the round is in trouble.
Interpretation guidance
When the planned close date is within 2 months of runway exhaustion (i.e. `runway_months` ≤ months_to_planned_close + 2), the board should be in active conversation about bridge financing or scope cuts. A slipping date should be paired with explicit re-baseline of runway.
Source
imboard Editorial
Related KPIs
fundraising.round_status fundraising.committed_amount fundraising.target_raise finance.runway_months

Post-Money Valuation

fundraising.post_money_valuation
currency Industry-backed
Description
Company valuation immediately after the new round closes, including the new capital raised — the canonical "valuation" number quoted in TechCrunch headlines. Per NVCA Model Documents, post-money = pre-money + new money raised. Common pitfall: post-money math gets messy with SAFEs — modern post-money SAFEs (the YC 2018+ form, per the Y Combinator SAFE primer) fix dilution at the SAFE's valuation cap regardless of subsequent priced-round pricing, so the board should always reconcile the headline post-money against the fully-diluted cap table.
Formula
post_money_valuation = pre_money_valuation + total_round_size. Per NVCA Model Documents. With outstanding post-money SAFEs, reconcile against the fully-diluted cap table — SAFE dilution is fixed at the cap regardless of priced-round price.
Why it matters
The headline number the company carries forward — sets the goalposts for the next round (a down-round means raising at a lower post-money) and the strike-price floor for new option grants.
Interpretation guidance
Watch the post-money-to-ARR multiple (or post-money-to-net-burn if pre-revenue): public sources covering 2024–2025 (e.g. SaaS Capital "Private SaaS Company Valuations" report, valuation-multiples section; Sapphire / KBCM SaaS Survey, "valuations" chapter) show median ARR multiples have compressed materially from 2021 peaks. Pull the current edition for the live range — do not rely on a memorized number — and flag out-of-band multiples as next-round price risk. Where you only have rough heuristics, mark them as "directional, not citation-grade" rather than fabricating a precise band.
Related KPIs
fundraising.pre_money_valuation fundraising.total_round_size fundraising.founder_dilution sales.arr finance.net_burn_rate

Pre-Money Valuation

fundraising.pre_money_valuation
currency Industry-backed
Description
Company valuation negotiated with investors immediately before the new round closes — the denominator for the new investors' ownership math. Per the NVCA Model Documents, pre-money = post-money − new money raised. Common pitfall: when convertible instruments (SAFEs, notes) are outstanding, the "headline" pre-money the CEO quotes and the effective pre-money after conversion can differ materially — the board should always ask for both. Equally important: option-pool top-ups taken pre-close come out of the pre-money share count, diluting founders not investors (the "option pool shuffle").
Formula
pre_money_valuation = post_money_valuation − total_round_size. Per NVCA Model Documents convention. Effective pre-money after SAFE/note conversion can be lower than headline — surface both when convertibles are material.
Why it matters
Sets the price for the round. Drives `founder_dilution`, the option-pool top-up math, and the precedent for the next round (down-rounds are punishing to recover from).
Interpretation guidance
Compare to stage-relative ranges from quarterly Carta / PitchBook reports (e.g. seed median has moved $12–18M post-money in 2024–2025). A pre-money below stage median typically signals either harsher terms or a strategic discount; above stage median demands real metric backing.
Related KPIs
fundraising.post_money_valuation fundraising.total_round_size fundraising.founder_dilution fundraising.convertible_outstanding

Round Completion %

fundraising.round_completion_pct
percentage (%) Editorial
Description
Progress of the round expressed as committed capital divided by target. Read alongside `round_status` and elapsed-time-in-round to detect stalls. Common pitfall: percentage progress is misleading when measured against a shifting `target_raise` — when management lowers the target mid-round, the percentage jumps without any new commitments arriving. The board should always be told when this is a target revision vs. a real progress event.
Formula
round_completion_pct = (committed_amount / target_raise) × 100. When using soft+hard committed, footnote the convention.
Why it matters
Single-number pacing signal — board members glance at it first when scanning a fundraising update. Pairs naturally with elapsed-time-in-round to surface stalls.
Interpretation guidance
Rough pacing checkpoints from collected practitioner essays (NfX, Bessemer founder content): 30% by 4–6 weeks in, 60% by week 8–10, 90%+ by week 12. Sustained pacing below these typically signals a need to widen the investor net or revise the target.
Source
imboard Editorial
Related KPIs
fundraising.target_raise fundraising.committed_amount fundraising.round_status fundraising.planned_close_date

Round Status

fundraising.round_status
text Editorial
Description
Current phase of the active fundraising round on a coarse state machine (e.g. not-started, in-progress, term-sheet, closing, closed). The board reads this to know which playbook applies — pipeline-building, diligence, closing, or post-close communications. Common pitfall: the field drifts when a round stalls or pivots, so treat each phase change as a board-update trigger. The PhasePlaybook widget binds to this enum and surfaces the appropriate phase guidance read-only beside the editor.
Formula
Categorical state derived from operational status — no calculation. Typical states: not-started, in-progress, term-sheet, closing, closed, paused.
Why it matters
Anchors every other fundraising number in board context — the same target_raise is read differently mid-pipeline than at closing. Drives which phase playbook the board should be advising on.
Interpretation guidance
Treat a phase regression (e.g. term-sheet → in-progress) as a yellow flag and pair it with `risk_factors`. Time spent in any single phase beyond stage-typical norms (6–9 months at seed, 4–6 months at A/B) signals a stalled round.
Source
imboard Editorial
Related KPIs
fundraising.target_raise fundraising.committed_amount fundraising.planned_close_date fundraising.risk_factors

Target Raise

fundraising.target_raise
currency Editorial
Description
Target gross capital the company intends to raise in the currently active round (the "ask"). This is the headline number the CEO walks investors through and the board uses to sanity-check dilution and runway implications. Note the distinction from `total_round_size` (which can include third-party participation beyond the company-led ask) and from `minimum_close_amount` (the floor at which the round can close). Common pitfall: the target is updated mid-process when investor demand or strategy shifts — every change deserves a board note.
Formula
Plain currency target — no derivation. Distinct from `total_round_size` (which may include strategic / employee allocations beyond the lead ask) and `minimum_close_amount` (the floor for the round to close).
Why it matters
Defines the contract between management and the board for this round — every downstream KPI (round_completion_pct, founder_dilution, runway extension) is calibrated against it.
Interpretation guidance
Compare against stage norms (per Carta / PitchBook quarterly reports): pre-seed $0.5–3M, seed $2–5M, Series A $8–20M, Series B $20–60M. A target significantly above stage norm warrants extra board scrutiny on burn assumptions and investor fit.
Source
imboard Editorial
Related KPIs
fundraising.committed_amount fundraising.total_round_size fundraising.minimum_close_amount fundraising.round_completion_pct fundraising.founder_dilution

Target Valuation

fundraising.target_valuation
currency Editorial
Description
The pre-money valuation the current round is being run to land — the valuation "ask" that anchors the pitch and the dilution math management is targeting. Distinct from `pre_money_valuation` (the precise NVCA-defined price the round actually closes at, known only once a term sheet is signed): this is the aim, set going in. The board reads the target alongside `fundraising.minimum_valuation` as a valuation BAND — the two together tell a very different story than a single point. Common pitfall: a target valuation set on 2021-vintage multiples in a compressed market; always sanity-check against current stage-relative ranges from quarterly Carta / PitchBook / SaaS Capital reports.
Formula
Currency target — the pre-money valuation management is running the round to land. Set going in; distinct from `pre_money_valuation` (the realized price at signing) and `minimum_valuation` (the floor). Typically expressed as pre-money to match `pre_money_valuation`.
Why it matters
Defines the best-case price of the round and the dilution math management is targeting — every downstream economic KPI (`founder_dilution`, option-pool top-up) is calibrated against where the round actually lands relative to this aim.
Interpretation guidance
Read as the top of the valuation band alongside `minimum_valuation`. Compare to stage-relative ranges from quarterly Carta / PitchBook / SaaS Capital reports — a target above stage-median pre-money demands real metric backing and raises next-round down-round risk if it cannot be defended. The gap between target and minimum is the team's implicit read on market softness: a wide band signals uncertainty, a tight band signals conviction.
Source
imboard Editorial
Related KPIs
fundraising.minimum_valuation fundraising.pre_money_valuation fundraising.post_money_valuation fundraising.total_round_size fundraising.founder_dilution

Total Capital Raised to Date

fundraising.total_capital_raised
currency Editorial
Description
Cumulative gross equity capital raised across all prior rounds (and the current round in-progress). Treated as historical context — investors and board members look at this to gauge capital efficiency (capital raised vs. ARR achieved). Common pitfall: includes all equity but typically excludes convertible debt that has not converted, venture debt principal, and grants — be explicit about what is and is not included when the number is presented. Capital efficiency benchmarks (per KBCM, SaaS Capital, and Bessemer State-of-the-Cloud) compare `total_capital_raised` to current ARR — e.g. "$30M raised, $10M ARR" is efficient at A but lean at B+.
Formula
Sum of gross equity capital raised across all rounds. Be explicit about inclusion of convertibles and exclusion of venture debt / grants — surface in a footnote.
Why it matters
Tracks capital efficiency over time and frames the company's next-round narrative ("we raised $X to get to $Y ARR"). Investors and board members use this for stage-vs-traction sanity-checking.
Interpretation guidance
Compare to current ARR for capital efficiency. The right ratio is ARR ÷ capital raised — most early-stage SaaS companies generate noticeably less ARR than they have raised (ratio typically well under 1.0x at Series A, with the gap intentional during growth-spend ramps). The KBCM (now Sapphire / KBCM) Private SaaS Company Survey publishes a "capital efficiency" cut for the current vintage; the Bessemer State of the Cloud report covers the same axis qualitatively. Pull the current edition for the live benchmark rather than relying on a memorized number, and tag any heuristic range as "directional, not citation-grade" if you cannot cite a specific section.
Source
imboard Editorial
Related KPIs
fundraising.total_round_size fundraising.total_received sales.arr finance.net_burn_rate

Total Received

fundraising.total_received
currency Editorial
Description
Cash that has actually been wired and cleared the company's bank account from investors in the current round. This is the cash-in-the-bank version of `committed_amount`. Common pitfall: commitments do not pay the bills — wiring can lag commitments by weeks to months for the second / third closes, and a committed-but-not-received delta of $5M+ can quietly extend the runway forecast incorrectly. Reconcile this against `finance.total_cash_in_bank` increases each period.
Formula
Sum of investor wires received and cleared in the current round. Always less-than-or-equal-to `committed_amount`; difference equals "to-be-wired" balance.
Why it matters
The only line of capital the company can actually deploy — runway forecasts based on `committed_amount` rather than `total_received` are aspirational, not operational.
Interpretation guidance
A widening gap between `committed_amount` and `total_received` past the first close warrants a follow-up — wiring delays beyond 30 days from signature increasingly correlate (per founder postmortems published on First Round Review) with investor regret or strategic shifts.
Source
imboard Editorial
Related KPIs
fundraising.committed_amount fundraising.target_raise finance.total_cash_in_bank finance.runway_months

Total Round Size

fundraising.total_round_size
currency Industry-backed
Description
Total new capital being raised in the current round across all participants — the lead, follow-on investors, employee/strategic allocations, and any side-letter pieces. This is the figure that goes into the post-money math. Common pitfall: companies sometimes confuse `total_round_size` with `target_raise` — the round size is final and used in valuation math, while the target is what management is aiming for and can move during the raise. Boards should expect a specific breakdown by investor when this number is reported.
Formula
Sum of all new-money allocations in the round (lead + follow-on + strategic + employee + side letters). Distinct from `target_raise` (intent) and `committed_amount` (in-progress signal).
Why it matters
Determines the round's post-money valuation and dilution math. Also signals investor concentration risk — a round with 80% from one investor differs structurally from a round with 5 equal participants.
Interpretation guidance
Round size noticeably below target typically signals investor demand weakness (consider repricing or scope cut). Round size meaningfully above target signals oversubscription — a healthy signal but raises governance questions on how allocations are decided.
Related KPIs
fundraising.target_raise fundraising.committed_amount fundraising.pre_money_valuation fundraising.post_money_valuation fundraising.founder_dilution

Venture Debt Available

fundraising.venture_debt_available
currency Editorial
Description
Undrawn capacity remaining on existing venture debt facilities. Optionality the company can call on quickly without re-pricing. Common pitfall: availability is conditional — most facilities require continued covenant compliance, and an available line can be pulled or frozen by the lender if cash, ARR, or other covenants slip (per the Bessemer venture-debt content and Battery Ventures primer). The board should treat `venture_debt_available` as a soft commitment, not a hard one, until drawn.
Formula
venture_debt_available = total_facility_committed − venture_debt_drawn − amounts no longer drawable (covenant restrictions, time-window expirations).
Why it matters
Strategic optionality — drawable capacity is a buffer for unexpected burn or a bridge to the next round. But it is contingent on staying inside covenants, so the board needs both this number and `venture_debt_covenant_status`.
Interpretation guidance
Available capacity of 3–6 months of net burn provides meaningful optionality. Less than ~1 month of burn in availability rarely justifies the facility complexity. Watch for facilities with expiring draw windows — undrawn capacity that vanishes on a calendar date.
Source
imboard Editorial
Related KPIs
fundraising.venture_debt_drawn fundraising.venture_debt_covenant_status finance.net_burn_rate finance.runway_months

Venture Debt Covenant Status

fundraising.venture_debt_covenant_status
text Editorial
Description
Stoplight state of the venture-debt facility covenants — typically minimum-cash, minimum-ARR or revenue, maximum-burn, customer-concentration, and material-adverse-change clauses (per the standard Bessemer / Battery Ventures venture-debt primers). A covenant trip can freeze the draw line, accelerate repayment, or both. Common pitfall: covenants are not always actively monitored between board meetings — drift between an internal forecast and a covenant threshold can cross the line silently. Boards should require monthly covenant headroom reporting when material debt is drawn.
Formula
Stoplight categorical: in-compliance (with headroom) / at-risk (headroom ≤ 1 quarter) / tripped / waived. List the binding covenant and current headroom.
Why it matters
A covenant trip can cascade into a liquidity crisis fast — frozen facility, accelerated repayment, MAC clause triggering. Board catches this only if it is on the dashboard explicitly.
Interpretation guidance
Headroom of less than one quarter on the binding covenant is "at-risk" — board action required. Headroom of less than one month is a crisis-management situation regardless of stoplight color. Always pair with the binding-covenant name (e.g. "minimum cash $5M, current $7.2M, headroom = $2.2M").
Source
imboard Editorial
Related KPIs
fundraising.venture_debt_drawn fundraising.venture_debt_available finance.total_cash_in_bank finance.net_burn_rate

Venture Debt Drawn

fundraising.venture_debt_drawn
currency Editorial
Description
Principal currently drawn from venture debt facilities (e.g. Silicon Valley Bank, Hercules Capital, Trinity Capital, Western Alliance, Bridge Bank facilities). Venture debt typically extends runway 6–12 months alongside the equity round — used well, it dilution-efficiently bridges to the next equity event; used poorly, it concentrates default risk into a single covenant covenant trip. Common pitfall: drawn debt creates interest expense and a repayment schedule that compresses runway in 18–24 months even though it extends runway today (per the Battery Ventures venture-debt primer and the Bessemer "venture debt playbook" series).
Formula
Sum of principal drawn (and not yet repaid) across all active venture debt facilities. Distinct from `venture_debt_available` (undrawn capacity). Servicing cost = drawn × (rate + fees) — reduces runway.
Why it matters
Drawn debt accelerates cash burn through interest plus principal amortization (typically 24–36 month amortization after a 6–18 month interest-only period). Misjudging the trade-off between dilution avoided and forced repayment is a common venture-backed startup failure mode.
Interpretation guidance
Drawn debt above ~30% of unrestricted cash starts to dominate the runway forecast and the covenant exposure. Pair with `venture_debt_covenant_status` and the next-round timeline — if the next equity event is uncertain past the amortization start date, the board should be in active conversation about refinancing.
Source
imboard Editorial
Related KPIs
fundraising.venture_debt_available fundraising.venture_debt_covenant_status finance.total_cash_in_bank finance.runway_months

Sales 48 KPIs

ARR

sales.arr
currency Industry-backed
Description
Annual Recurring Revenue — the value of all recurring subscription revenue normalized to a one-year run-rate as of the period close. The headline operating metric for a subscription business; every growth and efficiency ratio (NRR, GRR, magic number, CAC payback, Rule of 40) is calibrated against it. Excludes one-time fees, professional services, and non-contractual usage. Common pitfall: confusing ARR (contracted recurring) with revenue (recognized) or with CARR (contracted incl. not-yet-live) — the SMSB standard draws sharp lines between them, and boards expect the same discipline. The KpiVarianceTable widget surfaces forecast / actual / variance / status / future-forecast columns against the same field.
Formula
ARR = Sum of annualized value of all active recurring subscription contracts at period close. Per SMSB: includes only the recurring portion of contracts that are live (delivered / in production). Excludes one-time fees, professional services, and usage that is not contractually committed. For multi-year contracts, ARR is the contract value divided by the term in years.
Why it matters
Headline operating number that every other SaaS metric calibrates against — growth rate, efficiency ratios (CAC ratio, magic number, Rule of 40), retention math (NRR, GRR), and valuation multiples all read off ARR. Boards use the period-over-period ARR delta as the first-pass health check for the business.
Interpretation guidance
Per KBCM/Sapphire SaaS Survey 2024 §Growth Rate, public-SaaS-comparable private companies in the $5–25M ARR band typically grow ARR 40–60% YoY, falling toward 20–30% by $100M+ ARR; well-below-band growth at any ARR scale is the first thing a board interrogates. Always read ARR alongside Net New ARR (sales.new_business + sales.expansion − sales.churn_arr − sales.downgrades) — flat ARR can mask churn offset by upsell.
Related KPIs
sales.carr sales.new_business sales.expansion sales.churn_arr sales.downgrades sales.growth_rate_yoy sales.starting_arr customers.net_revenue_retention operations.rule_of_40

Average Contract Value

sales.avg_contract_value
currency Industry-backed
Description
Average annualized contract value across new-customer deals signed during the period (ACV). Defines where the company plays on the SaaS deal-size spectrum and dictates the operating model — high-ACV businesses tolerate longer sales cycles and direct sales motions; low-ACV businesses must run product-led or inside-sales motions to keep CAC payback short. Common pitfall: blending new and expansion ACV obscures the new-logo deal-size trend that boards actually want to see. Anchored to KBCM/Sapphire SaaS Survey 2024 §Average Contract Value for cross-company benchmarking.
Formula
Average Contract Value = New Business ARR / New Customers Added (for the same period). For multi-year contracts, use the annualized ACV (TCV / contract term in years), not Total Contract Value (TCV). Restrict to new-logo deals to keep the trend interpretable; track Expansion ACV separately if material.
Why it matters
Sets the cost ceiling for the sales motion — at $5k ACV the company cannot afford a field sales team; at $250k ACV inside sales alone usually leaves money on the table. The board uses ACV trend to validate up-market or down-market strategy bets.
Interpretation guidance
Per KBCM/Sapphire SaaS Survey 2024 §Average Contract Value, segmentation bands: SMB ≤ $5k, Mid-Market $5k–$50k, Enterprise > $50k (often $100k+ for true enterprise). ACV doubling over four quarters is a clear up-market motion — make sure CAC and sales-cycle changes are reflected in plan. Flat ACV with rising volume = scaling the existing motion; rising ACV with flat volume = a deliberate up-market bet that needs explicit board buy-in.
Benchmark
p25 25000 $ · median 62000 $ · p75 100000 $
Related KPIs
sales.new_business sales.new_customers_added sales.median_deal_size sales.average_deal_size sales.avg_sales_cycle_days sales.cac

Average Deal Size

sales.average_deal_size
currency Editorial
Description
Mean dollar value across active pipeline opportunities (Pipeline Value / Pipeline Deal Count). Distinct from sales.avg_contract_value (ACV) which measures closed-won deals — average_deal_size is forward-looking pipeline-shape, ACV is realized output. Common pitfall: a few oversized deals materially skew the average — always inspect median_deal_size alongside; a large gap between average and median signals a few mega-deals that drive most of the projected number, which concentrates pipeline risk.
Formula
Average Deal Size = Pipeline Value / Pipeline Deal Count. Same value convention (TCV or ACV) as the upstream metrics. Always report alongside Median Deal Size for skew detection.
Why it matters
Forward-looking signal for ACV mix-shift before it appears in closed-won numbers — pipeline-size trending up usually shows up in closed-won-size 1–2 cycles later. Boards use the lead-time to ask "is this an intentional up-market move or a mix drift to correct?"
Interpretation guidance
Track the spread (average − median) over time: stable, narrow spread = healthy uniform pipeline; widening spread = increasing concentration risk in a few oversized deals (those deals slipping then becomes catastrophic to the period). Average rising faster than median = up-market mix-shift in pipeline.
Source
imboard Editorial
Related KPIs
sales.median_deal_size sales.pipeline_value sales.pipeline_deal_count sales.avg_contract_value sales.closed_won_value

Average Sales Cycle (Days)

sales.avg_sales_cycle_days
number (days) Editorial
Description
Average number of days from opportunity creation to closed-won status — measured only on won deals (lost deals are tracked separately). The motion-velocity metric — directly determines how much pipeline coverage is needed, how quickly investment in new reps pays back, and how feedback loops on packaging or pricing experiments compound. Common pitfall: blending segment cycles (SMB and Enterprise often differ 5–10×) into a single average hides material trend signals — segment-cut the metric where deal-volume permits.
Formula
Average Sales Cycle (Days) = Σ (close_date − created_date) across closed-won opportunities in period / Count of those opportunities. Restrict to won deals; for cycle-time analysis on lost deals, compute separately.
Why it matters
Determines required pipeline coverage (a 90-day cycle needs ~1 quarter of forward pipeline; a 270-day cycle needs ~3 quarters), and is the leading indicator of ICP fit — strong fit shortens cycles; mismatched fit lengthens them.
Interpretation guidance
Typical ranges by ACV band (industry folk-wisdom, not citation-grade): SMB < $5k ACV → 14–45 days; Mid-Market $5k–50k → 45–90 days; Enterprise $50k+ → 90–270 days; Strategic > $250k → 180–365+ days. Cycle lengthening trend over 2+ quarters at constant ACV mix is the canonical "deals stuck in evaluation" signal — usually buyer-side decision-process changes (procurement, security review) or competitive friction.
Benchmark
p25 40 days · median 84 days · p75 150 days
Source
imboard Editorial
Related KPIs
sales.avg_contract_value sales.pipeline_stage_metrics sales.pipeline_sales_cycle sales.win_rate sales.pipeline_value

Blended CAC Ratio

sales.blended_cac_ratio
number Industry-backed
Description
Total fully-loaded S&M spend in the period divided by the dollars of new CARR generated in the period (new-customer + expansion CARR combined). Per the SMSB standard, the headline efficiency ratio for the full sales-and-marketing motion — answers "how many cents do we spend on S&M to add one dollar of contracted ARR." Common pitfall: blending without separately reporting New CAC Ratio and Expansion CAC Ratio hides which side of the motion is driving efficiency — for a healthy SaaS company expansion CAC is usually 3–5× cheaper per dollar than new-logo CAC.
Formula
Blended CAC Ratio = Total S&M Spend (period) / (New CARR + Expansion CARR generated in period). Per SMSB §Blended CAC Ratio: spend uses the same fully-loaded definition as CAC; CARR-based denominator (not ARR) reflects committed contract value at the point of sign.
Why it matters
The portfolio-level efficiency number — one ratio that summarizes the full S&M engine. Boards use it to track quarter-over-quarter efficiency improvement as the motion matures.
Interpretation guidance
Per SMSB convention, a Blended CAC Ratio < 1.0 means the company is acquiring more contracted ARR than it spends on S&M — capital-efficient growth. 1.0–1.5 is acceptable while the motion is scaling; > 2.0 sustained signals either a motion or pricing problem. Always pair with the New and Expansion CAC Ratio split to localize the issue.
Related KPIs
sales.new_cac_ratio sales.expansion_cac_ratio sales.cac sales.cac_payback_period sales.new_business sales.expansion sales.carr

Bookings Backlog

sales.bookings_backlog
currency Editorial
Description
Total value of signed contracts that have not yet been recognized as revenue — future revenue locked into the books. Equivalent to "remaining performance obligation" (RPO) in public-SaaS disclosures, though private companies often track only the in-period portion. Board reads this as the visibility horizon: a healthy backlog means recognized revenue is largely already-sold and not dependent on Q-end heroics. Common pitfall: confusing backlog with pipeline — backlog is contractually committed, pipeline is unsigned opportunity. Surface the two on the same dashboard but never sum them.
Formula
Bookings Backlog = TCV of all signed customer contracts − Revenue already recognized against those contracts. For a SaaS business, the dominant component is unrecognized subscription value on multi-month / multi-year contracts. Most-comparable public-disclosure equivalent: ASC 606 Remaining Performance Obligation (RPO).
Why it matters
The single best read on next-period revenue predictability — high backlog means the revenue line for the coming quarter is largely contractual, not pipeline-dependent. Boards use it to gauge whether the team is selling for in-quarter close or building durable forward visibility.
Interpretation guidance
Backlog at year-end ≥ 1.0× the next year's ARR plan is the "visible year" threshold most boards expect at Series B+ subscription companies (industry folk-wisdom — no single published standard; the underlying conventions derive from ASC 606 RPO disclosure norms in public-SaaS filings). Backlog declining as a share of forward plan = visibility eroding; usually demands a sales-cycle or pipeline-coverage drill-down.
Source
imboard Editorial
Related KPIs
sales.bookings_backlog_total sales.total_revenue sales.arr sales.carr sales.pipeline_value sales.weighted_forecast

Bookings Backlog Changes

sales.bookings_backlog_changes
text Editorial
Description
Structured bridge that reconciles opening bookings backlog to closing backlog through the period's new bookings, conversions to revenue, post-contract losses, and value adjustments (starting + new − converted − lost + increases − decreases = ending). The bespoke sales card reads this typed object to show the backlog motion. Distinct from the editor's `sales.bookings_backlog_total` FlowSubform container — this is the typed `IBookingsBacklog` the feed card consumes. Common pitfall: an ending value that does not reconcile because conversions to recognized revenue were not netted out.
Formula
Container — { startingBackLogValue, newBookingValue, bookingsConvertedToRevenue, lostBookingsPostContract, backlogValueIncreases, backlogValueDecreases, endingBackLogValue, netChange, netChangePercent }. Identity: starting + new − converted − lost + increases − decreases = ending.
Why it matters
Makes signed-but-not-yet-recognized revenue auditable — a growing backlog is forward revenue visibility; a shrinking one (conversions outpacing new bookings) is an early top-of-funnel warning.
Interpretation guidance
When conversions consistently exceed new bookings, the backlog erodes and future recognized revenue will soften. Disproportionate post-contract losses signal delivery or scoping problems.
Source
imboard Editorial
Related KPIs
sales.bookings_backlog_total sales.bookings_backlog sales.arr

Bookings Backlog Total

sales.bookings_backlog_total
currency Editorial
Description
Total dollar value of all signed contracts that have not yet been recognized as revenue — the visibility window into future revenue at a point in time. Closely related to sales.bookings_backlog; this entry serves as the FlowSubform `start` slot for the per-period bookings-backlog flow (open + new bookings − recognized − cancellations = close). Common pitfall: omitting cancellations from the flow leaves a phantom backlog that overstates future revenue visibility — every backlog flow needs an explicit cancellation line even when zero.
Formula
Bookings Backlog Total at period close = Σ TCV of all signed contracts − Σ revenue recognized to date against those contracts. Equivalent to ASC 606 RPO. The per-period flow: opening backlog + new bookings (TCV signed in period) − revenue recognized in period − cancellations = closing backlog.
Why it matters
Quantifies how much of forward revenue is already contracted — high ratios of backlog to forward plan = high revenue predictability. Boards use it to assess whether the business has visibility or is running quarter-to-quarter on pipeline conversion.
Interpretation guidance
Backlog at year-end ≥ 1.0× next-year ARR plan is the conventional "visible year" benchmark for Series B+ subscription companies (industry folk-wisdom — anchored to public-SaaS RPO disclosure norms but not a published cross-company threshold). Track the cancellation share of the flow — rising cancellations as a % of opening backlog signal contract instability.
Source
imboard Editorial
Related KPIs
sales.bookings_backlog sales.total_revenue sales.arr sales.carr sales.new_business

CAC Payback Period

sales.cac_payback_period
number (months) Industry-backed
Description
Number of months required for the gross profit generated from a new customer's ARR to recover the fully-loaded S&M spend used to acquire them. The single most decision-useful efficiency metric at the board level — it directly connects acquisition cost, ACV, and gross margin into one "how long until we break even on this customer" answer. Per the SMSB standard, the calculation must use gross-margin-adjusted ARR in the denominator (not raw ARR) to be cross-company comparable. Common pitfall: using raw ARR understates payback by ~25–30 percentage points and breaks comparability with peer benchmarks.
Formula
CAC Payback (months) = Total fully-loaded S&M spend (period) / (Monthly New ARR × Gross Margin %). Both numerator and denominator are period aggregates — the numerator is total S&M spend for the period, NOT per-customer CAC (pairing per-customer cost with aggregate new ARR is a dimensional error that yields months-per-customer). The denominator is gross-margin-adjusted total monthly new ARR. Per SMSB §CAC Payback Period: the gross-margin adjustment makes the metric comparable across companies with different cost structures.
Why it matters
The decision-relevant single number for "is the acquisition motion working" — sub-24 months signals capital-efficient growth; > 36 months means each dollar of S&M is locking up cash for too long to justify scaling spend.
Interpretation guidance
Per the SaaS-investor convention reflected in KBCM/Sapphire SaaS Survey 2024 benchmarking: < 24 months gross-margin-adjusted payback is healthy; 24–36 months is acceptable for early-stage / up-market motions; > 36 months requires either an explicit path to compress (motion change) or a strategic rationale (e.g. multi-year deferred-revenue contracts with strong retention).
Related KPIs
sales.cac sales.new_cac_ratio sales.blended_cac_ratio sales.gross_margin sales.new_business sales.arr

CARR

sales.carr
currency Industry-backed
Description
Contracted Annual Recurring Revenue — recognized MRR × 12 plus the annualized value of contracts that are signed but not yet live (i.e. implementation, ramp, deferred-start). Per the SMSB standard, CARR sits between ARR (live only) and pipeline (unsigned) on the revenue-certainty spectrum: contractually committed but not yet delivered. Boards reading CARR > ARR gap can quantify the in-flight implementation backlog and the leading indicator of next-period ARR. Common pitfall: counting verbal commitments or LOIs as CARR — only signed contracts qualify under the SMSB definition.
Formula
CARR = ARR (live, recognized contracts annualized) + Annualized value of signed contracts not yet in production. Per SMSB §CARR: requires a signed contract; excludes verbal commitments, letters of intent, and pipeline. The (CARR − ARR) gap = in-flight ARR awaiting go-live.
Why it matters
A leading indicator that ARR alone misses — if CARR is growing faster than ARR, an implementation backlog is building and ARR will accelerate as those contracts go live. Boards use the CARR-to-ARR ratio to interrogate the implementation engine.
Interpretation guidance
A CARR / ARR ratio of 1.00 means everything signed is already live (no implementation backlog); 1.10–1.20 is typical for enterprise SaaS with multi-month implementation timelines; > 1.30 may signal either an implementation bottleneck (operational risk) or a deliberate backlog-build before a known activation event (intentional). Always cross-reference with the implementation team's capacity plan.
Related KPIs
sales.arr sales.new_business sales.blended_cac_ratio sales.new_cac_ratio sales.bookings_backlog_total

Churned ARR

sales.churn_arr
currency Editorial
Description
Annualized recurring revenue lost during the period from customers who fully cancelled — terminating their contract or letting it lapse without renewal. The "leak" line of the ARR waterfall and the denominator of Gross Revenue Retention. Distinct from Downgrade ARR (sales.downgrades) which captures contractions where the customer stays. Common pitfall: lumping mid-term cancellations with non-renewals masks two very different retention failures — surface them separately when material. The KpiVarianceTable widget tracks period forecast vs actual; a widening miss against forecast is the earliest signal of a retention problem.
Formula
Churned ARR = Sum of ARR from contracts that terminated during the period (cancelled mid-term or not renewed at the end of term) attributable to customers whose ARR with the company drops to zero. Excludes contractions where the customer remains (those land in Downgrade ARR).
Why it matters
Direct read on Gross Revenue Retention (GRR) — the floor of the retention math, since downgrades and churn cannot be offset by upsell in GRR. A board can tolerate slow new-logo growth if churn is low, but cannot tolerate high churn at any growth rate — it compounds against valuation.
Interpretation guidance
Per KBCM/Sapphire SaaS Survey 2024 §Gross Revenue Retention, top-quartile SaaS companies hold GRR ≥ 90% (enterprise-segment) or ≥ 85% (SMB-segment); GRR below 80% in either segment usually means the product or onboarding has a structural problem, not a sales-execution one. Pair this line with the Customers domain to identify whether churn concentrates in a particular segment or cohort.
Source
imboard Editorial
Related KPIs
sales.arr sales.downgrades sales.expansion customers.gross_revenue_retention customers.net_revenue_retention customers.logo_retention_rate customers.logo_churn_rate

Competitive Alerts

sales.competitive_alerts
text Editorial
Description
Narrative read on competitive dynamics affecting the sales motion — material wins / losses to specific competitors, observed pricing or packaging moves in the market, new entrants, M&A in the competitive set. Boards use this surface to bring outside intelligence (their other portfolio companies, advisors) to bear on the competitive picture. Common pitfall: listing competitor names without quantifying how often they show up in deal cycles — a "Competitor X is being aggressive" entry without "we saw them in 8 of 20 active deals last quarter, up from 3 of 18" is too vague to act on.
Formula
Free-text narrative — no calculation. Convention: per-competitor sub-sections covering deal-frequency observed, win/loss split when statistically meaningful, and any observed pricing / packaging moves.
Why it matters
Competitive intelligence is the most under-shared information in board packs and the most useful for cross-portfolio learning — boards can validate or refute observations from other companies they sit on.
Interpretation guidance
Track entries quarter-over-quarter: a competitor whose mention frequency rises consistently is a leading indicator of market positioning erosion. Pair with sales.win_rate trend cut by competitor when possible.
Source
imboard Editorial
Related KPIs
sales.win_rate sales.closed_lost_count sales.closed_lost_value sales.key_concerns sales.strategic_context

Customer Acquisition Cost

sales.cac
currency Industry-backed
Description
Fully-loaded sales-and-marketing (S&M) expense incurred to acquire one new customer during the period. Per the SMSB standard, the CAC numerator includes salaries + commissions + benefits + travel + marketing programs + tooling — i.e. all S&M costs, not just direct-attribution paid acquisition. The denominator is new logos, not deals. Common pitfall: omitting fully-loaded comp (especially BDR/SDR base salary and CS-team cost-of-sale where they participate in expansion) understates CAC and inflates every downstream efficiency metric. The board cares about CAC alongside CAC Payback and the CAC Ratio family — single-number CAC is a building block, not a verdict.
Formula
CAC = Total fully-loaded S&M expense for the period / New Customers Added in the period. Per SMSB §CAC: numerator includes all S&M spend (compensation, benefits, programs, tooling, allocated overhead); denominator counts net-new logos only (not expansion deals).
Why it matters
The cost side of the customer-unit economics ledger — paired with ACV and gross margin, determines whether each customer is a profitable transaction over a reasonable horizon. Boards read CAC alongside payback period before debating S&M investment levels.
Interpretation guidance
Absolute CAC values vary by ACV band — what matters is the ratio CAC / first-year-ARR (= New CAC Ratio) and CAC Payback. Per public SaaS comps, healthy CAC payback is < 24 months gross-margin-adjusted; > 36 months usually means the acquisition motion is either too expensive or the contract terms too short.
Related KPIs
sales.cac_payback_period sales.new_cac_ratio sales.blended_cac_ratio sales.expansion_cac_ratio sales.new_business sales.new_customers_added

Deals Lost

sales.closed_lost_count
number Editorial
Description
Count of opportunities that transitioned to closed-lost during the period — the volume side of pipeline disqualification. The other half of the win rate denominator; without tracking it explicitly you cannot compute or benchmark win rate. Common pitfall: stale "open" deals that should be marked lost are left open, inflating pipeline value while suppressing the lost count — a hygiene problem that compounds because next-period coverage looks fine while win rates silently degrade. Every CRM hygiene policy should specify a max-age before deals auto-flag for lost-or-update review.
Formula
Closed-Lost Count = Count of distinct opportunities whose stage transitioned to closed-lost during the period. Excludes "no decision" / paused opportunities (which should have a separate stage); excludes disqualified-pre-pipeline opportunities.
Why it matters
Denominator input for win rate and direct read on pipeline hygiene. Cluster analysis of loss reasons feeds product / pricing / positioning decisions that boards expect to see referenced in strategic_context.
Interpretation guidance
Track loss-reason distribution quarter-over-quarter — concentration in "price" usually points to packaging; concentration in "feature gap" feeds product roadmap; concentration in "no decision" usually means lead-qualification is too loose. Pair count with value to spot mix-shift (e.g. losing more small deals while winning the large ones is a different motion problem than the inverse).
Source
imboard Editorial
Related KPIs
sales.closed_lost_value sales.closed_won_count sales.win_rate sales.competitive_alerts sales.pipeline_risk_factors

Deals Lost Value

sales.closed_lost_value
currency Editorial
Description
Total dollar value of opportunities closed-lost during the period — the opportunity-cost view on the pipeline motion. Useful for sizing the "what we missed" gap and prioritizing post-mortem efforts on the highest-value losses. Common pitfall: post-mortems on small lost deals waste time relative to insight; tier the post-mortem cadence by value (e.g. every loss above the 80th-percentile deal size gets a written debrief). Boards expect the largest 2–3 losses to be explained explicitly in commentary.
Formula
Closed-Lost Value = Σ (deal_value) across opportunities that transitioned to closed-lost in the period. Use the same value convention (TCV vs ACV) as Closed-Won Value for consistency.
Why it matters
Quantifies the realized opportunity cost — useful for justifying packaging changes, ICP refinement, or product investment that would have closed specific tier-1 losses. Drives loss-reason prioritization.
Interpretation guidance
Loss-Value / Won-Value (= loss share of total close events) — at steady state usually 30–60% depending on motion type (inbound-heavy motions have higher win rates and lower loss values; outbound motions have lower win rates and higher loss values). Spiking loss-value with stable won-value usually indicates competitive friction or pricing pressure on enterprise deals specifically.
Source
imboard Editorial
Related KPIs
sales.closed_lost_count sales.closed_won_value sales.win_rate sales.average_deal_size sales.competitive_alerts

Deals Summary (Won / Lost)

sales.deals_summary
text Editorial
Description
Container handle for the period's notable deals split into WON and LOST arrays — each deal carries name, account, amount, owner, deal type, source, and competitor, plus a win reason + close date (won) or a loss reason (lost). The bespoke sales feed card renders this as the "Notable Deals" won/lost breakdown the demo design shows. This is RICHER than the flat `sales.pipeline_key_deals` editor gallery (which has no won/lost split, reason, or close date). Common pitfall: carrying the same list forward each quarter — refresh to the actual period's closes.
Formula
Container — { wonDeals: IWonDeal[], lostDeals: ILostDeal[] }. Won deals carry winReason + closeDate; lost deals carry lossReason. No aggregate calculation; the surface makes specific outcomes visible at the board level.
Why it matters
Turns the quarter's win/loss outcomes into board-readable narrative — why deals were won (superior product / service) and lost (features / price / competitor) is the qualitative signal raw pipeline numbers miss.
Interpretation guidance
Cluster the loss reasons: repeated `features` losses signal a product gap; repeated `price` losses signal a packaging/positioning gap. Pair with `sales.pipeline_key_deals` for the still-open top deals.
Source
imboard Editorial
Related KPIs
sales.pipeline_key_deals sales.closed_won_value sales.closed_lost_value

Deals Won

sales.closed_won_count
number Editorial
Description
Count of opportunities that reached closed-won status during the period — the volume side of the period's sales output. Paired with closed_won_value gives the period's average won-deal size, a critical mix-shift indicator. Common pitfall: counting opportunity stage transitions rather than discrete deal closes (re-opened deals inflate the count). Boards read the trend over 4+ quarters to detect motion-volume stability — sharp drops while pipeline holds usually mean late-stage conversion has broken.
Formula
Closed-Won Count = Count of distinct opportunities whose stage transitioned to closed-won during the period. Each opportunity counted at most once even if multiple stage transitions occur.
Why it matters
The most direct sales-execution volume signal — separates "we sold lots of small things" from "we sold a few big things" when paired with deal-size lines. Inputs win rate and ASP analysis.
Interpretation guidance
Read alongside closed_won_value: rising count + rising value = healthy scale; rising count + falling value = down-market mix shift (often unintentional); falling count + rising value = up-market success or a coverage problem; both falling = execution issue requiring intervention.
Source
imboard Editorial
Related KPIs
sales.closed_won_value sales.closed_lost_count sales.win_rate sales.average_deal_size sales.new_customers_added

Deals Won Value

sales.closed_won_value
currency Editorial
Description
Total dollar value of all opportunities closed-won during the period — the period's realized bookings from the pipeline motion. Reconciles to (sales.new_business + sales.expansion) when split by deal type. Common pitfall: reporting TCV (total contract value) here when the rest of the dashboard uses ACV — pick one and apply it consistently across closed_won_value, weighted_forecast, and pipeline_value, or the dashboard math stops reconciling.
Formula
Closed-Won Value = Σ (deal_value) across opportunities that transitioned to closed-won in the period. The "deal_value" convention (TCV vs ACV) must match the pipeline-tracking convention for the math to reconcile.
Why it matters
Realized bookings — the period's actual sales output. Sum across periods should reconcile to total new-customer + expansion CARR additions; gaps indicate either revenue-recognition policy or stage-data-quality issues.
Interpretation guidance
Closed-Won Value / Quota gives the period's attainment percentage — > 100% is over-plan, 80–100% is the typical "acceptable" band, < 80% triggers the post-mortem cycle. Read alongside Win Rate to identify whether misses are pipeline-driven (low value but normal win rate) or execution-driven (normal value, depressed win rate).
Source
imboard Editorial
Related KPIs
sales.closed_won_count sales.weighted_forecast sales.quarterly_forecast sales.win_rate sales.new_business

Downgrade ARR

sales.downgrades
currency Editorial
Description
Annualized recurring revenue lost from existing customers who reduced spend mid-term or at renewal (seat reductions, tier downgrades, removed modules) — without leaving entirely. The "contraction" line of the ARR waterfall, distinct from full churn. Often a more sensitive leading indicator than churn because customers tend to contract before they cancel. Common pitfall: lumping downgrades into churn obscures the early-warning signal — boards looking only at logo churn miss the slow-bleed pattern. Surfaces in the KpiVarianceTable widget alongside expansion and churn so the net-retention math is auditable.
Formula
Downgrade ARR = Sum across existing customers of (ARR at period start − ARR at period close) for the subset where the delta is positive AND the customer's ARR did not drop to zero. Customers whose ARR drops to zero count in Churned ARR instead.
Why it matters
Earliest leading indicator of retention risk — customers usually contract before they cancel, so a rising downgrade line predicts churn 1–2 quarters out. Inputs NRR (subtracts from expansion) and CCO/CS comp models that gate on Net Retention.
Interpretation guidance
Downgrade ARR rising as a share of expansion ARR over 2+ quarters is the canonical leading signal of a renewal-cycle problem. There is no widely-published cross-company benchmark for downgrade rates as a standalone — read it in context of NRR (industry folk-wisdom: a healthy SaaS company with NRR ≥ 110% typically has downgrade ARR ≤ 3% of starting ARR per period).
Source
imboard Editorial
Related KPIs
sales.expansion sales.churn_arr sales.arr customers.net_revenue_retention customers.gross_revenue_retention

Expansion ARR

sales.expansion
currency Editorial
Description
Annualized recurring revenue added during the period from existing customers — through upsell (more seats / higher tier), cross-sell (additional products), or price increases. The "farm" line of the ARR waterfall. Boards read this as the leading indicator that product-market fit has translated into product-account fit and that the post-sale motion is creating compound growth. Common pitfall: classifying contractual price-step-ups (CPI escalators baked into the original contract) as expansion overstates new selling motion. Expansion CAC Ratio and Net Revenue Retention are derived from this number.
Formula
Expansion ARR = (ARR from existing customers at period close) − (ARR from those same customers at period start) for the subset where the delta is positive. Excludes downgrades (tracked separately) and excludes new-logo bookings. Pre-contracted CPI escalators may or may not be treated as expansion — pick one convention per the company and apply it consistently.
Why it matters
A high expansion line is the single best predictor of capital-efficient compounding growth — the SaaS playbook depends on existing customers expanding faster than new ones churn. Drives NRR, which is the metric public-market investors weight most heavily on the retention side of the model.
Interpretation guidance
Expansion ARR ≥ Churned + Downgrade ARR means NRR ≥ 100% (the "leaky bucket gets refilled by upsell" condition). Per KBCM/Sapphire SaaS Survey 2024 §Net Revenue Retention, median NRR is roughly 105–110% for $5M+ ARR SaaS — below 100% is a yellow flag at any stage; above 120% signals a category-leading account-expansion motion.
Source
imboard Editorial
Related KPIs
sales.arr sales.new_business sales.churn_arr sales.downgrades sales.expansion_cac_ratio customers.net_revenue_retention customers.gross_revenue_retention

Expansion CAC Ratio

sales.expansion_cac_ratio
number Industry-backed
Description
Fully-loaded S&M plus Customer Success expense attributable to expansion divided by expansion CARR generated in the period. Per SMSB, the efficiency read on the upsell / cross-sell / land-and-expand motion. Distinct from the new-logo CAC ratio because the cost base often includes CSMs whose primary metric is retention but whose secondary metric is expansion — boards expect to see that allocation called out. Common pitfall: excluding CS comp entirely understates the true cost of expansion; including all of CS overstates it. The SMSB standard prescribes a documented allocation rule (typically tied to expansion-quota OTE share).
Formula
Expansion CAC Ratio = (S&M + CS spend allocated to expansion in period) / (Expansion CARR generated in period). Per SMSB §Expansion CAC Ratio: allocation rule for cross-functional comp (typically split by quota share of OTE) must be documented and consistent.
Why it matters
Validates the financial logic of "expansion is cheaper than acquisition" — when this is healthy, the company should bias growth investment toward post-sale; when it inverts (Expansion CAC ≥ New CAC), the expansion motion is broken and acquisition is the only available lever.
Interpretation guidance
Per SMSB convention, healthy Expansion CAC Ratio is typically 3–5× cheaper than New CAC Ratio — i.e. 0.2–0.5 when New CAC Ratio is ~1.5. Expansion CAC Ratio > 1.0 is a yellow flag (expansion costs as much as it earns); inversion vs New CAC Ratio is a red flag warranting a CS / sales-team org review.
Related KPIs
sales.blended_cac_ratio sales.new_cac_ratio sales.expansion customers.net_revenue_retention sales.carr

Gross Margin

sales.gross_margin
percentage (%) Industry-backed
Description
Recognized revenue minus cost of goods sold (COGS), divided by recognized revenue, expressed as a percentage. The single best read on whether the business model can ever generate operating leverage — a low gross margin caps every downstream efficiency metric (CAC payback, LTV/CAC, Rule of 40). For SaaS, COGS includes hosting, third-party software, customer support, and customer-success cost-of-service. Common pitfall: omitting customer success from COGS inflates the margin and breaks comparability with peer benchmarks. Anchored to KBCM/Sapphire SaaS Survey 2024 §Gross Margin.
Formula
Gross Margin = ((Recognized Revenue − COGS) / Recognized Revenue) × 100. COGS for a SaaS business: cloud / hosting infrastructure, third-party data and APIs called for delivery, customer support, customer success cost-of-service, and any directly-attributable delivery personnel. Excludes R&D, S&M, and G&A.
Why it matters
Caps every long-term efficiency metric — Rule of 40, LTV/CAC, CAC payback all run off contribution margin which derives from gross margin. Board uses it to verify the unit economics are real before debating S&M investment levels.
Interpretation guidance
Per KBCM/Sapphire SaaS Survey 2024 §Gross Margin, healthy SaaS gross margin is 70–80%; > 80% is best-in-class infrastructure leverage; < 65% usually signals heavy services revenue or inefficient COGS (often customer-success scaling linearly with customer count). Sub-70% companies must show a credible path to 70%+ by next funding milestone or face valuation pressure.
Benchmark
p25 65 % · median 72 % · p75 81 %
Related KPIs
sales.total_revenue sales.arr sales.cac_payback_period operations.rule_of_40 sales.growth_rate_yoy

Growth Rate (YoY)

sales.growth_rate_yoy
percentage (%) Industry-backed
Description
Year-over-year percentage growth in ARR (or recognized revenue, if explicitly anchored) — comparing the current period to the equivalent period 12 months prior. The single most-watched investor metric and the largest single driver of SaaS valuation multiples. Common pitfall: comparing to the prior quarter (QoQ) and reporting it as "growth rate" — boards and investors mean YoY unless explicitly noted otherwise. Anchored to KBCM/Sapphire SaaS Survey 2024 §YoY ARR Growth for cross-company benchmarking.
Formula
YoY Growth Rate = ((ARR at period close − ARR 12 months prior) / ARR 12 months prior) × 100. State the underlying metric explicitly (ARR vs Recognized Revenue) — they diverge meaningfully for sub-scale businesses. For quarters, use end-of-quarter ARR vs end-of-same-quarter-prior-year.
Why it matters
Direct input to public-comparable valuation multiples (EV / NTM ARR multiples are sliced by growth band). Boards use it to triangulate stage-appropriate pace and to flag deceleration early.
Interpretation guidance
Per KBCM/Sapphire SaaS Survey 2024 §YoY ARR Growth, median private-SaaS growth bands by ARR scale: $5–10M ARR median ~55–70%, $10–25M ARR ~40–55%, $25–50M ARR ~35–45%, $50M+ ARR ~25–35%. Growth decelerating > 30 percentage points YoY at any ARR scale is the most actionable board warning signal — usually requires either pipeline-coverage diagnosis or product-investment reallocation.
Benchmark
p25 12 % · median 19 % · p75 27 %
Related KPIs
sales.arr sales.new_business sales.expansion sales.churn_arr operations.rule_of_40 sales.gross_margin

Median Deal Size

sales.median_deal_size
currency Editorial
Description
Median dollar value across active pipeline opportunities — the typical deal in the pipeline, robust against the few-big-deals skew that distorts the average. The honest read on the "core motion" deal-size; if the team is winning a few oversized deals but the median is shrinking, the underlying motion is degrading even though the topline numbers look fine. Common pitfall: omitting median in dashboards in favor of just the average lets concentration risk hide. A best-practice board pack always shows both.
Formula
Median Deal Size = 50th-percentile deal_value across active pipeline opportunities. Same value convention (TCV vs ACV) as upstream metrics; same active-pipeline stage filter as average_deal_size.
Why it matters
The most honest read on the typical motion — distinguishes "we have a real scalable motion" (high median) from "we have a few oversized deals carrying everything else" (low median, high average).
Interpretation guidance
When median deal size is stable while average deal size rises, the pipeline is becoming more skewed (a few mega-deals) — concentration risk. When median rises with average, the entire motion is shifting up-market. When median shrinks while average stays flat, deal-size compression is happening in the core motion (usually competitive pricing pressure).
Source
imboard Editorial
Related KPIs
sales.average_deal_size sales.pipeline_value sales.pipeline_deal_count sales.avg_contract_value

New Business ARR

sales.new_business
currency Editorial
Description
Annualized recurring revenue booked from net-new logos (first-time customers) during the period. This is the "hunt" line of the ARR waterfall — the output of the new-customer acquisition motion, distinct from expansion (existing-customer upsell) and from churn / downgrades. Common pitfall: counting renewals or expansion deals as new business inflates the new-logo conversion engine and hides a stalled acquisition motion. The KpiVarianceTable widget shows period forecast vs actual; downstream views compare it to S&M spend to derive new-business CAC and CAC payback.
Formula
New Business ARR = Sum of ARR contracts signed during the period by customers who had zero prior ARR with the company. Excludes expansion, renewals, and reactivations. Aligns with the SMSB definition of new-logo ARR and pairs 1:1 with sales.new_customers_added for ASP analysis.
Why it matters
Direct read on the health of the new-customer acquisition engine — separates "are we winning new logos" from "are existing customers expanding." Inputs the New CAC Ratio and CAC Payback calculations the board uses to judge sales efficiency.
Interpretation guidance
New Business ARR running below plan for two consecutive quarters is the classic early-stage growth-stall signal — usually upstream pipeline coverage or win-rate problems. New Business as a share of total Net New ARR should be 60–80% pre-Series B and trends down to 40–60% post-Series B as expansion picks up (industry folk-wisdom, not citation-grade — verify against KBCM/Sapphire 2024 segmentation tables for the company stage band).
Source
imboard Editorial
Related KPIs
sales.arr sales.new_customers_added sales.avg_contract_value sales.expansion sales.cac sales.new_cac_ratio sales.cac_payback_period

New CAC Ratio

sales.new_cac_ratio
number Industry-backed
Description
S&M expense attributable to new-customer acquisition divided by the new-customer CARR generated in the period. Per SMSB, the cleanest read on the new-logo acquisition engine's efficiency — strips out the expansion motion which has materially different unit economics. Common pitfall: failing to split AE comp time correctly between new and expansion activities — when the same AE owns both motions, an allocation rule (often the % of OTE tied to new-vs-expansion quota) is required and must be applied consistently quarter-over-quarter.
Formula
New CAC Ratio = (S&M spend allocated to new-customer acquisition in period) / (New-customer CARR generated in period). Per SMSB §New CAC Ratio: spend allocation must follow a documented rule (e.g. fraction of S&M headcount tied to new-business quota) applied consistently.
Why it matters
Isolates the new-logo engine — when blended CAC Ratio is moving, this is the first line of split-out diagnosis. Boards use it to evaluate whether to invest more in acquisition or shift weight toward expansion.
Interpretation guidance
Per SMSB convention, New CAC Ratio of 1.0–2.0 is the typical mid-stage SaaS band; > 2.5 sustained signals the new-logo motion is structurally expensive (often a fit problem with target segment). Should be ≥ Blended CAC Ratio (new-logo is always more expensive than expansion); if New CAC Ratio < Blended, the spend allocation between new and expansion is mis-tagged.
Related KPIs
sales.blended_cac_ratio sales.expansion_cac_ratio sales.cac sales.cac_payback_period sales.new_business sales.carr

New Customers Added

sales.new_customers_added
number Editorial
Description
Count of net-new logo customers signed during the period (a customer is a discrete buying entity — typically an account, not a seat). Paired with sales.new_business gives Average Selling Price (ASP) — a primary input to ICP / segment-fit conversations. Early-stage boards read the logo count as a sanity check on top-of-funnel and PMF before ARR-density grows enough to matter. Common pitfall: counting expansion deals or new contracts from existing customers as "new" inflates the acquisition signal — the count must match the same "first-time customer" criterion as New Business ARR.
Formula
New Customers Added = Count of distinct customer entities whose first-ever active contract started during the period. Must apply the same logo-counting unit (account / parent-org / billing entity) consistently across periods so the trend is comparable.
Why it matters
Logo count is the most direct read on acquisition-motion volume before contract-value mix dominates the ARR view. Early-stage boards read it before ARR; growth-stage boards pair it with ASP to spot segment drift (e.g. up-market mix-shift where logo count falls while ARR rises).
Interpretation guidance
Read alongside New Business ARR to derive ASP (= New Business ARR / New Customers Added). A rising ASP with falling logo count signals up-market drift (often intentional). Stable ASP with falling logo count signals a top-of-funnel problem. Falling ASP with stable logo count usually means discounting pressure — investigate competitive dynamics.
Source
imboard Editorial
Related KPIs
sales.new_business sales.avg_contract_value sales.cac sales.pipeline_deal_count sales.closed_won_count sales.win_rate

New Opportunities Added

sales.new_opps_added_value
currency Editorial
Description
Total dollar value of new opportunities entering the pipeline during the period — the top-of-funnel inflow line in the pipeline flow. The single best read on the marketing-and-SDR engine's output. Common pitfall: counting inflated, un-qualified opportunities (e.g. every demo request) overstates the engine's output; restrict to opportunities that pass a defined qualification stage (typically SQL or higher) before counting. Boards expect this number to track forward quota — a quarter's top-of-funnel should be ~1× the same quarter's quota for a normal sales-cycle business.
Formula
New Opportunities Added (Value) = Σ deal_value across opportunities that entered the pipeline during the period (using the same qualification-stage floor and value convention as upstream metrics). Excludes deals re-opened from closed-lost (those should be tracked separately to avoid double-counting top-of-funnel).
Why it matters
Marketing/SDR output measured in dollars — directly determines whether future periods will have sufficient pipeline coverage. Trending down with stable conversion = future-period miss baked in 1–2 cycles out.
Interpretation guidance
New opportunities added should run ~1× of the comparable-period quota at steady state (i.e. the period's top-of-funnel feeds roughly the same period's closes via the sales cycle). Sustained sub-quota top-of-funnel for 2+ quarters is the canonical signal to invest in marketing or SDR capacity.
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.opening_pipeline_value sales.pipeline_deal_count sales.pipeline_flow sales.win_rate

Opening Pipeline Value

sales.opening_pipeline_value
currency Editorial
Description
Total pipeline value at the start of the period — the baseline against which the period's pipeline flow (+ new opportunities − won − lost = closing) reconciles. Equal to the prior period's closing pipeline by construction. Surfaces in sales.pipeline_flow as the `start` slot. Common pitfall: restating opening pipeline to retroactively "clean up" stale deals masks the hygiene problem rather than addressing it; cleanup should happen via explicit "old-deal scrub" lines in the flow, not by editing the opening baseline.
Formula
Opening Pipeline Value = Total pipeline value snapshot at period open = prior period's closing pipeline. Identity: opening + new_opps_added − closed_won_value − closed_lost_value − scrubs = closing pipeline.
Why it matters
Without an explicit opening line, the pipeline flow has no anchor and the additions / removals cannot be audited. Boards expect the flow to reconcile to the penny period-over-period.
Interpretation guidance
Compare opening pipeline to the period's quota — opening pipeline coverage (opening / quota) is a stronger leading indicator than current pipeline (which has had time for in-period additions). Coverage < 1.5× quota at period open is usually a warning that the period requires above-average in-period generation to land.
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.new_opps_added_value sales.closed_won_value sales.closed_lost_value sales.pipeline_flow

Pipeline Assumptions

sales.pipeline_assumptions
text Editorial
Description
Narrative documenting the key assumptions underlying the pipeline forecast — conversion rates by stage, expected sales-cycle length, segment-mix expectations, and any deal-specific dependencies (e.g. "we assume Acme renews their POC by end of month and signs the upgrade in Q3"). Common pitfall: leaving assumptions implicit makes the forecast non-falsifiable — if you don't list the assumptions, you can't identify which one broke when the forecast misses. Renders side-by-side with sales.pipeline_risk_factors in the TwoColumnTextarea widget (sales.pipeline_context_notes container).
Formula
Free-text narrative — no calculation. Convention: 3–6 bullet assumptions, each one stating the assumed value/rate and the implication if it diverges (e.g. "Assumed Q3 win-rate of 28%; each 5pp miss = $X off forecast").
Why it matters
Makes the forecast falsifiable and post-mortem-able — without an assumptions list, missed quarters get attributed to vague "execution" rather than specific assumption failures the next plan should correct.
Interpretation guidance
After-the-fact review: which assumptions held and which broke? An assumption that consistently breaks (e.g. "Q4 always slips") is a planning-process problem, not an execution problem. Strong commentary names 1–2 assumptions explicitly and provides the sensitivity ("if conversion holds at 32%, forecast holds; below 28% we are $X short").
Source
imboard Editorial
Related KPIs
sales.pipeline_risk_factors sales.pipeline_context_notes sales.weighted_forecast sales.quarterly_forecast sales.win_rate

Pipeline Context Notes

sales.pipeline_context_notes
text Editorial
Description
Container handle for the side-by-side contextual notes — pairs sales.pipeline_assumptions (left slot) with sales.pipeline_risk_factors (right slot) in the TwoColumnTextarea widget. Visually positions the "what we're assuming" narrative directly next to the "what could break those assumptions" narrative, forcing the team to write them in concert (rather than as two independent surfaces that drift apart over quarters). Common pitfall: writing assumptions without their corresponding risks (or vice versa) means the forecast is incomplete — every assumption should pair to a risk factor that captures the failure mode.
Formula
Container — two-slot composite. Left slot = sales.pipeline_assumptions, right slot = sales.pipeline_risk_factors. No additional content; the value of the container is purely the side-by-side rendering, which structurally encourages assumptions and risks to be written together.
Why it matters
Forces a discipline that significantly improves forecast quality — assumption / risk pairs are more useful than either alone because each risk has a sensitivity (how much the forecast moves if the corresponding assumption breaks).
Interpretation guidance
Well-constructed pairs read like "Assume win rate of 28% in Q3 → Risk: Win rate has dropped below 25% in months with competitive entry." A board reading the surface should be able to identify every risk's sensitivity by cross-referencing to the assumption.
Source
imboard Editorial
Related KPIs
sales.pipeline_assumptions sales.pipeline_risk_factors sales.weighted_forecast sales.quarterly_forecast sales.key_concerns

Pipeline Deal Count

sales.pipeline_deal_count
number Editorial
Description
Total number of active opportunities in the pipeline (open stages only — excludes closed-won and closed-lost). The volume side of pipeline coverage; paired with pipeline_value gives the average deal size and the deal-count vs deal-size ratio that characterizes the motion shape. Common pitfall: counting non-bona-fide opportunities (orphaned trials, demo requests that never converted to a real evaluation) inflates the number — apply a stage-floor cutoff (e.g. SQL or higher) so the count reflects committed evaluation activity.
Formula
Pipeline Deal Count = Count of opportunities currently in any open stage (qualification through proposal / negotiation). Applies the same stage-floor convention quarter-over-quarter so trend is comparable.
Why it matters
Volume-side health of the funnel — when value rises with falling count, deal sizes are growing (often deliberate up-market motion); when count falls without value compensation, top-of-funnel is the problem.
Interpretation guidance
Read alongside average_deal_size and median_deal_size to characterize the motion shape: many small deals (high count / low size) implies a velocity / inside-sales motion; few large deals (low count / high size) implies an enterprise motion; mismatch between intended motion and observed shape is a strategic signal.
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.average_deal_size sales.median_deal_size sales.win_rate sales.pipeline_stage_metrics

Pipeline Flow

sales.pipeline_flow
text Editorial
Description
Container handle for the additive / subtractive pipeline-flow bridge — reconciles opening pipeline to closing pipeline through the period's adds, wins, and losses (opening + new_opps − closed_won − closed_lost = closing) with dual count + value columns. Renders via the FlowSubform widget. The audit trail of the pipeline motion — without this, period-over-period pipeline changes are unexplained. Common pitfall: a "scrub" line (deals reclassified from open to lost mid-period) is needed to keep the math reconciling when CRM hygiene happens; without it the flow appears not to balance and trust in the underlying numbers erodes.
Formula
Container — start/end slots with dual (count + value) columns. Identity that must hold: opening_pipeline_value + new_opps_added_value − closed_won_value − closed_lost_value − scrubs = closing pipeline_value. Same identity holds on the count side using deal counts. Any gap surfaces a data-quality issue worth root-causing before next period.
Why it matters
Makes the period's pipeline changes auditable line-by-line — boards can immediately see whether closing pipeline shrank because deals closed (good) or because deals were lost / scrubbed (bad). Without the flow, only the net change is visible and the underlying motion is opaque.
Interpretation guidance
A healthy flow shows new_opps_added ≈ (closed_won + closed_lost) at steady state (top-of-funnel replacing what closes). When new_opps_added consistently lags closes, the closing pipeline shrinks period-over-period — future quarters will run into coverage stress. Disproportionate scrubs (large negative reclassifications) signal a CRM hygiene problem that's been suppressed.
Source
imboard Editorial
Related KPIs
sales.opening_pipeline_value sales.new_opps_added_value sales.closed_won_value sales.closed_lost_value sales.pipeline_value sales.pipeline_deal_count

Pipeline Key Deals

sales.pipeline_key_deals
text Editorial
Description
Container handle for the field-array of key in-flight deals — each entry tracks deal name, current stage, dollar value, and confidence/commit status. Renders via the CollapsibleFormItemCardGallery widget (a reused gallery pattern shared with HR keyHires / keyOpenings). The "named deals the board should know about" surface — typically the top 5–10 deals by value or strategic importance. Common pitfall: a static list that doesn't reflect the current quarter — these should be refreshed each period to reflect actual top-of-mind deals, not carried forward from prior packs.
Formula
Container — field-array of items (name, stage, value, confidence). No aggregate calculation; the surface's purpose is to make individual deals visible at the board level. Sum of values across the items typically represents a meaningful share (≥ 25%) of the period's quarterly forecast.
Why it matters
Concentrates board attention on the specific deals whose outcomes will determine the quarter — sales leaders often have valuable context (executive relationships, partnership levers) that only the board can deploy. Without named-deal visibility, board help on big deals happens reactively.
Interpretation guidance
If the top 5 deals represent > 60% of the weighted forecast, the quarter is concentration-risky — any single slip is catastrophic. A healthy distribution has the top 5 below 50% of forecast. Track deal-mention persistence across quarters: deals that have appeared 2–3 quarters in a row at "high commit" without closing usually have a structural issue that's been missed.
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.weighted_forecast sales.quarterly_forecast sales.average_deal_size sales.median_deal_size sales.win_rate

Pipeline Quarterly Forecasts

sales.pipeline_quarterly_forecasts
text Editorial
Description
Container handle for the addable per-quarter forecast rows — each row tracks quarter, totalPipelineValue, weightedPipelineValue, expectedCloses (committed forecast), and dealCount. Rendered via the AddableQuarterlyForecastTable widget. Provides the multi-quarter forward visibility view the board reviews to validate the next 2–4 quarters of revenue, not just the current quarter. Common pitfall: filling in only the current quarter and treating future quarters as "we'll figure it out" — multi-quarter forecasting forces honest top-of-funnel planning for the periods beyond the immediate one.
Formula
Container — addable rows of (quarter, totalPipelineValue, weightedPipelineValue, expectedCloses, dealCount). Per-row: totalPipelineValue = same as sales.pipeline_value for that quarter; weightedPipelineValue = same as sales.weighted_forecast; expectedCloses = same as sales.quarterly_forecast; dealCount = pipeline deal count attributed to that close period.
Why it matters
Forward-quarter coverage view — the board needs to see whether next-quarter and next-next-quarter pipelines look credible, not just current. Many revenue misses are visible 2 quarters out if the multi-quarter pipeline view is honest; without this surface, the only data point is "current quarter looks ok."
Interpretation guidance
Next-quarter pipeline coverage should be ≥ 2× quota at the start of current quarter (giving the cycle time to fill in). A pattern of pipeline shrinking quarter-by-quarter from current to current+3 = top-of-funnel capacity gap that demands investment now (3+ months before the affected revenue period).
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.weighted_forecast sales.quarterly_forecast sales.pipeline_deal_count sales.new_opps_added_value

Pipeline Risk Factors

sales.pipeline_risk_factors
text Editorial
Description
Narrative listing the material risks to pipeline conversion or deal timing — specific deal slips, segment headwinds, budget freezes, competitive entry, ICP-fit misses on late-stage deals. Distinct from sales.key_concerns (which covers the whole sales motion) — this is specifically about the forecast / pipeline conversion math. Common pitfall: vague risks ("market is choppy") aren't actionable; a useful entry quantifies the at-risk dollar amount and names specific deals or segments. Renders side-by-side with sales.pipeline_assumptions in the TwoColumnTextarea widget.
Formula
Free-text narrative — no calculation. Convention: 3–5 bulleted risks, each quantified ($X at risk if Y materializes) and time-bound (in-quarter vs structural).
Why it matters
Surfaces the forecast tail risk early enough for the board to engage — large-deal slip risks often have customer-side levers (CEO outreach, partnership offer) that only the board can pull. Without this surface those interventions happen reactively at quarter-end.
Interpretation guidance
Quantified risks (with dollar amounts) are actionable; un-quantified ones consume meeting time without producing decisions. Boards typically ask the team to rank the top 3 risks by expected loss and confirm mitigation owners — a healthy entry pre-empts this.
Source
imboard Editorial
Related KPIs
sales.pipeline_assumptions sales.pipeline_context_notes sales.weighted_forecast sales.quarterly_forecast sales.key_concerns sales.competitive_alerts

Pipeline Stage Metrics

sales.pipeline_stage_metrics
text Editorial
Description
Container handle for the per-stage pipeline metrics grid — for each pipeline stage (qualification, discovery, evaluation, proposal, negotiation, closing) tracks dealCount, totalValue, closingProbability, winRateFromStage, and avgTimeToClose. The most diagnostic surface in the pipeline view: where deals are bunching, which stage is the bottleneck, where conversion math is breaking. Rendered via the StageMetricsGrid widget seeded from PipelineStageValues. Common pitfall: trusting unchanged stage probabilities even as the deal mix shifts — re-calibrate the per-stage close rates quarterly against actuals or the weighted forecast drifts unreliably.
Formula
Container — no scalar calculation. Per-stage rows: dealCount and totalValue are direct sums; closingProbability is the empirical historical close rate from that stage; winRateFromStage is the historical win rate of opportunities that reached that stage; avgTimeToClose is the average days from stage entry to close-won. Closing probabilities should be back-tested against actuals every 1–2 quarters and updated explicitly.
Why it matters
Localizes pipeline problems to specific stages — flat pipeline value with a stage-2 buildup means lead-qualification is too loose; stage-5 stall means closing-skill or pricing-objection issues. Without this surface, the weighted forecast is opaque.
Interpretation guidance
Look for stage where deal count is bunching disproportionately — that is the current bottleneck. Compare win-rate-from-stage at the entry stage (top of funnel) vs late stages: large gaps imply the team is investing time on low-probability deals. The avgTimeToClose by stage should monotonically decrease (later stage = closer to close); if not, stage definitions are likely misaligned with actual buyer behavior.
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.pipeline_deal_count sales.weighted_forecast sales.win_rate sales.avg_sales_cycle_days sales.pipeline_sales_cycle

Pipeline Value

sales.pipeline_value
currency Editorial
Description
Sum of the dollar value of all active deals currently in the sales pipeline — unweighted (raw deal-value sum, not probability-weighted). Boards read this as the top-of-funnel sufficiency check: if pipeline coverage (pipeline value / forecast) drops below the historic conversion-rate-implied threshold, the forecast is at risk. Common pitfall: confusing pipeline value with weighted forecast — the unweighted number always exceeds the weighted, often by 3–5× depending on the stage mix. Always report both and the implied conversion ratio.
Formula
Pipeline Value = Σ (deal_value) across all open opportunities in stages between qualification and signature (excludes closed-won and closed-lost). No probability weighting — for that, see sales.weighted_forecast.
Why it matters
The capacity number for the forecast — without sufficient pipeline value, the forecast is structurally unachievable regardless of close-rate execution. Coverage ratio (pipeline / quota) is the first read on whether the team can hit the period.
Interpretation guidance
Typical SaaS pipeline-coverage benchmark is 3× quota for the current quarter and 4–5× for the next quarter (industry folk-wisdom — varies meaningfully by historical win rate; the right multiple is 1 / historical-win-rate, not a fixed number). Coverage below the historic-conversion-implied threshold is the canonical "you will miss" signal.
Source
imboard Editorial
Related KPIs
sales.weighted_forecast sales.pipeline_deal_count sales.average_deal_size sales.win_rate sales.quarterly_forecast sales.pipeline_stage_metrics

Quarterly Forecast

sales.quarterly_forecast
currency Editorial
Description
The team's expected closed-won dollars for the current quarter — usually a sales-leader judgment call informed by weighted forecast but adjusted for deal-by-deal commit confidence. Distinct from weighted_forecast (which is mechanical, stage × probability). Boards read both: a quarterly_forecast materially below weighted_forecast means the team has explicit negative judgment on specific big deals; above it means they're calling deals stronger than the stage probabilities suggest. Common pitfall: anchoring the call to plan rather than reality — boards quickly learn to discount "we will hit plan" forecasts and reward calibrated commit-vs-actual track records.
Formula
Quarterly Forecast = Sales leadership's committed call on closed-won dollars for the current quarter. Convention: blends weighted forecast (mechanical) with deal-by-deal judgment overlays (commit / best-case adjustments). Typically reported as a single point estimate; some teams report commit / forecast / best-case ranges.
Why it matters
The number the board commits against — quarter-end attainment vs this number is the primary execution scorecard. Track forecast-accuracy (forecast vs actual) over time to calibrate trust in the call.
Interpretation guidance
Forecast attainment within ±5% over 4+ quarters = well-calibrated forecast and a leader the board can rely on. Persistent over-shoot = sandbagging (rebase quota); persistent under-shoot = forecasting / qualification problem worth a methodology change. The drift between weighted forecast and quarterly forecast is itself a signal — large gaps demand explicit explanation.
Source
imboard Editorial
Related KPIs
sales.weighted_forecast sales.pipeline_value sales.closed_won_value sales.win_rate sales.pipeline_quarterly_forecasts

Recognized Revenue

sales.total_revenue
currency Editorial
Description
Total revenue recognized under the company's accounting standard (ASC 606 / IFRS 15) during the period — distinct from billings (what was invoiced) and from ARR (an annualized run-rate snapshot). The income-statement top line and the basis for GAAP reporting. Common pitfall: confusing recognized revenue with ARR — for a company with mid-year contract starts, ARR exit will exceed recognized revenue for that year; the gap shrinks as the cohort matures. Boards reviewing a recognition-heavy investor pack should always see ARR alongside revenue to avoid mis-pricing growth.
Formula
Recognized Revenue = Sum of revenue earned during the period under ASC 606 (or IFRS 15). For subscription contracts, recognized ratably over the contract term; for usage / professional services, recognized as delivered. Distinct from bookings (signed contracts) and billings (invoiced amounts). Public-reporting companies should reconcile this line to the income statement.
Why it matters
The audited top line that anchors every GAAP-based valuation multiple, debt covenant, and tax filing. Boards need it to track the path to profitability (revenue − cost), which subscription ARR alone cannot show.
Interpretation guidance
For an early subscription business, recognized revenue typically lags ARR by 20–40% on an annual basis depending on contract-start distribution within the year; the gap shrinks at steady state. A material divergence between recognized-revenue growth and ARR growth in the same period usually signals either a billing-policy change or a contract-mix shift (e.g. shift to upfront-billed multi-year).
Source
imboard Editorial
Related KPIs
sales.arr sales.carr sales.bookings_backlog sales.bookings_backlog_total sales.gross_margin sales.growth_rate_yoy

Sales Base Currency

sales.base_currency
text Editorial
Description
The ISO-4217 currency code (e.g. `USD`) the sales/pipeline feed displays its money metrics in. The bespoke sales and pipeline feed cards read this to choose the currency symbol/format; absent it, they fall back to USD. This is a board-level reporting-currency constant rather than a measured metric. Common pitfall: leaving it unset on a non-USD board — the feed then silently renders USD symbols over non-USD figures.
Formula
A single ISO-4217 currency code. Not a calculation — it is the display/reporting currency the feed formats sales money figures in.
Why it matters
Ensures the sales/pipeline feed renders money in the board's actual reporting currency instead of a silent USD default.
Interpretation guidance
Should match the board's reporting currency. If FX conversion is applied upstream, this is the post-conversion display currency, not each deal's original currency.
Source
imboard Editorial
Related KPIs
sales.arr sales.pipeline_value

Sales Cycle Quarter-to-Quarter

sales.pipeline_sales_cycle
text Editorial
Description
Container handle for the three-section quarter-over-quarter compare object that tracks average days-to-close trend (lastQuarter / thisQuarter / improvement). Renders via the QuarterToQuarterImprovementGrid widget with three slots. The "is the motion getting faster or slower" diagnostic — cycle length trend is one of the most reliable leading indicators of ICP fit and packaging quality. Common pitfall: comparing without controlling for deal-size mix — if up-market mix is shifting, a flat cycle is actually an improvement (because up-market cycles are inherently longer). Note the mix context in commentary if material.
Formula
Container — three-slot composite. lastQuarter and thisQuarter slots = average sales cycle in days (sales.avg_sales_cycle_days for that period). improvement = (lastQuarter − thisQuarter) / lastQuarter × 100 (positive = cycle compressed = better). When deal-size mix changes materially, the slots should be ACV-segmented separately and the improvement read per segment.
Why it matters
Cycle compression compounds dramatically — a 20% reduction in cycle time roughly translates to a 20% capacity increase for the same headcount. Cycle expansion does the inverse and usually predicts future-period coverage stress.
Interpretation guidance
Cycle compression of 10%+ QoQ at constant ACV mix is a strong signal that ICP / pricing / sales-process changes are working. Expansion of 20%+ at constant mix is the canonical "something is broken in the buyer journey" signal — usually procurement, security, or competitive friction worth a stage-by-stage diagnosis.
Source
imboard Editorial
Related KPIs
sales.avg_sales_cycle_days sales.pipeline_stage_metrics sales.avg_contract_value sales.win_rate sales.pipeline_value

Sales Focus Areas

sales.focus_areas
text Editorial
Description
Forward-looking narrative naming the next-period (typically next-quarter) sales priorities — segment bets, pipeline-coverage actions, hiring focuses, enablement themes, ICP refinements. The "what we're changing or doubling-down on" surface, complementing strategic_context (which is past-tense) and key_concerns (which is present-tense). Common pitfall: listing too many focus areas (3 is the practical maximum a team can actually execute against; 7+ means everything is a priority, i.e. nothing is). Boards use this to track promise-vs-delivery quarter over quarter.
Formula
Free-text narrative — no calculation. Convention: 3 numbered priorities, each with a one-line statement and a measurable next-period success criterion.
Why it matters
Creates accountability across periods — the board can ask "you said X was the focus last quarter, what happened?" Without an explicit list, every quarter looks like a fresh strategy reset.
Interpretation guidance
Focus areas should reappear (with progress) for 2–3 quarters before retiring — single-quarter focus shifts are usually a thrash signal. Track which focuses produce measurable KPI delta vs which produce only activity reports.
Source
imboard Editorial
Related KPIs
sales.strategic_context sales.key_concerns sales.pipeline_assumptions sales.win_rate sales.cac_payback_period

Sales Key Concerns

sales.key_concerns
text Editorial
Description
Free-text narrative of the critical issues, pipeline risks, or blockers in the sales motion that require board attention this period. Distinct from sales.pipeline_risk_factors (which is forecast-specific) — this is the full-stack sales-org concerns list including hiring, comp, churn-cluster patterns, large-deal slippage, and competitive losses. Common pitfall: under-reporting concerns because the team wants to show progress — boards explicitly invite this surface so they can help, and a board pack with no concerns surfaces is itself a yellow flag (either the team is hiding something or not introspecting deeply enough).
Formula
Free-text narrative — no calculation. Convention: 3–7 bulleted concerns, each one sentence framing the issue + one sentence on what is being done.
Why it matters
Lets the board pre-load the discussion topics that need their judgment or network — the most leveraged use of board time. Absent this surface, the conversation drifts to whatever board members notice in the numbers, which is rarely the highest-leverage issue.
Interpretation guidance
A healthy entry names specific deals or accounts, quantifies the at-risk amount where possible, and links to follow-up KPIs. Vague concerns ("market is choppy") consume board time without producing action; ask the team to either quantify or remove.
Source
imboard Editorial
Related KPIs
sales.strategic_context sales.pipeline_risk_factors sales.competitive_alerts sales.focus_areas sales.churn_arr sales.downgrades

Sales Strategic Context

sales.strategic_context
text Editorial
Description
Executive-summary narrative for the sales section of the board pack — the CRO/CEO's one-screen synthesis of overall sales performance, market dynamics, and the story behind the quarter's numbers. Categorical state derived from operational reporting — no calculation. Renders via ExecutiveCommentary widget as multi-section tabbed prose with per-section word counts. Common pitfall: writing it as a numbers-recap repeats what the KPI table already shows; the goal is the connective tissue — why the numbers moved, what changed in the market, what the next 90 days look like. Boards read this first when scanning the deck.
Formula
Free-text narrative — no calculation. Convention: 3–5 sentences per section across overall performance, market dynamics, and forward outlook. The ExecutiveCommentary widget enforces a soft word-count target per section.
Why it matters
Provides the interpretive frame that turns the raw KPI table into a story the board can debate. Without it, board members default to their own (often wrong) interpretation of the numbers.
Interpretation guidance
A well-written entry calls out one or two surprises and links them to actionable next steps; a poorly-written entry just narrates the KPIs back. If the prose only describes what the numbers show, treat it as missing context — push back during pre-read.
Source
imboard Editorial
Related KPIs
sales.key_concerns sales.focus_areas sales.competitive_alerts sales.arr sales.growth_rate_yoy

Starting ARR

sales.starting_arr
currency Editorial
Description
Opening ARR at the beginning of the period — the baseline against which the period's ARR waterfall (new + expansion − downgrades − churn) reconciles to ending ARR. Equal to the prior period's closing ARR by construction. The FlowSubform widget binds starting_arr as the `start` slot of the ARR-bridge flow, and the ending position is computed as start + Σ(deltas). Common pitfall: restating starting_arr mid-period to "fix" a prior-period reporting error breaks the period-over-period audit trail; corrections should land as a separate restatement note, not by editing the opening balance.
Formula
Starting ARR = ARR snapshot at period open = the prior period's closing ARR. Identity that must hold: starting_arr + new_business + expansion − downgrades − churn_arr = ending ARR (sales.arr at period close). Reconcile any gap as a "data quality" line and root-cause it before next period.
Why it matters
The anchor of the ARR waterfall — without an explicit starting point, the period's net-new ARR cannot be audited. Boards expect the waterfall to reconcile to the penny, period over period.
Interpretation guidance
If starting_arr ≠ prior-period ending ARR, there is either a restatement or a data issue — surface it explicitly. Beyond that the value itself is descriptive, not interpretive; the interpretive work happens on the delta lines.
Source
imboard Editorial
Related KPIs
sales.arr sales.new_business sales.expansion sales.churn_arr sales.downgrades

Weighted Pipeline Forecast

sales.weighted_forecast
currency Editorial
Description
Total pipeline value with each deal multiplied by its stage-based close probability — the canonical probabilistic forecast number. More forecasting-useful than raw pipeline value because it accounts for the conversion-likelihood mix across stages (early-stage deals weighted ~10–25%, mid-stage ~40–60%, late-stage ~70–90%). Common pitfall: using globally-flat probabilities (e.g. always 50%) instead of stage-specific calibrated ones — a reliable weighted forecast requires the stage probabilities to be back-tested against actual close rates from prior periods.
Formula
Weighted Forecast = Σ (deal_value × stage_close_probability) across all open opportunities. Stage probabilities should be the empirical historical close rate by stage for the comparable cohort (segment / motion / quarter-of-year mix), not arbitrary fractions.
Why it matters
The single most-cited number in the weekly forecast call — the team's probabilistic answer to "what will we close." Boards compare it to commit and quota to assess delivery risk.
Interpretation guidance
Weighted forecast trending up while pipeline value is flat usually means deals are advancing through stages (good); trending flat while pipeline grows usually means new deals are entering early stages but not advancing (top-of-funnel-only growth — yellow flag). A weighted forecast meaningfully below quota mid-quarter is the canonical "you will miss without intervention" signal.
Source
imboard Editorial
Related KPIs
sales.pipeline_value sales.quarterly_forecast sales.pipeline_stage_metrics sales.win_rate sales.closed_won_value

Win Rate

sales.win_rate
percentage (%) Editorial
Description
Percentage of closed opportunities that resulted in closed-won (vs closed-lost) during the period. The single best read on bottom-of-funnel execution and the most direct input to pipeline-coverage math (required coverage = 1 / win rate). Common pitfall: computing win rate without disqualifying "no decision" outcomes inflates losses and depresses the rate artificially; the SaaS norm is to either bucket no-decisions separately or track a two-rate view (raw win rate vs ICP-fit win rate excluding no-decisions). Stage-segment cuts (SMB vs Enterprise) usually differ 2×–4× and should be reported separately when volume permits.
Formula
Win Rate = (Closed-Won Count / (Closed-Won Count + Closed-Lost Count)) × 100. Excludes "no decision" / paused opportunities (track separately). Compute on count basis for execution analysis; compute on value basis (Won Value / (Won Value + Lost Value)) for dollar-weighted view.
Why it matters
Reciprocal of required pipeline coverage — a 25% win rate requires 4× pipeline coverage to hit quota. Drives capacity planning, quota setting, and the pipeline-coverage commit conversation.
Interpretation guidance
Typical SaaS win rates (industry folk-wisdom, not citation-grade): inbound-heavy SMB motions 25–40%, mid-market 15–25%, enterprise / outbound-heavy 10–20%. Sharp drops (≥ 10pp over 2 quarters) at constant motion mix is the canonical "competitive entry" or "ICP drift" signal — investigate loss-reason distribution before changing tactics.
Source
imboard Editorial
Related KPIs
sales.closed_won_count sales.closed_lost_count sales.closed_won_value sales.closed_lost_value sales.pipeline_value sales.pipeline_stage_metrics sales.competitive_alerts

Customers 26 KPIs

% ARR at Risk

customers.percent_arr_at_risk
percentage (%) Editorial
Description
Share of total ARR flagged as at-risk for churn or contraction — the proportional view that complements the absolute `arr_at_risk` dollar figure. Computed as `arr_at_risk ÷ total ARR`. The board reads this as the worst-case-near-term-NRR-impact ceiling: if every at-risk account actually churned in-period, NRR would drop by roughly this percentage (before expansion offset). Common pitfall: the "at-risk" definition is internal and varies by company — a 12% percent_arr_at_risk under a conservative flagging rule is a very different signal than 12% under an aggressive rule. Document the flag rule and hold it constant.
Formula
percent_arr_at_risk = arr_at_risk ÷ total ARR. The numerator inherits the company-specific "at-risk" flag definition documented on `customers.arr_at_risk`.
Why it matters
Normalizes the at-risk dollar figure so it scales with the business. A 10% at-risk share is the same proportional threat at $5M ARR as at $50M ARR; the absolute figure alone hides that.
Interpretation guidance
No citation-grade industry benchmark; widely-cited industry folk-wisdom (not citation-grade) flags >15% percent_arr_at_risk as a destructive threshold worth board escalation — the `ArrAtRiskGauge` widget uses this internally. Trend it month-over-month — sustained growth in this share predicts a downward NRR move next quarter even if no single account has churned yet.
Source
imboard Editorial
Related KPIs
customers.arr_at_risk customers.churn_risks customers.top_customer_concentration customers.net_revenue_retention customers.gross_revenue_retention sales.arr

ACV Trend

customers.acv_trend_pct
percentage (%) Editorial
Description
Period-over-period percent change in Average Contract Value (mean ARR per active customer logo). A rising ACV trend signals pricing power, successful tier upgrades, or a mix-shift toward larger customers; a falling ACV trend signals seat compression, discounting pressure, or a mix-shift toward smaller customers. The board reads this alongside `total_customers` and `customers.net_revenue_retention` to disambiguate which lever is moving — logo growth vs. expansion vs. price. Common pitfall: ACV mix-shifts (a wave of new SMB logos at low ACV) can drag the average down even when existing-customer ACV is rising — segment-cut ACV is more diagnostic than the blended number.
Formula
acv_trend_pct = (ACV_current_period − ACV_prior_period) ÷ ACV_prior_period, where ACV = total ARR ÷ total active customer logos. The blended view is sensitive to logo-mix shifts; segment-cut ACV (by cohort, ACV band, or product tier) is more diagnostic.
Why it matters
Separates "more customers" from "bigger customers" in growth narrative. Combined with logo count, isolates the pricing-power signal that NRR and ARR alone can blur.
Interpretation guidance
No citation-grade absolute benchmark exists — compare to the company's own trailing trend and to deliberate strategy (a downmarket push should show ACV declining). Pair with segment cuts: a blended ACV that's flat may hide an upmarket cohort growing 20% offset by a downmarket cohort growing 50% in count. Persistent decline with flat NRR signals discounting / seat compression — surface the cause in `retention_insights` or `expansion_opportunities`.
Source
imboard Editorial
Related KPIs
sales.avg_contract_value customers.total_customers customers.net_revenue_retention customers.expansion_opportunities sales.arr

ARR at Risk

customers.arr_at_risk
currency Editorial
Description
Sum of ARR from customers flagged "at-risk" by the customer-success team — typically driven by usage decline, low health score, executive turnover at the customer, missed milestones, or explicit churn intent. The board reads this as the worst-case near-term churn exposure if no intervention happens. Common pitfall: the "at-risk" definition drifts across CSMs and quarters; standardize the criteria (e.g. health score below threshold OR 30-day usage drop > X% OR cancellation request received) and version-control the playbook so the absolute number is comparable period-over-period. Pair with `percent_arr_at_risk` for the proportional read.
Formula
arr_at_risk = Σ(ARR of customers flagged at-risk by CS team). The "at-risk" flag itself is a company-specific definition (typical components: health score below threshold, 30-day usage decline, executive churn at customer, missed onboarding milestones, explicit churn signal). Document the flag rule in `customer_definition_note` so the number is comparable across periods.
Why it matters
Converts the qualitative CS pipeline into a board-readable dollar exposure. Forces the team to put a number on hand-wavy customer-health concerns and surfaces concentration risk (a single $500K at-risk account is a different conversation than fifty $10K accounts).
Interpretation guidance
Always present alongside `percent_arr_at_risk` — $500K at-risk is a 5% problem at $10M ARR but a 0.5% problem at $100M ARR. There is no citation-grade industry benchmark for the absolute number; the >15% destructive threshold the `ArrAtRiskGauge` widget uses is internal heuristic, not an external standard. Trend month-over-month — sustained growth in `arr_at_risk` is the leading indicator that NRR will deteriorate next quarter.
Source
imboard Editorial
Related KPIs
customers.percent_arr_at_risk customers.churn_risks customers.top_customer_concentration customers.net_revenue_retention customers.gross_revenue_retention sales.arr

Average Contract Value (ACV)

customers.avg_contract_value
currency Editorial
Description
Average annualized contract value across the active customer base (or across new logos, depending on the board's convention — document which). Expressed in the reporting currency. The board reads ACV alongside total customers to disambiguate logo-led vs. value-led growth, and against `customers.prior_quarter_acv` to see whether deal sizes are trending up (enterprise mix shift) or down (SMB dilution). Common pitfall: mixing new-logo ACV and blended-base ACV across periods — they trend differently; pick one and hold it.
Formula
ACV = total annualized contract value ÷ customer count, over the chosen population (active base or new logos). Hold the population definition constant period-over-period.
Why it matters
The unit-economics lever beneath ARR — rising ACV with flat logo growth signals a successful move up-market; falling ACV signals SMB dilution or discounting.
Interpretation guidance
Trend against `customers.prior_quarter_acv` for direction. Absolute ACV bands are segment-specific (SMB vs. enterprise) — industry folk-wisdom, not citation-grade.
Source
imboard Editorial
Related KPIs
customers.prior_quarter_acv customers.total_customers customers.new_customers_added sales.arr

Churn Risks

customers.churn_risks
text Editorial
Description
Named at-risk accounts, root-cause analysis of why they're at risk, and the mitigation plan in flight. Pairs with the quantitative `arr_at_risk` and `percent_arr_at_risk` and gives the board the names + the playbook. Common pitfall: listing the at-risk accounts without the diagnosis or the plan — the board reader needs to see what the team is doing about it, not just what the team is worried about. Also: avoid using this surface as a generic "things are bad" venting forum — keep it account-specific and action-specific.
Formula
Qualitative — no calculation. Per-account list: account name, ARR exposure, churn driver (usage decline, exec churn, missed milestone, competitive loss, integration friction, etc.), mitigation owner, mitigation plan, next checkpoint date.
Why it matters
Converts the dollar exposure (`arr_at_risk`) into a board-readable narrative of named accounts and concrete plans. Forces the CS team to articulate the diagnosis, not just the symptom.
Interpretation guidance
Anti-pattern: an aggregate "we have $X at risk" with no per-account breakdown. Strong content lists the top 3–5 at-risk accounts by ARR, with cause and plan for each. If the team is asking the board for help (intro, exec sponsor, pricing relief), surface the ask here explicitly — boards routinely catch resolvable churn that the team would not have escalated.
Source
imboard Editorial
Related KPIs
customers.arr_at_risk customers.percent_arr_at_risk customers.top_customer_concentration customers.retention_insights customers.key_initiatives

Customer Segments

customers.customer_segments
text Editorial
Description
Container handle for the field-array of customer segments — each entry carries a segment name, its customer count, and its ARR. Feeds the bespoke customers card's segmentation stack-bars (customer-count + revenue distribution across segments). The "where does the revenue / logo base concentrate" surface (e.g. Enterprise / Mid-Market / SMB). Common pitfall: segment definitions drifting over time, or segment ARR not summing to total ARR because a segment is missing — keep the segmentation exhaustive and the cut stable.
Formula
Container — field-array of { name, customerCount, segmentARR } rows. No aggregate calculation; the surface's purpose is to show the distribution. Segment counts should sum to total customers and segment ARR to total ARR.
Why it matters
Shows where the book concentrates by logo and by revenue — a base that is logo-heavy in SMB but revenue-heavy in Enterprise has a very different risk profile than its headline ARR suggests.
Interpretation guidance
Compare the logo distribution to the ARR distribution: a small Enterprise logo share carrying most of the ARR is a concentration flag worth reading with `customers.top_customer_concentration`.
Source
imboard Editorial
Related KPIs
customers.total_customers customers.top_customer_concentration sales.arr

Customer Success Initiatives

customers.key_initiatives
text Editorial
Description
Active programs the CS / Product / Sales team is running to improve customer health, NPS, retention, or expansion — onboarding revamps, health-score model updates, success-plan rollouts, expansion playbooks, advocacy programs, executive-business-review cadence changes. The board reads this as the "what are we doing about it" companion to the metric pages and the at-risk narrative. Common pitfall: listing initiatives without owner, target metric movement, or checkpoint date — the board cannot follow up on vague programs.
Formula
Qualitative — no calculation. Per-initiative list: initiative name, target metric (which KPI it intends to move), owner, status (planned / in-progress / launched / measuring), next checkpoint date.
Why it matters
Closes the loop between the metrics page and the "what we're doing" board narrative. Lets the board hold the team accountable to the actions, not just the outcomes — especially valuable when KPI movement lags initiative launch by a quarter or two.
Interpretation guidance
Anti-pattern: a wishlist of initiatives without owners or target metrics. Strong content states which KPI each initiative aims to move (e.g. "Onboarding revamp → targeting +5pp lift in 90-day GRR for SMB cohort by Q2"), names the owner, and gives a checkpoint date. Closed initiatives should report the actual metric movement vs. target — wins and misses both teach the board.
Source
imboard Editorial
Related KPIs
customers.retention_insights customers.expansion_opportunities customers.churn_risks customers.net_revenue_retention customers.nps_score

Customers Churned

customers.customers_churned
number Editorial
Description
Count of customer logos that ended their subscription/contract during the period. Includes voluntary cancellations and non-renewals. Some companies separately track downgrade-to-zero as churn — be explicit about whether downgrades that drop ARR to $0 count as churn (typical: yes) vs. material contraction that keeps ARR > 0 (typical: tracked under contraction, not churn). The board reads this as the raw count behind `logo_churn_rate`; the percentage tells you the rate, the absolute count tells you the volume of CS pain. Common pitfall: counting customers that re-activate (sometimes called "boomerang" or resurrection) — settle the rule (typical: count each cancellation event, do not net resurrection).
Formula
customers_churned = count of customer logos that ended their paid subscription/contract during the period. Voluntary cancellations + non-renewals. Downgrade-to-$0 typically counts; document the rule and hold it constant.
Why it matters
The absolute volume read on customer loss. The percentage (`logo_churn_rate`) tells you the rate; the count tells you the CS team load and the number of post-mortem conversations needed.
Interpretation guidance
No citation-grade absolute benchmark exists — the right comparison is to the company's own trailing periods and to its starting logo count. Pair with `logo_churn_rate` for proportional context and with `churn_risks` for the qualitative narrative. A spike with stable rate means the install base grew; a spike with rising rate is a quality signal — both deserve a board comment.
Source
imboard Editorial
Related KPIs
customers.logo_churn_rate customers.logo_retention_rate customers.total_customers customers.churn_risks

Expansion Opportunities

customers.expansion_opportunities
text Editorial
Description
Identified upsell, cross-sell, and seat-expansion opportunities inside the existing customer base, with deal size and timing where known. This is the qualitative narrative behind the expansion component of NRR — what the CS / Sales team sees in the pipeline that has not yet converted. The board reads this as forward-looking signal on whether NRR will trend up or down next quarter. Common pitfall: confusing "opportunities" (real conversations with named accounts) with "addressable upside" (theoretical TAM uplift) — keep this field anchored in actual pipeline.
Formula
Qualitative — no calculation. Narrative list of named-account expansion opportunities (seat count, module add-on, tier upgrade, cross-sell) with estimated deal size and target close period when available.
Why it matters
Forward-looking signal on NRR trajectory. A thin expansion pipeline is the leading indicator of NRR compression — boards catch it here before it shows up in the metric next quarter.
Interpretation guidance
Anti-pattern: vague "we see room to expand in mid-market" framing without named accounts or sizing. Strong content lists ≥3 named opportunities with deal size estimates and target timing, plus a short note on the blocking gate (procurement, integration, exec sponsor) for each. If the pipeline is genuinely thin, write that explicitly — the board needs to know.
Source
imboard Editorial
Related KPIs
customers.net_revenue_retention customers.retention_insights customers.key_initiatives sales.arr

Gross Revenue Retention (GRR)

customers.gross_revenue_retention
percentage (%) Industry-backed
Description
Recurring revenue retained from the cohort of customers present at the start of the period, excluding expansion — so the metric captures only churn and contraction. Per the SaaS Metrics Standards Board (SMSB) GRR standard. GRR is bounded at 100% (cannot exceed it) and reads as the "no-defense-against-churn" floor on retention. The board reads GRR alongside NRR (`customers.net_revenue_retention`) — the gap between them is the expansion contribution. Common pitfall: treating GRR and NRR as substitutes — they answer fundamentally different questions, and a healthy NRR with sliding GRR signals churn masked by upsell.
Formula
GRR = (Starting ARR − Contraction − Churn) ÷ Starting ARR, on the cohort active at period start. Excludes expansion. Capped at 100% by definition. Per SMSB GRR standard.
Why it matters
Isolates the "do customers stay and not shrink" signal from expansion noise. GRR is the true downside floor on retention — boards use it to spot product or onboarding deterioration that NRR can hide.
Interpretation guidance
Per KBCM/Sapphire Private SaaS Company Survey 2024 (§ "Gross Dollar Retention"), private SaaS GRR medians typically sit in the high-80s to low-90s, with top-quartile in the mid-90s — distributions vary by ACV cohort and vintage, so pull the current edition. The NRR − GRR gap quantifies expansion contribution; a widening gap with declining GRR is a yellow flag (expansion masking churn). Trend it quarterly — a single-period drop with steady NRR usually means a one-off contraction; persistent decline with flat NRR is a product issue.
Benchmark
p25 82 % · median 91 % · p75 95 %
Related KPIs
customers.net_revenue_retention customers.logo_retention_rate customers.logo_churn_rate customers.churn_risks sales.arr

Logo Churn Rate

customers.logo_churn_rate
percentage (%) Industry-backed
Description
Share of customer logos lost during the period — the inverse of logo retention. Numerator is logos that churned during the period; denominator is logos present at period start. Per the KBCM/Sapphire Private SaaS Company Survey definition (treated as the de-facto private-SaaS reporting convention). The board reads this as the simplest churn signal — independent of revenue-weighting. Common pitfall: confusing annualized vs. period-rate (monthly churn × 12 ≠ annualized churn for a compounding base) — be explicit about the time window and annualization method.
Formula
logo_churn_rate = customers_churned ÷ (customers active at period start). Mathematically: 1 − logo_retention_rate. Annualization for monthly/quarterly rates should be explicit (e.g. (1 − monthly_retention)^12, not monthly_churn × 12).
Why it matters
Direct read on whether customers are walking away. Independent of revenue-weighting, so it cannot be masked by a few large expansions.
Interpretation guidance
Per KBCM/Sapphire Private SaaS Company Survey 2024 (§ "Customer Churn"), private SaaS logo churn typically sits in the high single digits annually, with top-quartile below 5% — but distributions are highly sensitive to ACV cohort (low-ACV SMB SaaS routinely sees 20%+ annual logo churn; six-figure enterprise contracts often see <3%). Pull the current vintage rather than citing a stale point estimate. Pair with `customers_churned` (absolute count) and `gross_revenue_retention` (revenue-weighted view).
Benchmark
p25 5 % · median 13 % · p75 20 %
Related KPIs
customers.logo_retention_rate customers.customers_churned customers.gross_revenue_retention customers.net_revenue_retention customers.churn_risks

Logo Retention Rate

customers.logo_retention_rate
percentage (%) Industry-backed
Description
Share of customer logos retained from the prior period, counted by logo (not by revenue). Per the SaaS Metrics Standards Board (SMSB) Logo Retention standard: numerator is logos present at both period start and period end; denominator is logos present at period start. New logos acquired during the period are excluded from both. The board reads this as a "stickiness" signal independent of ACV: high logo retention with weak NRR points to flat/contracting expansion; weak logo retention with strong NRR points to high concentration risk. Common pitfall: conflating logo retention with revenue retention — they answer different questions and routinely diverge.
Formula
logo_retention_rate = (logos active at period start AND active at period end) ÷ (logos active at period start). Excludes net-new logos acquired in-period. Per SMSB Logo Retention standard.
Why it matters
Isolates retention quality from revenue-weighting effects. A handful of large expansions can mask high logo churn in NRR — logo retention surfaces it directly.
Interpretation guidance
Per KBCM/Sapphire Private SaaS Company Survey 2024, private SaaS logo retention concentrates in the high-80s to mid-90s (median around 90% for the broad sample, higher for enterprise contract ACVs). Treat distributional ranges as period- and segment-specific; pull the current vintage of the source rather than relying on a memorized number. Pair every value with `logo_churn_rate` (1 − this) for the inverse view and `customers_churned` for the absolute count.
Related KPIs
customers.logo_churn_rate customers.customers_churned customers.gross_revenue_retention customers.net_revenue_retention

Net Revenue Retention (NRR)

customers.net_revenue_retention
percentage (%) Industry-backed
Description
Recurring revenue retained from the cohort of customers present at the start of the period, including expansion (upsell, cross-sell, price increases) and net of churn and contraction — but excluding revenue from net-new logos acquired in-period. Per the SaaS Metrics Standards Board (SMSB) NRR standard. NRR above 100% means the cohort grew faster than it lost — a hallmark of strong product-led expansion. The board reads NRR alongside GRR (`customers.gross_revenue_retention`) to separate the "keep + expand" signal from the "just keep" signal. Common pitfall: mixing GAAP revenue and ARR in numerator vs. denominator, or letting net-new logo revenue leak in — both inflate the number; SMSB is explicit that the cohort is closed at period start.
Formula
NRR = (Starting ARR + Expansion − Contraction − Churn) ÷ Starting ARR, measured on the cohort active at period start. New-logo ARR acquired in-period is excluded from both numerator and denominator. Per SMSB NRR standard.
Why it matters
The single best read on whether existing customers love the product enough to pay more over time. Strong NRR (>100%) compounds — it lets growth come from inside the install base, lowering reliance on new-logo acquisition and improving capital efficiency.
Interpretation guidance
Per KBCM/Sapphire Private SaaS Company Survey 2024 (§ "Net Dollar Retention"), private SaaS NRR medians have historically clustered around the low-to-mid 100s, with top-quartile in the 110s+ — but cuts vary year-over-year and by ACV segment, so pull the current edition rather than citing a stale point estimate. Top-quartile public SaaS (per typical Bessemer State of the Cloud commentary) cite NRR ≥ ~120% as the benchmark for "best-in-class" expansion — treat that thresholds as industry folk-wisdom unless cited to a named edition. Always pair NRR with GRR: a wide gap means expansion is propping up underlying churn.
Benchmark
p25 96 % · median 101 % · p75 109 %
Related KPIs
customers.gross_revenue_retention customers.logo_retention_rate customers.logo_churn_rate customers.expansion_opportunities sales.arr

New Customers Added

customers.new_customers_added
number Editorial
Description
Count of net-new customer logos acquired during the period (excludes expansion of existing accounts and re-activated churned logos unless they signed a fresh contract). The board reads this alongside `customers.customers_churned` to derive the net logo change and alongside `customers.prior_quarter_total_customers` to reconcile the logo bridge (prior total + new − churned = current total). Common pitfall: counting signed-but-not-yet-live logos here while counting them as live in `customers.total_customers` — keep the activation cut-off consistent across both.
Formula
Count of customer logos that signed their first paid contract in the period. Excludes expansion/upsell on existing logos. Net logo change = new_customers_added − customers_churned.
Why it matters
The acquisition half of the logo bridge — pairs with churn to show whether the customer base is growing by count, independent of ARR mix.
Interpretation guidance
Read with `customers.customers_churned` and `customers.prior_quarter_total_customers` to reconcile the logo bridge. Absolute counts are stage- and ACV-specific (industry folk-wisdom, not citation-grade) — compare to the company's own trailing trend.
Source
imboard Editorial
Related KPIs
customers.total_customers customers.customers_churned customers.prior_quarter_total_customers customers.avg_contract_value

NPS Responses

customers.nps_responses
number Editorial
Description
The number of survey responses the current `customers.nps_score` is computed from — the confidence qualifier the board must read alongside any NPS value. Per the NPS methodology (Reichheld/Bain), a score from a small or unrepresentative sample is unreliable; surfacing the response count lets the board discount low-n scores. Common pitfall: celebrating (or alarming at) an NPS swing that is actually a sample-size artifact — always read the score and the response count together.
Formula
Count of completed survey responses behind the period's NPS score. Response rate = nps_responses ÷ customers surveyed.
Why it matters
The confidence denominator under NPS — an NPS based on <50 responses or <10% response rate should be flagged low-confidence rather than trended.
Interpretation guidance
Read with `customers.nps_score` and `customers.nps_trend`. A trend across periods with materially different response counts may be measurement noise, not a real movement.
Source
imboard Editorial
Related KPIs
customers.nps_score customers.nps_trend

NPS Score

customers.nps_score
number Industry-backed
Description
Net Promoter Score — % of survey respondents who are promoters (score 9–10) minus % detractors (0–6), passives (7–8) excluded. Per the original NPS methodology (Reichheld, Bain & Company, 2003). The score ranges from −100 to +100. The board reads NPS as one read on product-market fit and word-of-mouth potential, not as a precise customer-loyalty measurement — the methodology is well-known for being sensitive to sample bias, response rate, and survey timing. Common pitfall: comparing NPS across companies without normalizing for industry — B2B SaaS NPS distributions sit much higher than consumer-app NPS, and the absolute number means little without a peer cohort.
Formula
NPS = (% promoters, score 9–10) − (% detractors, score 0–6). Passives (7–8) are excluded from both. Range: −100 to +100. Per Bain & Company / Reichheld NPS methodology (HBR 2003, "The One Number You Need to Grow").
Why it matters
A coarse-grained directional read on customer affection and word-of-mouth potential. Sustained movement (especially regressions) is the signal the board should focus on, not absolute values — the methodology is too noisy for fine comparisons across companies.
Interpretation guidance
Per Retently NPS Benchmarks 2025, B2B SaaS NPS medians by industry cluster around the +30 to +50 band, with top-quartile +50 to +70. Translate scores to categories: −100 to 0 = needs work, 0–30 = good, 30–70 = great, 70–100 = excellent — these category bands are widely circulated industry folk-wisdom (Bain does not publish strict thresholds). Always pair the score with sample size and response rate; an NPS based on <50 responses or <10% response rate should be flagged as low-confidence.
Benchmark
p25 20 count · median 36 count · p75 50 count
Related KPIs
customers.nps_trend customers.retention_insights customers.churn_risks customers.key_initiatives

NPS Trend

customers.nps_trend
number Editorial
Description
Period-over-period change in NPS score — the trajectory signal that matters more than any single absolute score. A 5-point swing between adjacent quarters is usually more informative than a "good" or "bad" absolute label, because the methodology's noise floor is high enough that absolute comparisons across companies (or even across quarters with different sample sizes) are unreliable. The board reads this to spot deterioration early — a persistent multi-quarter decline is one of the leading indicators of pending churn. Common pitfall: comparing periods with very different sample sizes or response rates — a "decline" from 45 to 35 means very different things at n=30 vs. n=300.
Formula
nps_trend = NPS_current_period − NPS_prior_period (delta in points, not %). Sample size and response rate should be reported alongside both periods.
Why it matters
NPS's methodology noise makes absolute scores hard to interpret across companies. The trend within a single company's own measurement cadence is more reliable — a sustained decline is a leading indicator of churn risk even when the absolute score still reads "good".
Interpretation guidance
There is no citation-grade benchmark for trend magnitude; treat any single-quarter swing of ±5 points or more as worth narrative explanation, and any 2+ consecutive declines as a yellow flag for the board. Always cite sample size deltas — if response rate or n changed materially, the trend may be measurement artifact rather than real movement.
Source
imboard Editorial
Related KPIs
customers.nps_score customers.retention_insights customers.churn_risks customers.key_initiatives

Prior-Quarter ACV

customers.prior_quarter_acv
currency Editorial
Description
The average contract value reported in the PRIOR period — the comparison anchor for the current `customers.avg_contract_value`. The board reads the two together to render the ACV trend chip on the bespoke customers card (delta + direction) without recomputing it. Common pitfall: comparing a prior new-logo ACV to a current blended-base ACV — keep the population definition identical across the two periods or the trend is an artifact.
Formula
ACV from the prior period, computed over the SAME population as the current `customers.avg_contract_value`. acv_trend ≈ (avg_contract_value − prior_quarter_acv) ÷ prior_quarter_acv.
Why it matters
Lets the board read ACV direction at a glance — the single most useful framing of an ACV number is its own trajectory.
Interpretation guidance
Only meaningful when the population definition matches the current-period ACV. A large jump usually reflects mix shift (a few large enterprise signings), not a uniform price increase.
Source
imboard Editorial
Related KPIs
customers.avg_contract_value customers.acv_trend_pct customers.total_customers

Prior-Quarter Concentration

customers.prior_quarter_concentration
percentage (%) Editorial
Description
The top-customer ARR concentration reported in the PRIOR period — the comparison anchor for the current `customers.top_customer_concentration`. The board reads the two together to render the concentration trend on the bespoke customers card (rising concentration = growing single-account dependency risk). Common pitfall: comparing a top-1 concentration to a top-5 concentration across periods — keep the "top-N" cut identical.
Formula
Top-customer ARR concentration from the prior period, on the same top-N cut as the current `customers.top_customer_concentration`. concentrationTrend = top_customer_concentration − prior_quarter_concentration.
Why it matters
Direction is the signal — rising concentration means a single account's health increasingly drives the whole book's risk.
Interpretation guidance
A rising trend warrants a board note on the largest accounts' renewal timing and health. Hold the top-N cut constant across periods.
Source
imboard Editorial
Related KPIs
customers.top_customer_concentration customers.arr_at_risk customers.percent_arr_at_risk

Prior-Quarter GRR

customers.prior_quarter_grr
percentage (%) Editorial
Description
Gross Revenue Retention reported in the PRIOR period — the comparison anchor for the current `customers.gross_revenue_retention`. The board reads the two together to render the GRR trend on the bespoke customers retention grid. Per the SMSB GRR definition (excludes expansion, capped at 100%), both periods must use the same cohort basis for the delta to mean anything. Common pitfall: reading a GRR trend without the matching NRR trend — the gap between them is the expansion signal.
Formula
GRR from the prior period, on the same SMSB closed-start cohort basis as the current `customers.gross_revenue_retention`. grrTrend = gross_revenue_retention − prior_quarter_grr (in points).
Why it matters
A declining GRR is the truest early churn signal — it cannot be masked by expansion the way NRR can.
Interpretation guidance
Persistent GRR decline with flat NRR is a product/onboarding problem hidden by upsell. Pair with `customers.prior_quarter_nrr` to read the expansion gap.
Source
imboard Editorial
Related KPIs
customers.gross_revenue_retention customers.prior_quarter_nrr customers.net_revenue_retention

Prior-Quarter NRR

customers.prior_quarter_nrr
percentage (%) Editorial
Description
Net Revenue Retention reported in the PRIOR period — the comparison anchor for the current `customers.net_revenue_retention`. The board reads the two together to render the NRR trend on the bespoke customers retention grid. Per the SMSB NRR cohort definition, both periods must use the same closed-start-cohort methodology for the delta to be meaningful. Common pitfall: comparing an NRR computed on a different cohort window across the two periods.
Formula
NRR from the prior period, on the same SMSB closed-start cohort basis as the current `customers.net_revenue_retention`. nrrTrend = net_revenue_retention − prior_quarter_nrr (in points).
Why it matters
Direction matters more than level for retention — a slipping NRR is an early expansion-engine warning even while still above 100%.
Interpretation guidance
A multi-point decline with steady GRR means expansion is decelerating; pair with `customers.prior_quarter_grr` to separate the "keep" signal from the "expand" signal.
Source
imboard Editorial
Related KPIs
customers.net_revenue_retention customers.prior_quarter_grr customers.gross_revenue_retention

Prior-Quarter Total Customers

customers.prior_quarter_total_customers
number Editorial
Description
The total active customer-logo count at the END of the prior reporting period — the opening balance for the current period's logo bridge. The board reads this so the bespoke customers card can show beginning vs. ending logos and the net change without having to re-derive the opening balance from `total_customers − new + churned`. Common pitfall: silently re-stating the prior total after a definitional change to "customer" — hold the counting unit constant or footnote the restatement.
Formula
Active paying customer logos at the prior period close. Serves as the opening balance: prior_quarter_total_customers + new_customers_added − customers_churned = total_customers.
Why it matters
Gives the board the explicit beginning-of-period logo balance so the logo bridge reconciles without inference.
Interpretation guidance
When this plus net change does not equal `customers.total_customers`, a definitional change or mid-period restatement happened — surface it rather than absorbing it silently.
Source
imboard Editorial
Related KPIs
customers.total_customers customers.new_customers_added customers.customers_churned

Retention Insights

customers.retention_insights
text Editorial
Description
Free-form commentary from the CS / Sales leadership on retention trends, cohort behavior, and underlying drivers of loyalty (or its absence). Pairs with the quantitative retention KPIs (NRR, GRR, logo retention) and gives the board the "why" behind the numbers — which cohorts are strong, which are weak, what feature engagement correlates with retention, what onboarding changes are landing. Common pitfall: filler prose that restates the numbers without adding causal insight — a board reader should learn something here they could not infer from the metrics page alone.
Formula
Qualitative — no calculation. Free-form narrative commentary, typically 100–300 words, with concrete cohort references (e.g. "Q3 2024 cohort showing 12% better NRR than Q2 2024 cohort, driven by adoption of feature X within 30 days of go-live").
Why it matters
Adds causal explanation to the retention numbers — boards optimize for diagnoses, not just descriptions. Reading "NRR slipped from 115% to 108%" is half the story; reading "NRR slipped because two large customers cut seat counts as they integrated us with an acquired vendor — non-recurring" is the actionable version.
Interpretation guidance
Anti-pattern: prose that restates the numbers ("NRR was 108% this quarter, down from 115%") without explaining drivers. Strong content names specific cohorts, customer segments, or product surfaces and ties them to the metric movement. If the team cannot articulate the "why" yet, write "diagnosis in progress — investigating with CS team, update next board" rather than padding.
Source
imboard Editorial
Related KPIs
customers.net_revenue_retention customers.gross_revenue_retention customers.logo_retention_rate customers.expansion_opportunities customers.churn_risks

Retention Reporting Method

customers.reporting_method
text Editorial
Description
Whether the company TRACKS cohort retention (NRR/GRR) or does NOT yet track it — the explicit signal the bespoke customers retention grid reads to choose between the tracked 4-card retention view and the not-tracked empty state. Value is `tracked` or `not_tracked`. Without this canonical field the card has to INFER "tracked" and can never honestly render the not-tracked state. Common pitfall: leaving NRR/GRR blank to mean "not tracked" — that is ambiguous with "tracked but zero"; this explicit enum removes the ambiguity.
Formula
Enum: 'tracked' (the company computes cohort NRR/GRR) or 'not_tracked' (it does not yet). Drives whether the retention grid renders values or its not-tracked state.
Why it matters
Lets the board distinguish "retention is bad" from "retention is not yet measured" — two very different early-stage situations that a blank NRR cannot tell apart.
Interpretation guidance
When 'not_tracked', the absence of NRR/GRR is expected (not a red flag); the board's ask is to start tracking. When 'tracked', read the NRR/GRR values normally.
Source
imboard Editorial
Related KPIs
customers.net_revenue_retention customers.gross_revenue_retention

Top Customer Concentration

customers.top_customer_concentration
percentage (%) Editorial
Description
Share of total ARR contributed by the top N customers — typically top 5 or top 10. Measures revenue concentration risk: a high concentration means losing one big customer would materially dent ARR. The board reads this alongside `arr_at_risk` and the customer list to gauge how much of the company's future is tied to a handful of accounts. Common pitfall: hiding parent-account aggregation — if three "customers" are subsidiaries of the same parent, true concentration is higher than the count-by-logo view shows; settle parent-rollup rules and document them in `customer_definition_note`.
Formula
top_customer_concentration = Σ(ARR of top N customers) ÷ total ARR. N is typically 5 or 10 — fix it for the company and hold it constant. Parent-account roll-up rules must be explicit and stable across periods.
Why it matters
Quantifies "single-account risk." For early-stage companies, high concentration is expected and not necessarily a problem; for growth-stage companies, it constrains valuation multiples and is a frequent due-diligence flag in fundraising and M&A.
Interpretation guidance
No single citation-grade benchmark exists; commonly-cited industry folk-wisdom (not citation-grade) holds that top-10 customer concentration above 50% is a yellow flag and any single customer above 20% of ARR is a serious risk that fundraising and acquirer diligence will flag. Trend it across quarters — falling concentration is healthy customer-base diversification; rising concentration deserves a board note even if the absolute number is acceptable today.
Source
imboard Editorial
Related KPIs
customers.arr_at_risk customers.percent_arr_at_risk customers.total_customers customers.churn_risks sales.arr

Total Customers

customers.total_customers
number Editorial
Description
Count of active paying customer logos at the end of the period. "Active" means the customer has a live paid subscription or contract on the reporting date — not trial, not cancelled, not zero-revenue. The board reads this alongside ARR to triangulate whether growth is logo-driven (more customers at similar ACV) or expansion-driven (existing customers paying more). Common pitfall: definitions of "customer" drift over time as the company sells to subsidiaries, parent accounts, or self-serve users — settle the counting unit (parent vs. account vs. seat) and document it in `customer_definition_note` so cross-period comparisons stay honest.
Formula
Count of customer logos with an active paid subscription/contract on the reporting date. Excludes trials, paused accounts, and $0 contracts. Counting unit (parent vs. account vs. seat) must be documented and held constant period-over-period.
Why it matters
The denominator beneath every retention, churn, and concentration metric — and the simplest read on whether the company is winning new logos at the headline level. Boards use it to disambiguate logo-led vs. ARR-led growth.
Interpretation guidance
Net change = new logos − churned logos; pair with `customers_churned` and ARR delta. Stage-typical absolute counts vary so widely (B2B enterprise vs. SMB SaaS) that an absolute count is industry folk-wisdom, not citation-grade — compare instead to the company's own trailing 4-quarter trend. A definitional change to "customer" must be flagged on the chart, not absorbed silently.
Source
imboard Editorial
Related KPIs
customers.customers_churned customers.logo_retention_rate customers.logo_churn_rate customers.top_customer_concentration sales.arr

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Finance 67 KPIs

Actual Burn Rate (Past Period)

finance.burn_rate_actual
currency (/month) Editorial
Description
The single past-period observed burn — gross and net — that anchors the forecast-scenario matrix. The "we just lived through this" baseline against which conservative / most-likely / best-case forecasts are projected. Differs from `finance.gross_burn_rate` and `finance.net_burn_rate` in being explicitly a point-in-time historical anchor with both components paired in one object, rather than the standalone monthly KPI values. Common pitfall: anchoring forecasts off a single month with a known one-off (large bill, prepayment received) bakes a distortion into all scenarios — pick a representative period or document the adjustment.
Formula
Paired historical observation: `{ gross: finance.gross_burn_rate_for_anchor_period, net: finance.net_burn_rate_for_anchor_period }`. The anchor period is typically the most recently closed reporting period.
Why it matters
Anchors the credibility of the forecast matrix — scenarios that diverge wildly from the actual baseline without explicit drivers are not credible. Boards typically interrogate any scenario whose burn differs from actual by more than ~20% without a named driver.
Interpretation guidance
Cross-check the anchor against the 3-month trailing average; if they differ materially, the anchor period was atypical and the forecast may be unrealistic. Pair every anchor with a one-line note on what was one-off and how the forecast normalizes for it.
Source
imboard Editorial
Related KPIs
finance.gross_burn_rate finance.net_burn_rate finance.burn_rate_scenarios finance.forecast_notes

Bank Accounts

finance.bank_accounts_list
text Editorial
Description
FX-aware enumeration of the company's bank, brokerage, and money-market accounts — each with bank name, account type, restricted flag, currency, balance, as-of date, and notes. The underlying data source for `finance.total_cash_in_bank`, `finance.total_restricted_cash`, `finance.total_unrestricted_cash`, and the FX conversion that turns multi-currency holdings into a single reporting-currency number. Common pitfall: a single forgotten account (often a legacy operational account or a money-market sweep) silently misstates the total — boards should ask for a checklist reconciliation against the prior board pack each cycle. Best practice: include account-number last-4 (not full numbers, for security) and the FX rate used per non-functional-currency account.
Formula
No calculation — structured list. Sum of balances (converted via per-row FX rate) equals `finance.total_cash_in_bank`; rows flagged `restricted: true` sum to `finance.total_restricted_cash`.
Why it matters
The auditable line-item basis for every aggregate cash KPI on the board pack. Without it, the headline numbers cannot be reconciled and a missing account cannot be detected.
Interpretation guidance
Watch for account churn (new accounts opened mid-period without commentary, accounts dropped without explanation) and FX-rate staleness (rates older than the as-of date). A growing number of accounts at growth stage is normal (multiple currencies, multiple banks); a sudden change in account count warrants a footnote.
Source
imboard Editorial
Related KPIs
finance.total_cash_in_bank finance.total_restricted_cash finance.total_unrestricted_cash finance.operationally_available_cash

Burn Rate Scenarios

finance.burn_rate_scenarios
text Editorial
Description
Forecast burn-rate matrix across three scenarios — conservative (defensive cost plan, slow revenue), mostLikely (current best-estimate), bestCase (aggressive investment with strong revenue) — with gross + net burn for each. Bound to the ScenarioBurnRateMatrix widget alongside the historical `finance.burn_rate_actual` anchor. The board reads this to understand what range of cash trajectories the company is planning for and which one management has chosen as the base case. Common pitfall: the three scenarios cluster tightly (all within ±10% of each other) — that's not three scenarios, it's one scenario with rounding error. Real scenarios should reflect meaningfully different operating decisions and produce visibly different runways.
Formula
Structured matrix: `{ conservative: { gross, net }, mostLikely: { gross, net }, bestCase: { gross, net } }`. Each scenario's implied runway = unrestricted_cash / scenario.net.
Why it matters
Forces explicit scenario thinking and surfaces the risk-adjusted range of outcomes the board should plan for — without this, the single-number forecast invites false confidence.
Interpretation guidance
Inspect the spread between scenarios — a conservative net burn within 10–15% of best-case usually means the team has not stress-tested its plan (industry folk-wisdom, not citation-grade). Cross-check `mostLikely` against `finance.burn_rate_actual` (trailing 3 months) — divergence > ±15–20% should be footnoted with named drivers in `finance.forecast_notes`.
Source
imboard Editorial
Related KPIs
finance.burn_rate_actual finance.net_burn_rate finance.gross_burn_rate finance.runway_months finance.forecast_notes finance.assumptions

Cloud / Hosting

finance.cloud_hosting
currency Editorial
Description
Direct cost of cloud infrastructure, hosting, storage, and compute used to deliver the product (AWS, GCP, Azure, CDN). A cost-of-revenue line because it scales with serving customers. For infrastructure-heavy and AI products this is often the largest COGS component.
Formula
Direct cloud / hosting / compute cost of delivering the product for the period.
Why it matters
A primary driver of gross margin, especially for infrastructure- and AI-heavy products.
Interpretation guidance
Track as a percentage of total revenue; a rising ratio signals margin pressure or unoptimized infrastructure.
Source
imboard Editorial
Related KPIs
finance.total_cogs finance.gross_margin_pct

Contractors / Outsourcing

finance.contractors_outsourcing
currency Editorial
Description
Cost of freelancers, dev shops, outsourced QA, and temporary engineering help for the period. Often used to flex capacity without permanent headcount.
Formula
External / contract R&D labor cost for the period.
Why it matters
A flexible-capacity lever; sustained high spend can signal an under-hired team.
Interpretation guidance
Persistent large contractor spend often warrants a build-vs-hire discussion at the board.
Source
imboard Editorial
Related KPIs
finance.total_rnd finance.rd_payroll

CS Payroll

finance.cs_payroll
currency Editorial
Description
Fully-loaded compensation for customer success managers, onboarding, and account management (where not sales-owned) for the period. The retention/expansion-oriented people cost, distinct from cost-of-revenue support.
Formula
Fully-loaded customer-success personnel cost for the period.
Why it matters
The investment behind retention and expansion; pairs with NRR.
Interpretation guidance
Read against NRR/GRR — CS spend that is not moving retention warrants scrutiny.
Source
imboard Editorial
Related KPIs
finance.total_cs customers.net_revenue_retention

CS Tools / Software

finance.cs_tools_software
currency Editorial
Description
Cost of customer-success tooling for the period — customer-health platforms, support/ticketing, chat, and onboarding tools.
Formula
Customer-success tools / software subscriptions for the period.
Why it matters
The tooling overhead of the retention motion.
Interpretation guidance
Scales with customer count; watch for overlap with support tooling in COGS.
Source
imboard Editorial
Related KPIs
finance.total_cs

Current Asset Adjustments

finance.current_asset_adjustments
currency Editorial
Description
Signed cash effect of period-over-period changes in current assets — accounts receivable, prepaid expenses, deposits, and other short-term assets. Positive when assets are converting back to cash (AR collections, prepaid expenses being consumed); negative when assets are growing and absorbing cash (AR balance up, new prepayments made). Half of the `finance.net_working_capital_adjustment` rollup. Common pitfall: a one-off enterprise prepayment to a vendor (e.g. 12-month infra commit) shows up here as a large negative without the P&L showing the cost yet — flag it explicitly so the board does not read deterioration where there is none.
Formula
-(Δ accounts_receivable + Δ prepaid_expenses + Δ other_current_assets) for the period. The negative sign converts the balance-sheet direction (asset increase = cash decrease) into a signed cash adjustment.
Why it matters
Surfaces the cash impact of growing receivables and prepayments separately from operating spend — important when DSO is moving or large prepaid commitments are taken.
Interpretation guidance
A sustained negative trend usually means AR is growing faster than collections (DSO lengthening) — pair with sales-side bookings and ARR to confirm. Industry folk-wisdom (not citation-grade): the cash drag from a growing AR book typically peaks late in the year when annual contracts billed in Q4 land as Q1 receipts.
Source
imboard Editorial
Related KPIs
finance.current_liability_adjustments finance.net_working_capital_adjustment finance.operationally_available_cash finance.working_capital_adjustments_list

Current Liability Adjustments

finance.current_liability_adjustments
currency Editorial
Description
Signed cash effect of period-over-period changes in current liabilities — accounts payable, accrued payroll/taxes/bonuses, deferred revenue from customer prepayments, and other short-term liabilities. Positive when liabilities grow and absorb less cash than the matched expense suggests (e.g. AP balance growing means vendor cash payments lag); negative when liabilities are being paid down faster than they accrue. Deferred revenue is the most powerful component in SaaS — a large annual prepayment received increases deferred revenue and supplies cash now against expense recognized later. Common pitfall: a board reading this as straight cash improvement misses that deferred revenue must still be earned out, and a stretched AP balance signals supplier strain. Best practice: footnote large components (deferred revenue, accrued bonus) separately.
Formula
+(Δ accounts_payable + Δ accrued_liabilities + Δ deferred_revenue + Δ other_current_liabilities) for the period. Liability increase = cash supplied, so positive sign.
Why it matters
Captures the cash benefit (or drag) of working-capital liability movements — deferred revenue inflows in particular can mask underlying cash burn at SaaS companies that book annual upfront.
Interpretation guidance
A sustained positive trend driven by AP growth (not deferred revenue) is a yellow flag — it means the company is funding itself by lengthening supplier payment cycles. A surge driven by deferred revenue (annual contract closes) is a one-time cash benefit that doesn't recur. Separate the components in commentary.
Source
imboard Editorial
Related KPIs
finance.current_asset_adjustments finance.net_working_capital_adjustment finance.operationally_available_cash finance.working_capital_adjustments_list

Customer Support & Delivery

finance.customer_support_delivery
currency Editorial
Description
Direct cost of supporting and serving customers that is part of cost-of-revenue (front-line support, delivery operations tied to the product). Distinct from the Customer Success OpEx section, which covers retention/expansion-oriented account management.
Formula
Direct cost of serving/supporting customers included in cost of revenue.
Why it matters
Captures the service cost embedded in gross margin, separate from go-to-market CS.
Interpretation guidance
If this scales linearly with customers, it caps gross margin; automation should bend the curve over time.
Source
imboard Editorial
Related KPIs
finance.total_cogs

Depreciation & Amortization

finance.depreciation_amortization
currency Editorial
Description
Non-cash expense allocating the cost of capitalized assets (equipment, capitalized software, intangibles) over their useful life for the period. Below the EBITDA line precisely because EBITDA excludes it; usually small for early-stage software companies.
Formula
Non-cash depreciation + amortization expense for the period.
Why it matters
Bridges EBITDA to net income; non-cash, so it affects accounting profit but not burn.
Interpretation guidance
Typically immaterial for asset-light software companies; flag if it becomes large (capitalized software/assets).
Source
imboard Editorial
Related KPIs
finance.ebitda finance.net_income

EBITDA

finance.ebitda
currency Editorial
Description
Earnings before interest, taxes, depreciation, and amortization for the period — gross profit minus total operating expense. The core operating result for a startup P&L: the clean view of operating profit or loss before non-operating and non-cash effects.
Formula
ebitda = gross_profit − total_opex.
Why it matters
The headline operating-result line the board reads for profitability/loss before financing and accounting effects.
Interpretation guidance
Trend toward break-even is the key read; pair with burn and runway for the cash picture (EBITDA is accrual, not cash).
Source
imboard Editorial
Related KPIs
finance.gross_profit finance.total_opex finance.net_burn_rate finance.runway_months

Events / Conferences

finance.events_conferences
currency Editorial
Description
Cost of conferences, booths, sponsorships, and event-linked travel for the period. Often lumpy quarter to quarter around event calendars.
Formula
Event / conference / sponsorship spend for the period.
Why it matters
A material, lumpy GTM line worth isolating so it does not distort the marketing trend.
Interpretation guidance
Expect seasonality around major events; flag the timing when comparing periods.
Source
imboard Editorial
Related KPIs
finance.total_sm

Financial Assumptions

finance.assumptions
text Editorial
Description
Narrative listing of the key inputs the forecast rests on — growth-rate assumptions, churn assumptions, hiring plan, FX rates, expected timing of large bookings, planned price changes, capitalized-vs-expensed R&D treatment, etc. Without this field, the board cannot tell whether a forecast change reflects a real-world update or a quietly changed assumption. Common pitfall: assumptions are written once at planning and never updated when the underlying reality shifts — track explicitly which assumption changed each quarter and why. Best practice (per "Venture Deals" by Feld & Mendelson, and standard board-pack guidance): every material variance vs. forecast should be traceable to either an executed plan or a changed assumption.
Formula
No calculation — free-text narrative. Convention: enumerate top 5–8 assumptions with the value used and the source (plan, observed run-rate, investor letter, board-approved hiring cap).
Why it matters
Makes the forecast auditable across periods. Boards cannot challenge or endorse a number whose assumptions are invisible — and quietly changing assumptions is the single most common source of forecast drift.
Interpretation guidance
Flag whenever an assumption changes vs. prior period and note the reason. If the list is missing or stale (unchanged for >2 reporting cycles while reality has clearly moved), treat as a yellow flag on financial-process maturity. No published threshold for completeness — coverage is judged by whether a board member can recompute the forecast from the listed assumptions.
Source
imboard Editorial
Related KPIs
finance.forecast_notes finance.risk_factors finance.burn_rate_scenarios

Financial Risk Factors

finance.risk_factors
text Editorial
Description
Material risks that could break the forecast or the cash position — customer concentration, contract renewal risk in the next 2 quarters, debt-covenant proximity, FX exposure on multi-currency revenue/cost mix, payment-processor concentration, audit/tax adjustments under review, regulatory changes affecting revenue recognition. Distinct from `risk_factors` at the operations level — this is explicitly financial. Common pitfall: this field becomes boilerplate ("market risk, execution risk") instead of naming the specific risks the board can act on this quarter. Best practice (per the standard board-pack guidance reflected in NVCA Model Investor Rights Agreement information-rights conventions): name the top 3–5 risks with a probability/impact note and a current mitigation status.
Formula
No calculation — narrative. Convention: 3–5 risks, each with a one-line statement, qualitative likelihood, qualitative impact, and current mitigation action.
Why it matters
Gives the board a defensible answer to "what should worry us next quarter" — and creates an audit trail of which risks management saw coming vs. which surprised them. Frequently the highest-signal part of the cash dashboard at growth stage.
Interpretation guidance
Track risks across periods — risks that disappear without explicit resolution are usually still active, just not being managed. Boards should treat a thin or unchanged list (no movement quarter-over-quarter on multiple periods) as a yellow flag on financial-controls maturity.
Source
imboard Editorial
Related KPIs
finance.assumptions finance.forecast_notes finance.burn_rate_scenarios fundraising.risk_factors

Forecast Commentary

finance.forecast_notes
text Editorial
Description
Executive narrative on what the latest forecast says and how it has changed since prior reporting — which scenarios were considered, which was picked as "most likely" and why, what changed since last quarter, and what would push the forecast into a different scenario. Pairs with `finance.burn_rate_scenarios` (the numeric scenarios) to provide the qualitative "why" beside the quantitative "what". Common pitfall: this becomes a restatement of the numbers rather than commentary — every paragraph should add interpretation the numbers do not by themselves convey (drivers, decisions taken, decisions deferred).
Formula
No calculation — narrative commentary. Convention: cover (1) selected scenario and rationale, (2) deltas vs. prior forecast with reasons, (3) trigger conditions that would move the forecast.
Why it matters
Gives the board the interpretation layer that raw scenario numbers lack — without it, the burn-rate-scenarios table is data without meaning. Disciplined commentary also creates a record of management's rationale that can be re-examined when reality plays out.
Interpretation guidance
Compare commentary across periods — if the rationale shifts without the underlying numbers shifting, the team is rationalizing rather than analyzing. If numbers shift without the rationale acknowledging it, controls maturity is the concern. Length is not a quality signal; concrete drivers and named triggers are.
Source
imboard Editorial
Related KPIs
finance.assumptions finance.burn_rate_scenarios finance.burn_rate_actual finance.risk_factors

FX Gain / Loss

finance.fx_gain_loss
currency Editorial
Description
Foreign-exchange gain (positive) or loss (negative) for the period from revaluing non-functional-currency balances and transactions. A SIGNED line; relevant for companies holding cash or transacting in multiple currencies.
Formula
Signed FX gain (positive) / loss (negative) for the period.
Why it matters
Can swing net income for multi-currency companies even when operations are stable.
Interpretation guidance
Enter signed: gain positive, loss negative. Large swings should be footnoted as FX, not operating, effects.
Source
imboard Editorial
Related KPIs
finance.net_income

G&A Payroll

finance.ga_payroll
currency Editorial
Description
Fully-loaded compensation for finance, HR, operations, admin, and executive/admin allocation for the period. The people cost of running the company.
Formula
Fully-loaded G&A personnel cost for the period.
Why it matters
Overhead that should grow slower than revenue as the company scales.
Interpretation guidance
Track as a percentage of revenue; the ratio should trend down with scale.
Source
imboard Editorial
Related KPIs
finance.total_ga

Gross Burn Rate

finance.gross_burn_rate
currency (/month) Editorial
Description
Average monthly cash outflow before any inflows are netted off — essentially the company's monthly cost base in cash terms. Tracked alongside net burn because net burn alone can mask a structural problem when revenue is masking high cost. The board reads gross burn to understand the absolute cost commitment (mostly payroll, infra, COGS, sales spend) regardless of revenue mix. Common pitfall: founders often optimize the net burn narrative ("we cut burn 30%") via a one-time inflow without addressing the gross-burn cost base — the next quarter without that inflow re-exposes the underlying spend. Always present gross and net side-by-side.
Formula
gross_burn_rate = total_operational_outflow / months_in_period. Same denominator and averaging convention as net burn (3-month trailing average is standard). Always greater than or equal to net burn.
Why it matters
Strips revenue volatility from the survival picture — shows the cost commitment the company must support each month regardless of bookings outcomes. A widening gap between gross and net burn that depends on a single deal or one-off inflow is a fragility signal.
Interpretation guidance
Compare gross-burn composition (payroll, infra, GTM, COGS) to revenue mix; sustained gross burn growing faster than ARR is a leading deterioration signal even when net burn looks flat. No single published gross-burn threshold exists — interpret relative to ARR and revenue per FTE (`hr.arr_per_fte`). Practitioner consensus (industry folk-wisdom, not citation-grade): payroll typically accounts for 65–80% of gross burn in venture-backed SaaS.
Source
imboard Editorial
Related KPIs
finance.net_burn_rate finance.burn_rate_actual finance.total_operational_outflow finance.runway_months hr.arr_per_fte sales.arr

Gross Margin %

finance.gross_margin_pct
percentage Editorial
Description
Gross profit as a percentage of total revenue for the period — the headline quality-of-revenue and delivery-efficiency metric. Expressed 0–100. The P&L-statement margin computed from the revenue/COGS split; complements the GTM-level `sales.gross_margin`.
Formula
gross_margin_pct = (gross_profit / total_revenue) × 100.
Why it matters
Signals revenue quality and how much each revenue dollar contributes to covering OpEx.
Interpretation guidance
Read the trend and the mix behind it: a services- or usage-heavy period typically lowers blended margin. Benchmark against the company’s own plan; external SaaS benchmarks vary by model (pull a current source rather than assuming a fixed band).
Source
imboard Editorial
Related KPIs
finance.gross_profit finance.total_revenue sales.gross_margin

Gross Profit

finance.gross_profit
currency Editorial
Description
Total revenue minus total cost of revenue for the period — the profit left to fund operating expenses. The dollar complement to gross margin and the starting point for the operating-result section.
Formula
gross_profit = total_revenue − total_cogs.
Why it matters
The dollars available to cover OpEx — the bridge from revenue to operating result.
Interpretation guidance
Growing gross profit faster than OpEx is the path to EBITDA; read with `finance.gross_margin_pct` for quality.
Source
imboard Editorial
Related KPIs
finance.total_revenue finance.total_cogs finance.gross_margin_pct

Insurance / Compliance

finance.insurance_compliance
currency Editorial
Description
Cost of D&O and cyber insurance, SOC 2, and regulatory compliance for the period. Rises with company size, customer requirements, and financing stage.
Formula
Insurance & compliance cost for the period.
Why it matters
A step-fixed cost driven by stage, customer requirements, and risk posture.
Interpretation guidance
Expect step-ups at financings and enterprise-customer thresholds (e.g. SOC 2).
Source
imboard Editorial
Related KPIs
finance.total_ga

Interest Income / Expense

finance.interest_income_expense
currency Editorial
Description
Net interest for the period as a SIGNED line: interest earned on cash/deposits (positive) net of interest paid on loans, venture debt, or other financing (negative). For cash-rich post-raise companies this is often net positive income.
Formula
Signed net interest: interest income − interest expense for the period (positive = net income).
Why it matters
Surfaces financing effects below the operating line; meaningful for companies with venture debt or large cash balances.
Interpretation guidance
Enter signed: net interest income positive, net interest expense negative. Material venture-debt interest should be footnoted.
Source
imboard Editorial
Related KPIs
finance.net_income finance.total_cash_in_bank

Legal, Accounting & Professional Services

finance.legal_accounting_professional
currency Editorial
Description
Cost of legal, accounting, audit, tax, fractional CFO, and outside consultants for the period. Often spikes around financings, audits, and major contracts.
Formula
Legal / accounting / professional-services fees for the period.
Why it matters
A lumpy overhead line; spikes usually map to financing or compliance events.
Interpretation guidance
Footnote one-off drivers (a raise, an audit) so the board reads the trend, not the spike.
Source
imboard Editorial
Related KPIs
finance.total_ga

Marketing Payroll

finance.marketing_payroll
currency Editorial
Description
Fully-loaded compensation for marketing leadership, demand generation, content, and growth for the period.
Formula
Fully-loaded marketing personnel cost for the period.
Why it matters
The people cost of demand generation, distinct from paid media spend.
Interpretation guidance
Read alongside paid marketing to understand the people-vs-media split of GTM spend.
Source
imboard Editorial
Related KPIs
finance.total_sm finance.paid_marketing

Net Burn Rate

finance.net_burn_rate
currency (/month) Editorial
Description
Average monthly net cash outflow over the reporting period — total cash spent minus total cash collected, divided by the number of months in the period. The headline survival number for venture-backed startups: it pairs with `finance.total_cash_in_bank` to produce runway, and pairs with revenue growth to produce the Bessemer "burn multiple". Common pitfall: net burn is volatile — large quarterly bills (annual SaaS renewals, employer-tax true-ups), enterprise prepayments, and FX swings can mask the underlying trend. Smoothing over a trailing 3-month average is standard board practice. Equally important: do not silently include one-off cash events (acquisitions, settlements, large prepayments received) without flagging them — boards prefer a "core burn" and "headline burn" pair when the period is noisy.
Formula
net_burn_rate = (total_operational_outflow − total_operational_inflow) / months_in_period. Most boards average over a trailing 3 months to dampen lumpy items; flag the methodology explicitly. When net burn is negative, the company is net-cash-generative for the period.
Why it matters
Single most-watched metric below revenue at venture-backed companies — drives runway, valuation reads (via the burn multiple), and the calculus on when to fundraise vs. cut.
Interpretation guidance
Compare against the company's own forecast first (`finance.burn_rate_scenarios`); deviation > ±15–20% from the most-likely scenario typically warrants a board note (industry folk-wisdom, not citation-grade). Stage-level industry context: per the SaaS Capital 2025 Spending Benchmarks for Private B2B SaaS Companies, total median spend runs ~95% of ARR for bootstrapped and ~107% of ARR for equity-backed private SaaS, with 55% of equity-backed companies operating at a loss. For burn-multiple framing (net burn ÷ net new ARR), Series A medians sit near 1.2x and growth-stage companies above $25M ARR target ~1.4x with best performers below 1.0x (per cited 2025 industry analyses; pull the live edition to confirm).
Source
imboard Editorial
Related KPIs
finance.gross_burn_rate finance.runway_months finance.total_cash_in_bank finance.burn_rate_actual finance.burn_rate_scenarios finance.total_operational_inflow finance.total_operational_outflow sales.arr

Net Income / Loss

finance.net_income
currency Editorial
Description
The accounting bottom line for the period — EBITDA less depreciation & amortization and tax, plus the signed interest and FX lines. The final result of the income statement. Distinct from cash burn (an accrual figure, not a cash-flow measure).
Formula
net_income = ebitda − depreciation_amortization + interest_income_expense − tax + fx_gain_loss (interest and FX are signed inputs).
Why it matters
The statutory bottom line; read alongside burn/runway, since net income is accrual and does not equal cash consumed.
Interpretation guidance
For startups, burn and runway usually matter more than net income — but a widening accrual loss is still a board signal. Reconcile to burn via the working-capital and non-cash lines.
Source
imboard Editorial
Related KPIs
finance.ebitda finance.depreciation_amortization finance.interest_income_expense finance.tax finance.fx_gain_loss finance.net_burn_rate

Net Working Capital Adjustment

finance.net_working_capital_adjustment
currency Editorial
Description
Signed net effect on cash of changes in current assets and current liabilities — receivables coming in (positive), payables going out (negative), prepaid expenses (negative when paid, positive when burned down), and accrued liabilities (positive when accrued, negative when settled). The rollup of `finance.current_asset_adjustments` and `finance.current_liability_adjustments`. Common pitfall: at early stage this is dominated by payroll-cycle noise and is near zero — once the company adds enterprise contracts with annual prepayments or 60-day net terms, this can swing 1–3 months of burn either direction. Becomes material at Series A+; ignored before that.
Formula
net_working_capital_adjustment = current_asset_adjustments + current_liability_adjustments (signed). Positive value means working capital is releasing cash; negative means working capital is consuming cash beyond what the P&L shows.
Why it matters
Bridges the gap between accrual-basis P&L and cash-basis runway. A board reading the P&L alone can miss a working-capital headwind that is materially shortening runway.
Interpretation guidance
Track period-over-period: a multi-period negative trend (working capital absorbing cash) usually means DSO is lengthening or supplier terms are tightening — both warrant a board note. No published threshold exists for "good" magnitude — it scales with revenue and contract mix.
Source
imboard Editorial
Related KPIs
finance.current_asset_adjustments finance.current_liability_adjustments finance.operationally_available_cash finance.working_capital_adjustments_list

Office / Facilities

finance.office_facilities
currency Editorial
Description
Cost of rent, coworking, and office facilities for the period. Smaller for remote-first companies; a fixed commitment where leased.
Formula
Office / facilities / rent cost for the period.
Why it matters
A fixed cost and lease commitment that affects runway flexibility.
Interpretation guidance
Note lease commitments separately from period cost when discussing runway.
Source
imboard Editorial
Related KPIs
finance.total_ga

Operationally Available Cash

finance.operationally_available_cash
currency Editorial
Description
Unrestricted cash adjusted for near-term working-capital effects — i.e. the cash that is actually deployable after accounting for receivables coming in, payables going out, and accrued obligations crystallizing in the next reporting period. More conservative than `finance.total_unrestricted_cash` because it nets out the cash a healthy AR/AP cycle is already promising or claiming. The board reads this as the "real" cash position when working capital is material to the business (typical at Series A+, when AR/AP cycles get sizeable). Common pitfall: at early stage AR is small and AP is mostly payroll/SaaS, so this collapses to unrestricted cash — once enterprise deals or 60-day net terms appear, the gap widens fast.
Formula
finance.total_unrestricted_cash + finance.net_working_capital_adjustment. The working-capital adjustment is signed (positive when AR collection > AP outflow over the horizon, negative otherwise).
Why it matters
Best single-number answer to "how much cash do we really have to deploy this quarter" once working capital is material. Substituted for unrestricted cash in the runway denominator at growth stage.
Interpretation guidance
A large negative gap between unrestricted and operationally-available cash means working-capital headwinds are eating into apparent runway — common when DSO is lengthening. Track the gap quarter-over-quarter; widening signals deteriorating collections or stretched payables. No published industry threshold — interpretation is company- and cycle-specific.
Source
imboard Editorial
Related KPIs
finance.total_unrestricted_cash finance.net_working_capital_adjustment finance.current_asset_adjustments finance.current_liability_adjustments finance.runway_months

Other COGS

finance.other_cogs
currency Editorial
Description
Direct cost-of-revenue items not captured by the named COGS lines — a catch-all kept small by design. If it becomes material it should be split into a named line.
Formula
Direct cost-of-revenue not classified in the named COGS lines.
Why it matters
Keeps total COGS complete without distorting the primary cost categories.
Interpretation guidance
If "Other" grows beyond a few percent of COGS, split it into a named line.
Source
imboard Editorial
Related KPIs
finance.total_cogs

Other CS

finance.other_cs
currency Editorial
Description
Customer-success operating costs not captured by the named CS lines for the period — a catch-all kept small by design.
Formula
CS operating costs not classified in the named CS lines.
Why it matters
Keeps Total Customer Success complete without distorting the primary lines.
Interpretation guidance
If it grows beyond a few percent of CS, split into a named line.
Source
imboard Editorial
Related KPIs
finance.total_cs

Other G&A

finance.other_ga
currency Editorial
Description
G&A operating costs not captured by the named G&A lines for the period — the roll-up home for minor overhead (bank fees, office supplies, small licenses). Kept small by design.
Formula
G&A operating costs not classified in the named G&A lines.
Why it matters
Keeps Total G&A complete and absorbs the many small overhead items.
Interpretation guidance
If it grows beyond a few percent of G&A, split into a named line.
Source
imboard Editorial
Related KPIs
finance.total_ga

Other R&D

finance.other_rnd
currency Editorial
Description
R&D operating costs not captured by the named R&D lines for the period — a catch-all kept small by design.
Formula
R&D operating costs not classified in the named R&D lines.
Why it matters
Keeps Total R&D complete without distorting the primary lines.
Interpretation guidance
If it grows beyond a few percent of R&D, split into a named line.
Source
imboard Editorial
Related KPIs
finance.total_rnd

Other Revenue

finance.other_revenue
currency Editorial
Description
Recognized revenue not captured by the subscription, usage, or services lines — a catch-all for small or unusual revenue items. Kept small by design; if it becomes material it should be split into a named line.
Formula
Recognized revenue not classified as subscription, usage, or services.
Why it matters
Keeps total revenue complete without polluting the primary revenue categories.
Interpretation guidance
If "Other" grows beyond a few percent of total revenue, split it into a named line for the board.
Source
imboard Editorial
Related KPIs
finance.total_revenue

Other S&M

finance.other_sm
currency Editorial
Description
Sales & marketing operating costs not captured by the named S&M lines for the period — a catch-all kept small by design.
Formula
S&M operating costs not classified in the named S&M lines.
Why it matters
Keeps Total S&M complete without distorting the primary lines.
Interpretation guidance
If it grows beyond a few percent of S&M, split into a named line.
Source
imboard Editorial
Related KPIs
finance.total_sm

Paid Marketing

finance.paid_marketing
currency Editorial
Description
Paid demand-generation spend for the period — search, social, performance marketing, and sponsorships. The variable media component of go-to-market.
Formula
Paid media / performance-marketing spend for the period.
Why it matters
A directly-tunable growth lever; the core input to paid CAC.
Interpretation guidance
Judge against pipeline/bookings generated, not in isolation.
Source
imboard Editorial
Related KPIs
finance.total_sm finance.marketing_payroll

Payment / Transaction Costs

finance.payment_transaction_costs
currency Editorial
Description
Direct payment-processing and transaction fees attributable to delivering revenue (card processing, gateway fees, marketplace take rates). A cost-of-revenue line where relevant; omitted or near-zero for invoice-only businesses.
Formula
Payment-processing / transaction fees attributable to revenue for the period.
Why it matters
Directly reduces gross margin on transaction- or consumer-billed revenue.
Interpretation guidance
Expressed as a percentage of processed revenue it should be roughly stable; spikes usually mean a pricing/mix change.
Source
imboard Editorial
Related KPIs
finance.total_cogs

Product / Design Payroll

finance.product_design_payroll
currency Editorial
Description
Compensation for product managers and designers for the period. Can be folded into R&D payroll at smaller companies; kept separate where product/design is a distinct cost center.
Formula
Fully-loaded product + design personnel cost for the period.
Why it matters
Separates product/design investment from pure engineering for a clearer R&D mix.
Interpretation guidance
Read alongside R&D payroll; a rising ratio signals heavier product investment.
Source
imboard Editorial
Related KPIs
finance.total_rnd finance.rd_payroll

R&D Payroll

finance.rd_payroll
currency Editorial
Description
Fully-loaded compensation for engineering, data, QA, DevOps, and technical leadership for the period (salary, employer taxes, benefits). The largest R&D cost for most software companies.
Formula
Fully-loaded R&D personnel cost for the period.
Why it matters
The dominant input to R&D spend and a primary driver of total burn.
Interpretation guidance
Track against headcount plan; step-changes usually reflect hiring or comp true-ups.
Source
imboard Editorial
Related KPIs
finance.total_rnd hr.total_headcount

R&D Tools / Software

finance.rd_tools_software
currency Editorial
Description
Cost of engineering tooling and platforms for the period (source control, CI/CD, testing, observability, developer platforms). Operating expense — distinct from cloud/hosting COGS that serves customer traffic.
Formula
Engineering tools / software subscriptions for the period.
Why it matters
Scales with team size; a useful efficiency read per engineer.
Interpretation guidance
Do not confuse with cloud/hosting COGS — this is internal developer tooling, not delivery infrastructure.
Source
imboard Editorial
Related KPIs
finance.total_rnd finance.cloud_hosting

Recruiting

finance.recruiting
currency Editorial
Description
Cost of agencies, job boards, and referral bonuses for the period. Scales with the pace of hiring and is lumpy around growth pushes.
Formula
Recruiting / agency / referral cost for the period.
Why it matters
A leading indicator of headcount growth and future payroll.
Interpretation guidance
Expect it to lead payroll increases; spikes precede hiring waves.
Source
imboard Editorial
Related KPIs
finance.total_ga hr.total_headcount

Restricted Cash

finance.total_restricted_cash
currency Editorial
Description
Cash on the balance sheet that is not available for general operating use because it is contractually pledged or held for a specific purpose — typical examples include landlord lease-deposit escrows, customer-funds collateral, security deposits backing letters of credit, payment-processor reserves, and debt-covenant minimum-balance requirements. Per IFRS and US GAAP balance-sheet presentation, restricted cash must be disclosed separately from unrestricted cash; the board should treat this number as removed from runway. Common pitfall: payment-processor "reserve" balances and large customer-deposit floats are often missed when reporting unrestricted cash, inflating apparent runway.
Formula
Sum of bank-account balances flagged `restricted: true` in `finance.bank_accounts_list`, plus any restricted balances held in non-bank vehicles (escrow agents, payment-processor reserve accounts).
Why it matters
Excluded from operationally available cash and from the runway calculation — reporting it inside total cash without flagging the restriction overstates runway and can mask a covenant or liquidity issue.
Interpretation guidance
A non-trivial restricted balance (say, >5% of total cash — industry folk-wisdom, not citation-grade) usually warrants a footnote on the source of the restriction and any release schedule. Watch for restricted cash that grows faster than the corresponding operating activity (e.g. payment-processor reserves growing faster than GMV) — that often signals a tightening processor relationship.
Source
imboard Editorial
Related KPIs
finance.total_cash_in_bank finance.total_unrestricted_cash finance.operationally_available_cash finance.bank_accounts_list

Runway (Months)

finance.runway_months
number (months) Industry-backed
Description
Estimated number of months the company can operate at the current net burn before unrestricted cash reaches zero, holding everything else constant. The single most consequential survival input for venture-backed companies — it sets the urgency of every fundraising, hiring, and cost decision. Common pitfall: runway is often quoted off `finance.total_cash_in_bank` and a single-month spot-burn instead of operationally-available cash and a 3-month-trailing burn — the result is a runway that looks 2–4 months longer than it actually is when working capital tightens. Boards should ask which cash and which burn went into the calculation.
Formula
runway_months = cash_basis / finance.net_burn_rate, where cash_basis is finance.operationally_available_cash when working capital is material (Series A+), and finance.total_unrestricted_cash otherwise (early stage, when AR/AP is immaterial and the two converge). Never use max() of the two — that discards the more conservative number exactly when working capital is a headwind, the very pitfall this KPI warns about. When net burn is negative (cash-flow positive), runway is unbounded — render as ∞ rather than negative. Most boards use a 3-month-trailing-average net burn for the denominator to dampen single-month noise.
Why it matters
Drives the timing of every fundraise, hire, and budget cut — and is the number investors lead with in diligence. Crossing under stage-typical thresholds usually triggers a board-level cost or fundraising conversation.
Interpretation guidance
Stage-typical industry context (per the 2024 KeyBanc Capital Markets & Sapphire Ventures SaaS Survey §runway / month-of-cash discussion): private SaaS companies with $10M–$50M year-end ARR median ~25 months of cash; those <$10M or >$50M ARR median ~18 months. Practitioner heuristics (industry folk-wisdom, not citation-grade): under 6 months is critical (immediate fundraise or cost action); 12–18 months is healthy for active fundraising; 24+ months gives optionality. Recalculate any time burn changes materially or a tranche closes.
Related KPIs
finance.total_cash_in_bank finance.total_unrestricted_cash finance.operationally_available_cash finance.net_burn_rate finance.burn_rate_scenarios fundraising.target_raise

S&M Tools / Software

finance.sm_tools_software
currency Editorial
Description
Cost of go-to-market tooling for the period — CRM, enrichment, outbound, attribution, and sales-engagement platforms.
Formula
Sales & marketing tools / software subscriptions for the period.
Why it matters
The tooling overhead of the GTM motion; scales with team size.
Interpretation guidance
Watch per-rep tooling cost; tool sprawl is a common, quiet cost creep.
Source
imboard Editorial
Related KPIs
finance.total_sm

Sales Commissions

finance.sales_commissions
currency Editorial
Description
Variable sales compensation earned on bookings for the period. Separated from sales payroll because it scales with deals closed and explains period-to-period variance differently.
Formula
Variable commission expense recognized for the period.
Why it matters
Ties go-to-market cost to bookings; a key input to CAC.
Interpretation guidance
Should move with bookings; a spike without bookings growth warrants a note.
Source
imboard Editorial
Related KPIs
finance.total_sm finance.sales_payroll

Sales Payroll

finance.sales_payroll
currency Editorial
Description
Fully-loaded base compensation for account executives, SDRs, and sales leadership for the period. Excludes commissions, which are tracked separately because they scale with bookings.
Formula
Fully-loaded sales base personnel cost for the period (excl. commissions).
Why it matters
The fixed component of go-to-market cost; pairs with commissions for full sales cost.
Interpretation guidance
Read with bookings to gauge sales efficiency; keep separate from variable commissions.
Source
imboard Editorial
Related KPIs
finance.total_sm finance.sales_commissions

Services / Implementation Revenue

finance.services_revenue
currency Editorial
Description
Recognized non-recurring revenue from implementation, onboarding, or professional services for the period. Kept separate from recurring revenue because it is lower-margin and does not compound — a services-heavy quarter can grow total revenue while ARR stays flat.
Formula
Recognized professional-services / implementation revenue for the period.
Why it matters
Separating services keeps recurring revenue clean and exposes margin dilution from delivery-heavy periods.
Interpretation guidance
A rising services share of total revenue often pressures blended gross margin — read against `finance.gross_margin_pct`.
Source
imboard Editorial
Related KPIs
finance.total_revenue finance.services_delivery_costs

Services Delivery Costs

finance.services_delivery_costs
currency Editorial
Description
Direct cost of delivering implementation and professional services — the cost paired with services/implementation revenue. Tracking it against `finance.services_revenue` reveals whether services are run at, above, or below cost.
Formula
Direct cost of delivering implementation / professional services for the period.
Why it matters
Pairs with services revenue to show services margin — often a board question.
Interpretation guidance
Compare to `finance.services_revenue`: services run below cost dilute blended gross margin and should be flagged.
Source
imboard Editorial
Related KPIs
finance.total_cogs finance.services_revenue

Software & IT

finance.software_it
currency Editorial
Description
Cost of internal software, IT, security, devices, and admin tooling for the period (company-wide SaaS not specific to R&D or GTM).
Formula
Company-wide software / IT / security cost for the period.
Why it matters
A quiet cost-creep area as headcount and tool sprawl grow.
Interpretation guidance
Track per-employee software cost; rationalize overlapping subscriptions.
Source
imboard Editorial
Related KPIs
finance.total_ga

Subscription Revenue

finance.subscription_revenue
currency Editorial
Description
Recognized recurring software revenue for the period — the recurring subscription fees earned under contract, recognized on an accrual basis over the service period. The core revenue line for a SaaS P&L; kept separate from usage and services so the board can read the recurring-vs-non-recurring mix. Distinct from ARR (a forward run-rate) and from cash collected (a financing-timing view).
Formula
Recognized recurring subscription revenue for the period (accrual basis), excluding usage, services, and one-time fees.
Why it matters
Isolates durable recurring revenue — the basis of SaaS quality-of-revenue and gross-margin reads.
Interpretation guidance
Read alongside `sales.arr`: recognized subscription revenue trails ARR and the gap reflects timing and mid-period changes. Persistent divergence warrants a note.
Source
imboard Editorial
Related KPIs
finance.total_revenue sales.arr sales.total_revenue

Tax

finance.tax
currency Editorial
Description
Corporate income tax, withholding tax, or other tax expense for the period. Often minimal for loss-making startups, but can be non-trivial with multi-jurisdiction operations or specific tax regimes.
Formula
Tax expense for the period (subtracted in net income).
Why it matters
Completes the path to net income; can surprise multi-entity companies even while loss-making.
Interpretation guidance
Usually small at a loss-making startup; flag if multi-jurisdiction operations make it material.
Source
imboard Editorial
Related KPIs
finance.net_income

Third-Party / API / Data Costs

finance.third_party_data
currency Editorial
Description
Direct cost of external APIs, data providers, enrichment, and model/LLM inference consumed to deliver the product. Broken out from cloud/hosting because for AI products these costs can move gross margin materially and scale with usage rather than headcount.
Formula
Direct external API / data / model-inference cost of delivery for the period.
Why it matters
For AI-native products this can be the swing factor in gross margin and deserves its own board line.
Interpretation guidance
Watch the ratio to usage revenue and total revenue; per-unit inference cost trends matter more than the absolute.
Source
imboard Editorial
Related KPIs
finance.total_cogs finance.usage_revenue finance.gross_margin_pct

Total Cash in Bank

finance.total_cash_in_bank
currency Editorial
Description
Sum of all bank account balances at the reporting cut-off, expressed in a single reporting currency after FX conversion. This is the gross top-of-house cash number — it does not net out restrictions, near-term liabilities, or commitments. The board reads this as the absolute denominator for runway and as a checksum against the cap table (capital raised − cumulative net burn ≈ cash). Common pitfall: founders sometimes report a USD figure that silently includes ILS/EUR accounts at stale FX rates — always reconcile against the bank-accounts list (per FX-aware MultiCurrencyAccountList) and tag the rate date.
Formula
Sum of period-end balances across all bank accounts, converted to the board reporting currency at the cut-off date FX rate. See `finance.bank_accounts_list` for the underlying line items.
Why it matters
The denominator of runway and the single most important survival input — every other cash KPI is read in proportion to this number. Also the basic cap-table sanity check: capital raised minus cumulative net burn should reconcile to total cash within working-capital noise.
Interpretation guidance
Read in conjunction with `finance.total_restricted_cash` and `finance.net_burn_rate` to derive operationally available runway. A drop materially larger than net burn for the period signals an unreported outflow (deposit, settlement, FX) that deserves a board note. No published industry threshold exists for "good" — interpretation is always company- and stage-specific.
Source
imboard Editorial
Related KPIs
finance.total_restricted_cash finance.total_unrestricted_cash finance.operationally_available_cash finance.net_burn_rate finance.runway_months finance.bank_accounts_list

Total COGS

finance.total_cogs
currency Editorial
Description
Total cost of revenue for the period — the sum of the COGS lines. Subtracted from total revenue to produce gross profit. Includes only direct delivery costs; operating expenses (R&D, S&M, CS, G&A) sit below the gross-profit line.
Formula
total_cogs = cloud_hosting + third_party_data + customer_support_delivery + payment_transaction_costs + services_delivery_costs + other_cogs.
Why it matters
The direct-cost base that determines gross profit and gross margin.
Interpretation guidance
Track as a percentage of total revenue; the inverse is gross margin. Decompose increases into the COGS lines.
Source
imboard Editorial
Related KPIs
finance.cloud_hosting finance.third_party_data finance.customer_support_delivery finance.payment_transaction_costs finance.services_delivery_costs finance.other_cogs finance.gross_profit

Total Customer Success

finance.total_cs
currency Editorial
Description
Total customer-success operating expense for the period — the sum of the CS lines. Shown as its own OpEx section (some companies fold CS into COGS; the default here is OpEx). One of the four OpEx section totals.
Formula
total_cs = cs_payroll + cs_tools_software + other_cs.
Why it matters
Isolates the retention investment so the board can weigh it against NRR.
Interpretation guidance
Track against NRR and revenue; classification as OpEx vs COGS is a reporting choice (statementGroup).
Source
imboard Editorial
Related KPIs
finance.cs_payroll finance.cs_tools_software finance.other_cs finance.total_opex

Total G&A

finance.total_ga
currency Editorial
Description
Total general & administrative operating expense for the period — the sum of the G&A lines. One of the four OpEx section totals.
Formula
total_ga = ga_payroll + legal_accounting_professional + office_facilities + software_it + recruiting + travel_entertainment + insurance_compliance + other_ga.
Why it matters
Overhead the board expects to grow sublinearly with revenue.
Interpretation guidance
Track G&A as a percentage of revenue; the ratio should compress as the company scales.
Source
imboard Editorial
Related KPIs
finance.ga_payroll finance.legal_accounting_professional finance.office_facilities finance.software_it finance.recruiting finance.travel_entertainment finance.insurance_compliance finance.other_ga finance.total_opex

Total Operational Inflow

finance.total_operational_inflow
currency Editorial
Description
Sum of cash actually received from operating activities for the period — customer collections (subscription, services, transactional revenue), refunds claimed back from vendors, and any operating tax credits. Excludes financing activities (debt draws, equity proceeds) and investing activities (asset sales, investment income). This is the numerator-side of the net-burn equation, and the cash-basis counterpart to recognized revenue on the P&L. Common pitfall: companies sometimes book annual SaaS prepayments here as a single-month inflow, masking the underlying monthly run-rate — split lumpy items out or smooth over a trailing 3 months.
Formula
Sum of operating-activity cash receipts for the period. Subtract from total_operational_outflow to get the absolute net-burn dollar value (before dividing by months to get the rate).
Why it matters
Inputs the cash-basis revenue side of net burn. A growing inflow at flat-or-falling outflow is the textbook "earning its runway" trajectory; the reverse means the company is more dependent on the cash balance than on revenue.
Interpretation guidance
Reconcile against recognized revenue from `sales.arr` and bookings — a persistent gap means deferred-revenue or DSO is moving. Watch lumpy enterprise prepayments and isolate them; they distort the trailing-average net burn read.
Source
imboard Editorial
Related KPIs
finance.total_operational_outflow finance.net_burn_rate finance.net_working_capital_adjustment sales.arr

Total Operational Outflow

finance.total_operational_outflow
currency Editorial
Description
Sum of cash actually paid for operating activities for the period — payroll and benefits, employer taxes, vendor payments (infra, tooling, contractors), sales and marketing spend, rent, professional services, refunds issued. Excludes financing activities (debt repayment, dividend payments) and investing activities (acquisitions, capex). Direct input to gross burn. Common pitfall: capitalized R&D and long-term capex sometimes get bucketed here; if so they distort gross burn. Keep this strictly operating-cash and surface investing/financing outflows separately so the board can see "ongoing cost base" vs. "discretionary capital deployment".
Formula
Sum of operating-activity cash payments for the period. Equals gross burn × months_in_period when there are no working-capital re-classifications.
Why it matters
The denominator-side of net burn and the basis of gross burn — controlling the structural cost base is the lever most boards can directly act on between fundraises.
Interpretation guidance
Decompose by spend category each board cycle (payroll vs. infra vs. GTM) — a sustained shift toward GTM or infra usually signals a strategic decision worth explicit board endorsement. No single industry threshold for "good" — interpretation is always against ARR, revenue per FTE, and gross-margin context.
Source
imboard Editorial
Related KPIs
finance.total_operational_inflow finance.gross_burn_rate finance.net_burn_rate hr.arr_per_fte

Total OpEx

finance.total_opex
currency Editorial
Description
Total operating expense for the period — R&D + Sales & Marketing + Customer Success + G&A. Subtracted from gross profit to produce EBITDA. Excludes COGS (above the gross-profit line) and below-EBITDA items.
Formula
total_opex = total_rnd + total_sm + total_cs + total_ga.
Why it matters
The full operating cost base below gross profit — the lever between gross profit and EBITDA.
Interpretation guidance
Track against gross profit: OpEx growing faster than gross profit pushes EBITDA down.
Source
imboard Editorial
Related KPIs
finance.total_rnd finance.total_sm finance.total_cs finance.total_ga finance.ebitda

Total R&D

finance.total_rnd
currency Editorial
Description
Total research & development operating expense for the period — the sum of the R&D lines. One of the four OpEx section totals that roll into Total OpEx.
Formula
total_rnd = rd_payroll + product_design_payroll + contractors_outsourcing + rd_tools_software + other_rnd.
Why it matters
The headline R&D investment line the board tracks against revenue and plan.
Interpretation guidance
Track as a percentage of revenue and against budget; decompose movement into the R&D lines.
Source
imboard Editorial
Related KPIs
finance.rd_payroll finance.product_design_payroll finance.contractors_outsourcing finance.rd_tools_software finance.other_rnd finance.total_opex

Total Revenue

finance.total_revenue
currency Editorial
Description
Total recognized revenue for the period — the sum of subscription, usage, services, and other revenue. The P&L revenue subtotal and the denominator for gross margin. Distinct from `sales.total_revenue` (the GTM recognized-revenue metric) and from ARR; this line is the statement total built from the revenue split.
Formula
total_revenue = subscription_revenue + usage_revenue + services_revenue + other_revenue.
Why it matters
The accounting top line and the basis for gross margin and every margin ratio.
Interpretation guidance
Compare period-over-period and against budget; decompose movement into the four revenue lines to explain the change.
Source
imboard Editorial
Related KPIs
finance.subscription_revenue finance.usage_revenue finance.services_revenue finance.other_revenue finance.gross_profit sales.total_revenue

Total Sales & Marketing

finance.total_sm
currency Editorial
Description
Total sales & marketing operating expense for the period — the sum of the S&M lines (payroll, commissions, marketing, paid media, events, tools, other). One of the four OpEx section totals.
Formula
total_sm = sales_payroll + sales_commissions + marketing_payroll + paid_marketing + events_conferences + sm_tools_software + other_sm.
Why it matters
The headline go-to-market spend line; the numerator behind blended CAC.
Interpretation guidance
Track against new ARR/bookings and budget; decompose into the S&M lines to explain change.
Source
imboard Editorial
Related KPIs
finance.sales_payroll finance.sales_commissions finance.marketing_payroll finance.paid_marketing finance.events_conferences finance.sm_tools_software finance.other_sm finance.total_opex

Travel & Entertainment

finance.travel_entertainment
currency Editorial
Description
Cost of travel, meals, customer travel, and board travel for the period. Rolls up minor items; kept as one readable line.
Formula
Travel & entertainment cost for the period.
Why it matters
A discretionary line that is an early lever when tightening burn.
Interpretation guidance
Read directionally; large moves usually reflect travel policy or event timing.
Source
imboard Editorial
Related KPIs
finance.total_ga

Unrestricted Cash

finance.total_unrestricted_cash
currency Editorial
Description
Cash that the company can freely deploy for any operational purpose — total bank balances minus any contractually restricted balances. This is the input most boards actually want when judging runway, because it strips out escrows, security deposits, and processor reserves that cannot be spent on payroll or vendors. The distinction matters more as the company adds enterprise contracts (deposit obligations), debt facilities (covenant balances), and payment processing volume (rolling reserves). Common pitfall: at early stage, restricted cash is often near zero so teams equate this with `finance.total_cash_in_bank` — track them separately from day one to avoid surprise reclassifications later.
Formula
finance.total_cash_in_bank − finance.total_restricted_cash. Equivalent to the sum of bank-account balances flagged `restricted: false` in `finance.bank_accounts_list`.
Why it matters
The right cash number to divide by net burn when computing the spendable runway a board can act on — restricted cash cannot bridge a payroll gap.
Interpretation guidance
A meaningful divergence between unrestricted and total cash (industry folk-wisdom: >5–10%) is the trigger to surface a restricted-cash schedule in the board pack. Compare period-over-period — a sudden drop in unrestricted cash that does not match burn usually means a reclassification (e.g. a new lease deposit) rather than spending.
Source
imboard Editorial
Related KPIs
finance.total_cash_in_bank finance.total_restricted_cash finance.operationally_available_cash finance.runway_months finance.bank_accounts_list

Usage / Consumption Revenue

finance.usage_revenue
currency Editorial
Description
Recognized revenue tied to usage- or consumption-based pricing for the period (metered API calls, compute, seats-on-demand, overages). Separated from subscription revenue because it scales with customer activity rather than contracted commitments and is typically more volatile period to period.
Formula
Recognized usage/consumption-based revenue for the period.
Why it matters
Surfaces how much revenue depends on variable customer activity vs. fixed commitments — a key durability signal.
Interpretation guidance
Higher volatility than subscription revenue is normal; large swings should be explained by customer activity, not pricing changes, unless flagged.
Source
imboard Editorial
Related KPIs
finance.total_revenue finance.subscription_revenue

Working Capital Adjustments

finance.working_capital_adjustments_list
text Editorial
Description
Itemized list of working-capital adjustments with explicit sign-prefix driving the additive-vs-subtractive multiplier — e.g. "+ AR collected: $250k", "− Prepaid infra: $80k", "+ Deferred revenue: $600k". The line-item basis for `finance.net_working_capital_adjustment` and its child KPIs (current_asset_adjustments, current_liability_adjustments). The signed-prefix UI convention prevents the most common working-capital reporting bug — sign-flips that double-count or invert the cash effect. Common pitfall: lumping unrelated items into a single "other working capital" line loses the diagnostic value; break out the top 3–5 components.
Formula
Each row contributes `± amount` based on its sign prefix. Sum of signed amounts equals `finance.net_working_capital_adjustment`. Asset-side rows roll up to `finance.current_asset_adjustments`; liability-side rows roll up to `finance.current_liability_adjustments`.
Why it matters
Makes the working-capital aggregate auditable — the board can see exactly which items moved the number and which direction. Critical at Series A+ when working capital is material.
Interpretation guidance
Track the same line items across periods; a previously material line that vanishes from the list usually indicates a hidden reclassification rather than resolution. New material lines deserve a one-line footnote on what happened.
Source
imboard Editorial
Related KPIs
finance.net_working_capital_adjustment finance.current_asset_adjustments finance.current_liability_adjustments finance.operationally_available_cash

HR 28 KPIs

Approved Headcount Budget

hr.approved_headcount_budget
number Editorial
Description
Board-approved end-of-period headcount target. The contractual reference point against which `hr.total_headcount` and `hr.open_positions` are read — drift means either hiring under plan (typically a growth concern) or over plan (typically a burn-discipline concern). Common pitfall: silent in-year adjustments — boards approve a number, the CEO informally expands or contracts to it, and the variance never gets reconciled. Best practice is to treat changes to this number as board-action items, recorded in `hr.board_actions`.
Formula
Plain board-approved end-of-period FTE target. No derivation — set by board resolution as part of the annual or semi-annual budget. Changes require board approval and should be logged in `hr.board_actions`.
Why it matters
The single number that converts strategic intent into operating constraint. Variance against this number drives the budget-vs-actual conversation that anchors most board meetings' HR section.
Interpretation guidance
DEPRECATED (#2056) — superseded by `hr.total_headcount` carrying scenario=`budget` (the F1 scenario axis, #2019). The board-approved headcount plan is now the budget scenario of the single canonical headcount KPI, so budget and actual share one definition and one variance view instead of two parallel KPIs. This KPI is hidden from the "Add KPI" picker and onboarding but still resolves so existing references keep rendering; prefer `hr.total_headcount` (scenario=budget) for all new budget surfaces. For migration context: pair with `hr.total_headcount` and `hr.headcount_change` for variance reporting — a sustained gap >10% under plan typically signals recruiting capacity issues; sustained over plan signals approval-gate slippage or contractor-conversion that bypassed the budget process (industry folk-wisdom, not citation-grade).
Source
imboard Editorial
Related KPIs
hr.total_headcount hr.headcount_change hr.open_positions hr.hiring_plan hr.board_actions

ARR per FTE

hr.arr_per_fte
currency Industry-backed
Description
Annual Recurring Revenue divided by total FTE-equivalent workforce — the canonical SaaS workforce-productivity ratio anchored to the SaaS Capital Annual Survey methodology (revenue per employee benchmarks). A high-signal denominator for "are we over- or under-staffed for our revenue scale?" Common pitfall: choosing different ARR conventions (ending vs average, GAAP-reconciled vs raw) without locking in a board-level standard. Best practice is to pair this with `sales.arr` so the numerator is unambiguous and to disclose whether contractors are included in the FTE denominator.
Formula
ARR per FTE = `sales.arr` / `hr.total_headcount` (or FTE-equivalent including contractor adjustment from `hr.fte_metrics`). Document the denominator convention in board materials. Per SaaS Capital Annual Survey 2025 methodology (Revenue per Employee).
Why it matters
Investors use this as a quick scalability and operating-leverage proxy — companies with higher ARR/FTE at a given scale typically command premium multiples. Internally, the metric anchors hiring-plan discipline: does each net new FTE earn its keep?
Interpretation guidance
SaaS Capital Annual Survey 2025 (§Revenue per Employee) reports private SaaS medians clustering in the $150K–$250K range, with top quartile $250K+ and bottom quartile under $150K (verify exact figures against the cited report — distributions vary by ARR band). Sub-$100K sustained at Series B+ is a board-level efficiency conversation. Reads should be paired with stage and growth rate — high-growth-stage companies tolerate lower ratios for a window in exchange for growth.
Benchmark
p25 100000 $ · median 130000 $ · p75 175000 $
Related KPIs
sales.arr hr.total_headcount hr.fte_metrics hr.payroll_run_rate operations.rule_of_40

At-Risk Employees

hr.at_risk_count
number Editorial
Description
Count of employees actively flagged as flight risk by managers, based on engagement signals (skip-level surveys, manager 1:1s, counter-offer activity, tenure-curve risk). A leading indicator that complements the lagging `hr.voluntary_exits` number. Common pitfall: stale flags that never get cleared — at-risk lists tend to drift toward "every senior IC ever" without manager discipline. Best practice is a quarterly refresh with explicit add/remove notes and an action attached to each flag.
Formula
Count of currently-flagged active employees on the retention watchlist. Flag criteria are company-defined but typically include: declining 1:1 engagement, missed comp expectations, counter-offer activity, or manager-flagged tenure-curve risk. Convention: flags expire 90 days after entry unless renewed.
Why it matters
Converts manager intuition into a board-readable risk count, and pairs naturally with `hr.retention_initiatives` so the board sees risk and response together. A rising at-risk count without rising retention activity is a yellow flag.
Interpretation guidance
Read alongside `hr.voluntary_exits` — at-risk count should lead exits by 1–2 quarters. Concentration in one team or level is more diagnostic than the absolute number. Sustained at-risk count above ~10–15% of headcount typically warrants a targeted retention program (industry folk-wisdom, not citation-grade).
Source
imboard Editorial
Related KPIs
hr.voluntary_exits hr.voluntary_turnover_rate hr.retention_initiatives hr.talent_challenges

Average Days to Fill

hr.avg_days_to_fill
number (days) Industry-backed
Description
Mean elapsed days between requisition opening (approved and posted) and offer acceptance, averaged across requisitions filled in the period. The headline recruiting-velocity KPI commonly tracked in the SHRM Talent Acquisition Benchmarking Report. Common pitfall: choosing between time-to-fill (req-opened to offer-accepted) and time-to-hire (first-applicant to offer-accepted) without locking the convention — the two can differ by weeks. Best practice is to standardize on time-to-fill (the SHRM benchmark convention) and document any deviation.
Formula
Average Days to Fill = Σ(offer-accepted-date − requisition-opened-date) / count of requisitions filled in the period. Convention: time-to-fill per SHRM Talent Acquisition Benchmarking Report — req-opened to offer-accepted, not first-applicant to offer-accepted.
Why it matters
A stretching time-to-fill is one of the earliest leading indicators of either comp-band misfit, role-spec creep, or recruiter capacity exhaustion. Combined with `hr.open_positions`, it projects when promised capacity actually arrives.
Interpretation guidance
SHRM Talent Acquisition Benchmarking Report typically reports cross-industry medians around 40–45 days time-to-fill, with technical roles (engineering, data) often longer (60–90+ days). Verify against the most recent SHRM report for the exact figure. A sustained increase of >20% with no role-mix change typically signals a recruiting-pipeline issue (industry folk-wisdom, not citation-grade).
Related KPIs
hr.open_positions hr.hiring_plan hr.new_hires hr.key_openings hr.key_hires

Departments

hr.departments
text Editorial
Description
Field-array of per-department rows — department name, leader status (resolved against `hr.leader_status`), and headcount metrics with stable-count auto-calc — rendered as a drag-sortable table grouped by department. Common pitfall: department boundaries drift over time (Eng+R&D merging, GTM splitting into Sales/Marketing/CS) — when boundaries change, prior-period comparisons need an explicit reconciliation note. This KPI is structural, not numeric — no formula applies.
Why it matters
Shows the org map at a glance — where capacity is allocated, which departments are short-staffed, where leader-vacancies are concentrated. Boards use this to validate the strategy-vs-investment alignment ("we say we are product-led but R&D is 20% of headcount").
Interpretation guidance
Read with `hr.leader_status` alongside — a department with strong leader and on-plan headcount tells a very different story from one with interim/vacant leader and growing headcount. Material department-shape changes should be called out in `hr.talent_highlights` or `hr.talent_challenges` narrative.
Source
imboard Editorial
Related KPIs
hr.leader_status hr.total_headcount hr.key_hires hr.key_openings hr.executive_commentary

FTE Metrics

hr.fte_metrics
text Editorial
Description
Derived triple — effective FTE, cost-per-FTE, and annualized payroll — computed from `hr.payroll_run_rate` + `hr.total_contractors` and a contractor-to-FTE conversion factor. Lets the board see capacity in normalized terms even when the staffing mix shifts. Common pitfall: choosing a contractor-to-FTE factor without explicit board agreement — some companies use 1.0 (1 contractor = 1 FTE for capacity), others use 0.8 (account for ramp / partial-engagement), others use cost-equivalent ratios. Lock the convention.
Formula
effectiveFTE = `hr.total_headcount` + (`hr.total_contractors` × contractorFactor). costPerFTE = `hr.payroll_run_rate` / effectiveFTE. annualizedPayroll = effectiveFTE × costPerFTE. Default contractor factor 0.8 unless the board adopts a different convention.
Why it matters
Normalizes capacity and cost across companies with very different contractor strategies, making `hr.arr_per_fte` and `hr.payroll_as_pct_of_burn` more comparable over time. Surfaces hidden cost inflation when contractor headcount grows faster than employee headcount.
Interpretation guidance
Watch the drift between `hr.total_headcount` and effectiveFTE — divergence indicates contractor expansion that may warrant a build-vs-rent conversation. CostPerFTE materially above stage-typical comp benchmarks suggests either a senior-heavy mix or contractor-rate premium creep (industry folk-wisdom, not citation-grade — varies by geography and role mix).
Source
imboard Editorial
Related KPIs
hr.total_headcount hr.total_contractors hr.payroll_run_rate hr.arr_per_fte hr.payroll_as_pct_of_burn

Hiring Plan

hr.hiring_plan
text Editorial
Description
Forward-looking narrative on next-period hiring priorities — target roles, sequence, sourcing strategy, and any unusual asks (executive search, specialized recruiter spend, location flexibility shifts). Anchors the board's understanding of where capacity is heading and what approvals or help are needed. Common pitfall: a stale plan that gets copy-pasted across quarters — the hiring plan should evolve with strategy shifts. Best practice is to lead with the 2–3 highest-priority hires and their justification, then a brief on backfills and bench-builds.
Why it matters
Converts `hr.approved_headcount_budget` (a number) into a board-relevant sequence (a story). Without this, board members lack the context to help with intros, validate strategic-role timing, or push back on questionable role specs.
Interpretation guidance
Strong plans name specific roles, target start dates, and what board help would accelerate the hire. Pair with `hr.key_openings` for the structured priority list — narrative here, table there.
Source
imboard Editorial
Related KPIs
hr.approved_headcount_budget hr.open_positions hr.key_openings hr.avg_days_to_fill hr.new_hires

HR Board Actions

hr.board_actions
text Editorial
Description
Explicit list of HR items requiring board attention, approval, or decision in this meeting — executive comp changes, headcount-budget changes, equity-pool top-ups, employment-policy approvals, and any items needing a board resolution. Common pitfall: burying decisions inside other narrative sections — boards consistently miss requests that are not explicitly tagged as "decision required." Best practice is to label each item as approval-required vs awareness-only and give a one-line ask.
Why it matters
The single most under-served section on most board packs. CEOs often expect their narrative to drive a decision; boards often miss the implicit ask. An explicit "board actions" section is a low-cost forcing function for board-level decision hygiene.
Interpretation guidance
Each item should be one of: approval-required (specific resolution wording or vote), assistance-required (intro / reference / open-door request), or awareness-only (FYI for governance log). Use `hr.risk_items` for the structured-table version once the board adopts that pattern.
Source
imboard Editorial
Related KPIs
hr.risk_items hr.approved_headcount_budget hr.talent_challenges hr.retention_initiatives

HR Executive Commentary

hr.executive_commentary
text Editorial
Description
Stacked commentary editor with per-section icon and live word count, hosting the four canonical HR narrative slots (talent highlights, talent challenges, hiring plan, retention initiatives) under a single base path — each section persists under `<basePath>.<sectionKey>`. The composite container for the narrative side of the HR scorecard, paired with `hr.departments` and `hr.risk_items` for the structured side. Common pitfall: writing each section in isolation — strong commentary cross-references the numbers ("voluntary turnover up 4 points QoQ, here is what we are doing").
Why it matters
Centralizes the narrative around the HR numbers in one editing surface, with word-count feedback that prevents both under- and over-writing. The structure mirrors how boards expect HR to be presented: positives, concerns, plan, mitigations.
Interpretation guidance
Each section should weigh in at roughly the same length (target 80–150 words per section is a reasonable convention) — wildly uneven sections signal the CEO/CHRO is avoiding one of the four. Word-count outliers are an editorial check, not a hard rule.
Source
imboard Editorial
Related KPIs
hr.talent_highlights hr.talent_challenges hr.hiring_plan hr.retention_initiatives hr.departments hr.risk_items

HR Risk Items

hr.risk_items
text Editorial
Description
Structured field-array of board-attention items, each with type / department / action / narrative quartet (problem / impact / proposal / ask). Chip color follows boardActionNeeded: approval=red, assistance=yellow, awareness=blue. The structured-table version of `hr.board_actions` — preferred when the board has adopted the formal risk-item pattern. Common pitfall: drift toward vague "we are working on it" entries — strong items name a specific action with a date.
Why it matters
Forces explicit categorization of each item (approval / assistance / awareness) so the board cannot accidentally skip a decision item. The color chips make scanning faster than narrative text alone.
Interpretation guidance
Red chips (approval) demand a vote or formal resolution this meeting. Yellow chips (assistance) need specific board-member action (intros, references) — name the asks. Blue chips (awareness) are FYI for governance log. A standing meeting with all-blue is well-run; all-red signals a governance backlog.
Source
imboard Editorial
Related KPIs
hr.board_actions hr.talent_challenges hr.retention_initiatives hr.executive_commentary

Involuntary Turnover Rate

hr.involuntary_turnover_rate
percentage (%) Industry-backed
Description
Annualized rate of company-initiated separations as a percentage of average headcount. Complement to `hr.voluntary_turnover_rate`; together they form the total turnover picture per the Mercer US Turnover Survey methodology. Common pitfall: lumping one-time RIFs into the steady-state rate, which makes the trend unreadable. Best practice is to report steady-state involuntary turnover and call out any RIF events separately in `hr.board_actions` with the headcount delta.
Formula
Involuntary Turnover Rate (annualized) = (Terminations in period / Average Headcount in period) × (12 / months in period) × 100. Convention: exclude announced RIF events from the steady-state series; report them separately with headcount delta. Per Mercer US Turnover Survey methodology.
Why it matters
A read on performance-management cadence and any active restructuring. Sustained near-zero raises questions about management discipline; sustained-elevated raises questions about hiring quality or strategy thrash.
Interpretation guidance
US all-industry total turnover historically clusters in the 18–25% annualized range per Mercer US Turnover Survey 2025 (§Total Turnover); involuntary typically represents 4–8% of that total (verify exact splits against the cited report — distributions vary by industry). Companies with very low involuntary rates (<2% annualized) often have buried under-performers; companies above ~8% steady-state typically have a hiring or onboarding-quality issue (industry folk-wisdom on the upper bound, not citation-grade).
Related KPIs
hr.terminations hr.performance_watch_count hr.voluntary_turnover_rate hr.talent_challenges

Key Hires

hr.key_hires
text Editorial
Description
Field-array of notable individual hires that warrant board-level visibility — typically C-1 executives, director-level functional leaders, and strategic specialist hires. Per-item shape: name, level, role, start status, days-to-fill. Rendered via the T2 collapsible-card gallery pattern. Structural, not numeric — formula does not apply. Common pitfall: listing every hire instead of the strategic few — boards lose signal quickly when this section turns into a directory.
Why it matters
Gives the board context for the headline `hr.new_hires` count — five generalist engineers reads very differently from five senior staff engineers plus a new VP Eng. Boards routinely volunteer reference checks and network help when key hires are surfaced.
Interpretation guidance
Keep this list short (typically 3–8 items per board period). Each entry should be defensibly "board-relevant" — strategic role, executive level, or a market-significant external hire. Tie related items to `hr.talent_highlights` narrative where additional context is warranted.
Source
imboard Editorial
Related KPIs
hr.new_hires hr.avg_days_to_fill hr.talent_highlights hr.key_openings hr.leader_status

Key Openings

hr.key_openings
text Editorial
Description
Field-array of priority open roles the board should be aware of and may be able to accelerate — typically C-1 executives, hard-to-fill specialists, and any role open >60 days. Per-item shape: title, department, level, urgency, owner. Rendered via the T2 collapsible-card gallery pattern. Structural, not numeric. Common pitfall: padding the list with every open req — boards add the most value on the 3–8 strategic openings, not on backfilling the next IC.
Why it matters
Turns the scalar `hr.open_positions` count into board-actionable context. Every entry is an opportunity for the board to help with intros, references, or compensation reality-checks. The owner field also surfaces accountability for the search.
Interpretation guidance
Items should name the role, the owner (recruiter or hiring manager), days open, and the ask (intros / references / approval for higher comp band). A role open >90 days without a board narrative or escalation usually signals either insufficient executive attention or unrealistic specifications.
Source
imboard Editorial
Related KPIs
hr.open_positions hr.avg_days_to_fill hr.hiring_plan hr.key_hires hr.leader_status hr.talent_challenges

Leader Status

hr.leader_status
text Editorial
Description
Tri-state leader status (permanent / interim / vacant) for each board-tracked department. Permanent shows name+title; interim shows the covering person; vacant shows the gap explicitly. The single most board-relevant org-design signal — an extended interim or vacant status in a strategic function is almost always a board-level concern. Common pitfall: leaving "interim" indefinitely as a way to avoid the search-and-hire conversation — boards should set a maximum interim duration and treat overruns as board-action items. Structural KPI; no formula.
Why it matters
Leader-coverage gaps in strategic functions (e.g., engineering, sales, product) are leading indicators of execution risk. Boards routinely under-weight this signal because the headcount number can look healthy while the leadership layer is hollow.
Interpretation guidance
Interim status sustained >90 days warrants a board narrative on the search; vacant status in a board-relevant function is a board-action item on day one. Pair with `hr.key_openings` for the structured search-status view and `hr.talent_challenges` for narrative on the search difficulty.
Source
imboard Editorial
Related KPIs
hr.departments hr.key_openings hr.talent_challenges hr.board_actions

Net Headcount Change

hr.headcount_change
number Editorial
Description
Net change in employee headcount during the period — new hires minus (voluntary exits + terminations). The bottom-line growth-or-contraction number on the HR scorecard. Common pitfall: reporting net change without showing the gross-in / gross-out components — boards can't diagnose a flat net number caused by 5 hires and 5 exits the same way they'd diagnose a flat number from zero on each side. Best practice is to surface the four components (new hires, voluntary exits, terminations, net change) together.
Formula
Net Headcount Change = `hr.new_hires` − (`hr.voluntary_exits` + `hr.terminations`). Sign convention: positive = growth, negative = contraction. Does not include contractor changes (those flow through `hr.total_contractors`).
Why it matters
Single-number summary of HR's execution in the period and the simplest reconciliation point between this period's `hr.total_headcount` and the prior period's. Variance from `hr.hiring_plan` is the board-conversation trigger.
Interpretation guidance
Pair with the gross flows for diagnosis. A small positive net with high gross-in and gross-out tells a churn story; a large positive net during contraction periods raises plan-discipline questions. Compare to `hr.approved_headcount_budget` for budget-vs-actual posture.
Source
imboard Editorial
Related KPIs
hr.new_hires hr.voluntary_exits hr.terminations hr.total_headcount hr.approved_headcount_budget

New Hires

hr.new_hires
number Editorial
Description
Count of employees whose first day fell within the reporting period. The growth-input side of the headcount equation, paired with `hr.voluntary_exits` and `hr.terminations` on the loss side. Common pitfall: counting accepted offers vs actual start dates — these can diverge by weeks (notice period) or fall through entirely (offer rescind, candidate ghosting). The board number should be actual starts, not signed offers; pipeline movement belongs in `hr.hiring_plan` narrative.
Formula
Count of employees whose official start date is within the reporting period. Exclude rehires within 90 days (boomerangs) from this count if the board has adopted that convention; otherwise include and note in `hr.talent_highlights`.
Why it matters
Directly drives `hr.headcount_change` and validates execution against `hr.hiring_plan`. Persistent gaps between hiring-plan targets and actual new hires usually indicate either a pipeline problem or compensation-market mismatch — both board-action triggers.
Interpretation guidance
Reconcile against `hr.open_positions` closed in period and against `hr.hiring_plan` targets. A new-hire count materially below plan (e.g. <70% of target for two consecutive periods) typically warrants a board conversation about recruiting capacity, comp band competitiveness, or scope realism (industry folk-wisdom, not citation-grade).
Source
imboard Editorial
Related KPIs
hr.open_positions hr.hiring_plan hr.headcount_change hr.avg_days_to_fill hr.key_hires hr.total_headcount

Open Positions

hr.open_positions
number Editorial
Description
Count of board-approved roles that are currently posted and unfilled (requisition open, offer not yet accepted). The leading-edge indicator for upcoming hiring capacity demand. Common pitfall: "approved" drift — roles that were verbally green-lit but never went through the approval gate get counted here, inflating the number. The board number should match the approved headcount budget; everything else belongs in narrative as "pipeline ideas."
Formula
Count of requisitions where status = open AND approval = granted AND no candidate has accepted. Excludes positions filled but not yet started (those belong to `hr.new_hires` in the next period). Excludes role ideas not yet approved.
Why it matters
Quantifies the hiring debt — every open role is unrealized capacity. Combined with `hr.avg_days_to_fill`, it projects when capacity actually arrives. A growing open-position count while time-to-fill stretches is a recruiting-capacity yellow flag.
Interpretation guidance
Open positions ÷ approved budget gap = recruiting load. If open positions exceed 15–20% of total headcount sustained over multiple periods, it usually signals either compensation issues, recruiter capacity issues, or unrealistic role specs (industry folk-wisdom, not citation-grade). Tie to `hr.key_openings` for priority-weighted context.
Source
imboard Editorial
Related KPIs
hr.approved_headcount_budget hr.avg_days_to_fill hr.hiring_plan hr.key_openings hr.new_hires

Payroll as % of Burn

hr.payroll_as_pct_of_burn
percentage (%) Editorial
Description
Monthly fully-loaded payroll cost as a percentage of `finance.gross_burn_rate`. Tells the board what share of cash outflow funds people vs everything else (infra, GTM spend, professional services, facilities). Common pitfall: comparing this ratio across companies without normalizing for stage and capex intensity — a pure-software seed company will run very payroll-heavy; a hardware-or-bio company will not. Best practice is to read this in conjunction with the burn-rate trend, not in isolation.
Formula
Payroll as % of Burn = (Monthly payroll run rate from `hr.payroll_run_rate` / 12) / `finance.gross_burn_rate` × 100. Use gross burn (not net) so growing revenue does not distort the share. Document any non-cash comp adjustments (e.g., stock-based comp included vs excluded).
Why it matters
Cost-structure shape indicator — pairs naturally with runway math. A rising share without rising headcount can signal comp-band drift; a falling share with rising headcount often signals contractor / GTM expansion. Boards use this for the people-vs-program trade-off conversation.
Interpretation guidance
Early-stage pure-software companies often run 60–75% payroll-of-burn (people-heavy by design); later-stage companies with material GTM spend typically run 40–55%; hardware-heavy or bio-tech companies can run lower still (industry folk-wisdom, not citation-grade — varies materially by business model). Sudden drops without a hiring freeze or program-spend spike are usually accounting reclassifications, not real economics.
Source
imboard Editorial
Related KPIs
hr.payroll_run_rate finance.gross_burn_rate finance.net_burn_rate hr.total_headcount hr.arr_per_fte

Payroll Run Rate

hr.payroll_run_rate
currency Editorial
Description
Annualized fully-loaded payroll cost based on current employee compensation — wages plus employer-paid taxes, benefits, and typical equity refresh allocation. Used as the dominant input into `hr.payroll_as_pct_of_burn` and the projection for `hr.fte_metrics`. Common pitfall: reporting base-salary-only and missing employer payroll taxes, benefits, and bonus accrual — this can understate true cost by 15–30%. Document the loading convention (typically wages × 1.20–1.30 for US fully-loaded) and apply consistently.
Formula
Payroll Run Rate = Σ(employee fully-loaded annual comp). Fully-loaded ≈ base + bonus + employer taxes (Social Security, Medicare, FUTA, SUTA) + benefits (health, dental, 401k match) + equity refresh accrual. US-typical loading factor 1.20–1.30× base; international varies materially by country.
Why it matters
The single largest line in most operating budgets — drives runway calculus, dilution sensitivity at fundraise, and the unit economics conversation. Visible upward steps without a corresponding revenue or headcount-plan justification are board-action triggers.
Interpretation guidance
Trend month-over-month; flag step-changes >5% that are not attributable to net new hires or planned comp cycles. Compare against `hr.approved_headcount_budget` × stage-typical avg comp (industry folk-wisdom, not citation-grade) to surface comp-band drift.
Source
imboard Editorial
Related KPIs
hr.payroll_as_pct_of_burn hr.fte_metrics hr.total_headcount hr.arr_per_fte finance.gross_burn_rate

Performance Watch

hr.performance_watch_count
number Editorial
Description
Count of employees currently on a formal Performance Improvement Plan (PIP) or equivalent performance-bar process. Leading indicator for `hr.terminations` — most PIPs that do not resolve with measurable improvement convert to involuntary exits within one quarter. Common pitfall: confusing PIPs with informal coaching — only employees on a written, time-bound plan with defined exit criteria should be counted here. Informal "we need to talk" relationships belong in the at-risk count, not this number.
Formula
Count of active employees on a written, time-bound Performance Improvement Plan with explicit success criteria and an end date (typically 30/60/90 days). Excludes informal coaching relationships and probationary new hires unless the new-hire is on a formal extension PIP.
Why it matters
Leading indicator for `hr.terminations` and a read on management discipline — managers who avoid PIPs accumulate B-players, managers who over-use them are training-out coachable performers. The trend matters more than the snapshot.
Interpretation guidance
A sustained PIP count of ~1–3% of headcount typically reflects healthy performance management; near-zero suggests management avoidance; >5% sustained suggests either hiring-quality issues or unrealistic performance bars (industry folk-wisdom, not citation-grade). PIP-to-termination conversion rate (tracked privately) usually settles in the 60–80% range when the program is well-run.
Source
imboard Editorial
Related KPIs
hr.terminations hr.involuntary_turnover_rate hr.at_risk_count hr.talent_challenges

Retention Initiatives

hr.retention_initiatives
text Editorial
Description
Narrative on the programs and actions in flight to retain key talent and reduce voluntary turnover — refresh grants, comp-band adjustments, manager training, career-pathing programs, and similar. The response side of the `hr.at_risk_count` and `hr.voluntary_turnover_rate` story. Common pitfall: listing perks (snacks, swag) instead of actions tied to retention drivers. Best practice is to name the initiative, the at-risk population it targets, and the leading-indicator metric you'll watch.
Why it matters
Shows the board that retention risk is being actively managed, not just measured. Initiatives without measurement plans are typically performative — pairing each initiative with a leading-indicator KPI (engagement score, manager 1:1 cadence, refresh-grant acceptance) shows operational rigor.
Interpretation guidance
Strong examples: equity refresh program targeting tenure-3+ engineers; manager-effectiveness training tied to attrition-by-manager data; career-laddering for senior ICs. Weak examples: generic culture/perks investments without a specific retention thesis.
Source
imboard Editorial
Related KPIs
hr.voluntary_turnover_rate hr.at_risk_count hr.voluntary_exits hr.talent_challenges

Talent Challenges

hr.talent_challenges
text Editorial
Description
Narrative on key hiring difficulties, attrition concerns, comp-market pressure, and market-driven talent risks that the board should weigh in on or be aware of. The "watch this" companion to `hr.talent_highlights`. Common pitfall: sanitizing this section to avoid uncomfortable conversations — but talent challenges are precisely where boards add the most value (warm intros, comp benchmarking, executive search). Best practice is to name the specific role, team, or risk and the ask explicitly.
Why it matters
The mechanism by which the board's network and judgment get applied to talent gaps. Numbers in `hr.voluntary_exits` and `hr.at_risk_count` show the symptom; this section names the cause and the ask.
Interpretation guidance
Treat each item as a problem-statement plus ask. Example structure: "Senior PM role open 90 days; market comp drift +12% over our band; ask: board intros + comp-band re-baseline." Vague items signal either insufficient management attention or unwillingness to surface the issue.
Source
imboard Editorial
Related KPIs
hr.voluntary_exits hr.at_risk_count hr.open_positions hr.avg_days_to_fill hr.board_actions hr.risk_items

Talent Highlights

hr.talent_highlights
text Editorial
Description
Free-form narrative on notable hires, promotions, internal moves, and other positive organizational developments the board should be aware of. The "good news" companion to `hr.talent_challenges`. Common pitfall: listing every internal move and burying the genuinely important signals (key executive hires, strategic team-build milestones). Best practice is 3–5 bulleted items per period, each tied to a board-relevant outcome or risk-it-mitigates rather than a generic celebration.
Why it matters
Gives the board context for the headline numbers — a flat headcount with a major engineering leader hired tells a different story than a flat headcount with no narrative. Also primes board members for warm-intro asks and reference checks.
Interpretation guidance
Quality > quantity. Items should answer "why does the board care?" — a senior IC promotion to manager matters because it filled a key vacancy; a generic "we promoted 4 people" doesn't. Pair each highlight with the role/team it impacts.
Source
imboard Editorial
Related KPIs
hr.key_hires hr.executive_commentary hr.talent_challenges hr.new_hires

Terminations

hr.terminations
number Editorial
Description
Count of company-initiated employee separations during the period — performance-management exits, layoffs, redundancies, and for-cause terminations. The numerator of `hr.involuntary_turnover_rate` and the inverse of `hr.voluntary_exits` on the attrition page. Common pitfall: bundling layoff events (often one-time, board-known) with normal performance-management churn (steady-state, manager-driven). Best practice is to break out layoffs in `hr.talent_challenges` narrative and reserve this number for the recurring stream.
Formula
Count of company-initiated employee separations within the reporting period — performance terminations, RIFs, role eliminations, and for-cause exits. Excludes voluntary resignations (those are `hr.voluntary_exits`) and contractor-end events.
Why it matters
A direct read on performance-management cadence and any organizational restructuring activity. Spikes correlate with strategy pivots, post-fundraise rebalancing, or recovery from over-hiring — each implies different board narratives.
Interpretation guidance
Sustained termination volume above ~1% of headcount per month (excluding announced RIFs) typically signals hiring-quality issues, performance-bar drift, or comp-band misfit (industry folk-wisdom, not citation-grade). One-time RIF events should be reported separately in `hr.board_actions` with the headcount delta noted.
Source
imboard Editorial
Related KPIs
hr.involuntary_turnover_rate hr.performance_watch_count hr.headcount_change hr.talent_challenges hr.board_actions

Total Contractors

hr.total_contractors
number Editorial
Description
Count of active 1099 contractors, consultants, agencies-of-record, and similar non-employee labor at period end. Tracked separately from `hr.total_headcount` because the cost structure, retention dynamics, and classification risk are different. Common pitfall: under-counting agencies that bill on a project basis without per-head visibility — these often slip out of HR systems and surface only in finance AP detail. A contractor-to-FTE ratio above ~30% sustained typically warrants a classification audit and a deliberate "build vs rent" board conversation.
Formula
Count of active non-employee workers (1099 contractors, agency contractors, consultants) with engagement status = active at period end. Convert to FTE-equivalent in `hr.fte_metrics` using a contractor-to-FTE factor (e.g., 0.8) if applying to capacity math.
Why it matters
Hidden capacity and hidden cost — contractors expand effective capacity without going through the headcount-approval gate, but they carry classification risk and tend to convert into permanent cost without explicit board approval. Surfacing the count counters that quiet expansion.
Interpretation guidance
Contractor share of total workforce above ~30% sustained signals either a hiring-gate workaround or a deliberate flex-staffing strategy — both warrant a narrative explanation. Under US IRS rules and similar in other jurisdictions, sustained "contractors" working full-time under direction risk reclassification (industry folk-wisdom on the 30% threshold, not citation-grade).
Source
imboard Editorial
Related KPIs
hr.total_headcount hr.fte_metrics hr.hiring_plan hr.payroll_run_rate

Total Headcount

hr.total_headcount
number Editorial
Description
Total number of employees (W-2 / direct-employment equivalents) across all departments at period end. The base denominator for nearly every other HR ratio — turnover rate, revenue per FTE, payroll as % of burn — so getting the snapshot date and the FTE-vs-headcount convention right matters. CANONICAL HEADCOUNT (#2056): this single KPI carries BOTH the plan and the reported figure via the scenario axis (#2019) — scenario=`budget` is the board-approved headcount plan (formerly the separate, now-deprecated `hr.approved_headcount_budget`), and scenario=`actual` is the reported end-of-period count. Budget-vs-actual variance is read off the two scenarios of this one definition. Common pitfall: mixing headcount (people) with FTE (capacity) — they diverge whenever part-time, contractor, or shared-services arrangements exist. Document the convention (typically "FTE-equivalent, employees only, end-of-period") at the board level once and apply consistently.
Formula
Count of active employees at period end. Convention: FTE-equivalent (a 0.5 FTE counts as 0.5), employees only (contractors tracked separately in `hr.total_contractors`). Snapshot is end-of-period unless the board has explicitly adopted an average-headcount convention for ratio math. The budget scenario (scenario=`budget`) holds the board-approved plan for the same definition; actuals (scenario=`actual`) hold the reported count.
Why it matters
The denominator for every HR ratio the board reads — turnover %, revenue/FTE, payroll as % of burn. Drift in this number without a corresponding hiring-plan update is a leading signal of unmanaged growth or quiet attrition.
Interpretation guidance
Read budget-vs-actual off this one KPI: scenario=`budget` is the board-approved headcount plan, scenario=`actual` is the reported count (#2056 — this replaces the deprecated `hr.approved_headcount_budget`). A delta above ±5% without a board note typically warrants explanation. Stage norm for SaaS (industry folk-wisdom, not citation-grade): seed 5–15, Series A 20–50, Series B 50–150, Series C 150–400.
Source
imboard Editorial
Related KPIs
hr.headcount_change hr.approved_headcount_budget hr.new_hires hr.voluntary_exits hr.terminations hr.total_contractors hr.arr_per_fte

Voluntary Exits

hr.voluntary_exits
number Editorial
Description
Count of employees who resigned during the period (initiated by employee, not the company). The numerator of the `hr.voluntary_turnover_rate` calculation and the headline "are we losing people" number boards anchor on. Common pitfall: ambiguous "mutually agreed" exits — companies sometimes log managed-out exits as voluntary to keep the visible number low. Define the test: if the employee initiated the conversation and there was no formal performance trigger, it is voluntary; otherwise log as termination.
Formula
Count of employees with employee-initiated separations (resignations, retirements) during the period. Excludes terminations (those go in `hr.terminations`), exclusions for cause, and contractor-end events.
Why it matters
The leading indicator the board reads for retention health and culture risk. Concentration in a single team, level, or tenure cohort is more informative than the absolute number — investigate the pattern, not just the headline.
Interpretation guidance
Convert to `hr.voluntary_turnover_rate` (annualized %) for cross-period comparison and benchmarking. Spike triggers: 3+ voluntary exits from one team in a quarter, or any C-1 (executive direct-report) departure — both warrant board narrative in `hr.talent_challenges`.
Source
imboard Editorial
Related KPIs
hr.voluntary_turnover_rate hr.at_risk_count hr.retention_initiatives hr.talent_challenges hr.total_headcount

Voluntary Turnover Rate

hr.voluntary_turnover_rate
percentage (%) Industry-backed
Description
Voluntary exits over a trailing period, expressed as an annualized percentage of average headcount — the headline attrition number on the HR scorecard. Anchored to the Mercer US Turnover Survey methodology (Mercer reports voluntary vs involuntary turnover annually). Common pitfall: comparing a single quarter's annualized rate against an annual benchmark — short-window annualization is noisy. Best practice is trailing-12-months for benchmark comparison and trailing-3 or trailing-6 for trend reads. Per #1426: stage-specific industry norms here are folk-wisdom unless tied to a specific Mercer or comparable published cut.
Formula
Voluntary Turnover Rate (annualized) = (Voluntary Exits in period / Average Headcount in period) × (12 / months in period) × 100. Average headcount = (start headcount + end headcount) / 2 is the simplest acceptable convention; (Σ daily headcount / days in period) is more precise. Per Mercer US Turnover Survey methodology.
Why it matters
The canonical retention KPI investors and boards benchmark against. Tracks the cost of churn — every voluntary exit triggers a replacement-cost cycle (recruiting + onboarding + ramp), commonly estimated at 0.5–2× the role's annual salary depending on level (industry folk-wisdom, not citation-grade).
Interpretation guidance
US all-industry voluntary turnover is typically 13–17% annualized per Mercer US Turnover Survey 2025 (§Voluntary Turnover). Tech sector typically runs higher than the all-industry average; engineering and sales roles run highest within tech. Sustained voluntary turnover above ~20% annualized at any stage is a board-action trigger; sustained sub-5% can indicate under-performance management (managers not exiting B-players). Compare trailing-12-month rates, not quarterly snapshots.
Benchmark
p25 7 % · median 11 % · p75 17 %
Related KPIs
hr.voluntary_exits hr.involuntary_turnover_rate hr.at_risk_count hr.retention_initiatives hr.talent_challenges

Product 18 KPIs

Capacity Allocation

product.capacity_allocation
text Editorial
Description
Container handle for the structured 5-category engineering-capacity object — total engineers plus the per-bucket split across innovation, maintenance, tech debt, customer support, and sales support (each a percentage), with an optional innovation target. The bespoke product feed card renders this as the capacity-allocation donut the demo design shows. RICHER than the 3-way `product.capacity_allocation_pct` percentage triple — this carries the headcount + the five operating buckets the donut needs. Common pitfall: the five buckets not summing to 100% because a category (e.g. sales support) was omitted.
Formula
Container — { totalEngineers, innovation, maintenance, techDebt, customerSupport, salesSupport, targetInnovation? }. The five percentage buckets sum to 100%; targetInnovation is the aspirational innovation share, not allocated capacity.
Why it matters
Shows where engineering headcount actually goes across all five operating modes — the gap between innovation and its target is the clearest read on whether the company is investing in the future or absorbed by run-the-business work.
Interpretation guidance
A large gap between `innovation` and `targetInnovation` (e.g. 32% vs 50%) means run-the-business work is crowding out new capabilities. Read alongside `product.delivery_predictability` — falling innovation with falling predictability is a capacity-crisis signal.
Source
imboard Editorial
Related KPIs
product.capacity_allocation_pct product.innovation_capacity_pct product.total_engineers product.delivery_predictability

Capacity Allocation

product.capacity_allocation_pct
percentage (%) Editorial
Description
Breakdown of engineering capacity across new features, maintenance, and tech debt — typically reported as a three-way split summing to 100%. The execution-level view of where engineering hours are actually going (vs. `innovation_capacity_pct` which is a single percentage for new-capabilities work, and vs. `offensive_roadmap_pct` which is a roadmap-classification percentage). Common pitfall: capacity allocation reported in plan rather than actuals. The plan can say 60% new features but the actuals can be 30% new features and 50% support work — the gap is the operating signal. Boards should require both planned and actual splits, at least quarterly.
Formula
Three-way breakdown: new_features_pct + maintenance_pct + tech_debt_pct = 100%. Measured in the same unit as capacity (eng-weeks, story points, or sprint capacity). Report planned vs actual split — the gap is the operational signal.
Why it matters
Names where engineering hours actually go. The plan-versus-actual gap is one of the highest-signal operational metrics for the board — a persistent 20+ point gap between planned and actual new-feature allocation is the loudest possible flag that the company is under-investing in platform health (the missing hours are going to firefighting).
Interpretation guidance
Industry folk-wisdom, not citation-grade: a healthy steady-state split at growth-stage SaaS is roughly 50–70% new features, 15–30% maintenance, 10–20% tech debt. Companies in platform-investment cycles will skew toward maintenance and tech debt. Pair with `innovation_capacity_pct` and `delivery_predictability` — capacity allocation tells you where time goes, predictability tells you whether commitments hold, innovation capacity tells you the available headroom for offense.
Source
imboard Editorial
Related KPIs
product.innovation_capacity_pct product.offensive_roadmap_pct product.defensive_roadmap_pct product.delivery_predictability product.total_engineers

Churn from Quality Issues

product.quality_churn_pct
percentage (%) Editorial
Description
Percentage of customer churn (logo or ARR, define explicitly) where the primary stated reason is product or quality problems — bugs, performance, missing core functionality, reliability incidents. Distinguishes product-driven churn from pricing-driven, competitor-driven, or use-case-fit-driven churn. Common pitfall: relying on free-text exit-survey reasons. Customers commonly cite "price" when the underlying issue was reliability or missing features — boards should require both the customer-stated reason and the CSM/Account-Manager-assigned root cause, and watch the gap. The Pendo "Product-Led Growth Benchmark" and similar product-analytics publishers cover product-driven churn qualitatively, not as published numeric ranges.
Formula
quality_churn_pct = (churn_attributable_to_quality / total_churn) × 100. Be explicit about whether the numerator and denominator are logo-churn or ARR-churn; the two can diverge sharply if quality issues hit small-customer vs strategic-account differently. Define "attributable to quality" explicitly (CSM root-cause assignment is more reliable than customer-stated exit reason).
Why it matters
Isolates the share of revenue loss the R&D organization can directly act on. High and rising quality-churn is the loudest signal that engineering investment should shift from new-feature to platform-hardening. Low quality-churn alongside high overall churn signals the problem is GTM or product-market-fit, not engineering.
Interpretation guidance
Industry folk-wisdom, not citation-grade: at healthy growth-stage SaaS, quality-driven churn is typically a minority of total churn (under one-third). Quality-churn rising past 40% of total churn is a strong "harden the platform" signal — the board should expect a `defensive_roadmap_pct` increase in response. Cross-reference with `scalability_headroom`: a thin headroom paired with rising quality-churn usually means the company is hitting a reliability cliff.
Benchmark
p25 5 % · median 10 % · p75 20 %
Source
imboard Editorial
Related KPIs
product.feature_adoption product.defensive_roadmap_pct product.scalability_headroom customers.logo_churn_rate customers.gross_revenue_retention

Commitments Roadmap %

product.commitments_roadmap_pct
percentage (%) Editorial
Description
Share of roadmap capacity allocated to CUSTOMER COMMITMENTS — the third slice of the offensive / defensive / commitments roadmap mix (the slice the bespoke product card needs to complete the roadmap-mix bar). Offensive (`product.offensive_roadmap_pct`) is net-new market expansion, defensive (`product.defensive_roadmap_pct`) is retention/churn-prevention work, and this commitments slice is contractually or relationship-committed deliverables (e.g. enterprise SCIM/audit-log promises). The three should sum to 100%. Common pitfall: commitments work going untracked, so the roadmap looks more offensive than the team actually is.
Formula
commitments_roadmap_pct = customer-committed roadmap capacity ÷ total roadmap capacity × 100. Third slice of the mix: offensive_roadmap_pct + defensive_roadmap_pct + commitments_roadmap_pct = 100%.
Why it matters
Makes contractually-committed roadmap work visible as its own category — a high commitments share means the roadmap is increasingly dictated by sales promises rather than product strategy.
Interpretation guidance
Read as the third slice with `product.offensive_roadmap_pct` and `product.defensive_roadmap_pct`. A rising commitments share that crowds out offensive work signals the roadmap is being driven by deal-by-deal promises.
Source
imboard Editorial
Related KPIs
product.offensive_roadmap_pct product.defensive_roadmap_pct product.key_initiatives

Delivery Predictability

product.delivery_predictability
percentage (%) Editorial
Description
Percentage of committed deliverables shipped on or before the originally-promised date within a measurement window (typically a quarter). Surfaces whether the engineering organization can be trusted to hit commitments the company makes externally — to customers in contracts, to the board in quarterly plans, to GTM teams sequencing launches. Common pitfall: gaming. Teams over-deliver by under-promising (predictability climbs while velocity drops) or move the goalposts (re-baseline mid-quarter so "on-time" stays high). Boards should ask for "predictability against original commitment", not "against current plan", and pair with throughput trends.
Formula
delivery_predictability_pct = (commitments_delivered_on_time / total_commitments) × 100, measured against the originally-promised date (not the most recently re-baselined date). Define "on time" explicitly — within the promised week, sprint, or quarter — and apply consistently.
Why it matters
Predictability is the contract between engineering and the rest of the business. When it slips, GTM cannot sequence launches, sales cannot promise dates, and the board cannot trust the quarterly plan. Sustained low predictability is a leading indicator of either capacity mismatch, planning hygiene problems, or accumulated technical debt.
Interpretation guidance
Industry folk-wisdom, not citation-grade: 70–85% predictability is typical for healthy growth-stage engineering organizations; 90%+ usually means sandbagging (commitments are too soft); below 60% means the planning process is broken or capacity is mismatched. Trend matters more than absolute level — a stable 75% is healthier than a 90% sliding to 70% quarter-over-quarter.
Benchmark
p25 55 % · median 70 % · p75 85 %
Source
imboard Editorial
Related KPIs
product.key_initiatives_status product.capacity_allocation_pct product.innovation_capacity_pct product.scalability_headroom

Growth & Differentiation %

product.offensive_roadmap_pct
percentage (%) Editorial
Description
Percentage of the planned roadmap (typically next 1–2 quarters) allocated to offensive bets — net-new capabilities, market expansion, differentiation moats, new monetization. The "what proportion of the plan is about winning" view. Common pitfall: counting "improvements to existing features" as offensive when the change is really table-stakes parity work. Boards should expect a McKinsey-style horizon framing (Horizon 1 = core, Horizon 2 = adjacent, Horizon 3 = transformational) or an equivalent classification, and apply it consistently. Per the original McKinsey "Three Horizons" framing (Baghai/Coley/White, "The Alchemy of Growth", 1999), a healthy portfolio funds all three — over-indexing on any one is a strategic risk.
Formula
offensive_roadmap_pct = (roadmap_capacity_allocated_to_offensive_initiatives / total_roadmap_capacity) × 100, where "offensive" includes net-new capabilities, market-expansion work, differentiation features, and new monetization. Complement of `defensive_roadmap_pct` (they should sum to ~100% in a fully-classified roadmap).
Why it matters
Encodes the company's strategic posture in one number. Boards use this to check the roadmap against the strategy narrative — a company saying it is "going on offense" while showing a 30% offensive roadmap has a story-versus-execution gap worth flagging.
Interpretation guidance
Industry folk-wisdom, not citation-grade: 50–70% offensive in growth-stage companies pursuing market expansion; 30–50% in companies stabilizing a platform; below 30% in turnaround / harden-the-base modes. Pair with `defensive_roadmap_pct` and `innovation_capacity_pct` — strategic offense requires both intent (this metric) and available bandwidth (innovation capacity). The right number is stage-, market-, and strategy-dependent — the trend and the stated rationale matter more than the absolute level.
Source
imboard Editorial
Related KPIs
product.defensive_roadmap_pct product.innovation_capacity_pct product.portfolio_strategy product.feature_adoption product.key_initiatives_status

Innovation Capacity %

product.innovation_capacity_pct
percentage (%) Editorial
Description
Percentage of R&D capacity (typically measured in engineering-weeks or story points over a quarter) allocated to net-new capabilities, as opposed to maintenance, bug fixes, internal tooling, or customer-support engineering. The "available bandwidth for offense" view. Common pitfall: confusing innovation capacity (input — how much team-time is available for new work) with `offensive_roadmap_pct` (output — what proportion of the planned roadmap is growth-oriented). A team can have 60% innovation capacity allocated entirely to defensive work if the roadmap demands it. Boards should look at both together.
Formula
innovation_capacity_pct = (engineering_capacity_on_new_capabilities / total_engineering_capacity) × 100. Define "new capabilities" explicitly — typically excludes bug fixes, performance work, internal tooling, support engineering, and tech-debt paydown. Measured in the same unit as capacity allocation (eng-weeks, story points, or sprint capacity).
Why it matters
Surfaces structural friction. A team with only 20% innovation capacity is being eaten by maintenance and reactive work — the board should be asking why (platform debt, support load, headcount mismatch) before approving new feature commitments.
Interpretation guidance
Industry folk-wisdom, not citation-grade: 50–70% innovation capacity is typical for healthy growth-stage product engineering; below 40% suggests the team is operating in firefighting mode; above 80% suggests under-investment in platform health (will eventually pay back in `quality_churn_pct` and `scalability_headroom` compression). Trend matters most — a steady decline quarter-over-quarter is a leading indicator of accumulating maintenance debt.
Source
imboard Editorial
Related KPIs
product.capacity_allocation_pct product.offensive_roadmap_pct product.defensive_roadmap_pct product.rd_efficiency product.delivery_predictability

Key Initiatives

product.key_initiatives
text Editorial
Description
Container handle for the field-array of strategic product initiatives committed for the current quarter / half — each entry tracks the initiative name, status (on-track / at-risk / blocked / shipped / cut), owner, target date, and a one-line explanation or mitigation plan. The structured, per-initiative companion to the `product.key_initiatives_status` narrative: the narrative gives the execution-pulse story, this gallery makes each initiative individually trackable with its own owner and status. Renders via the CollapsibleFormItemCardGallery widget (the reused gallery pattern shared with sales pipeline deals and HR key hires / openings). Common pitfall: every initiative defaults to "on-track" until two weeks before its deadline — require an explicit at-risk state with a mitigation plan, not a re-label at the deadline.
Formula
Container — field-array of initiative items (name, status, owner, targetDate, explanation / mitigation). No aggregate calculation; the surface makes each strategic initiative individually trackable at the board level.
Why it matters
Connects the strategic narrative to delivery reality at the level the board can act on — surfaces where engineering needs unblocking and where commitments are slipping, per named initiative with a named owner. The board's most efficient leverage point on the product organization.
Interpretation guidance
Watch for chronic "at-risk" items without escalation (the team has accepted slippage and is notifying, not asking for help) and for all-green status alongside a declining `product.delivery_predictability` (status is being optimistically managed). Pair with the `product.key_initiatives_status` narrative — the gallery tracks the items, the narrative explains the pattern.
Source
imboard Editorial
Related KPIs
product.key_initiatives_status product.delivery_predictability product.portfolio product.offensive_roadmap_pct product.capacity_allocation_pct

Key Initiatives Status

product.key_initiatives_status
text Editorial
Description
Stoplight-plus-narrative status of the strategic product initiatives committed for the current quarter / half — each initiative ideally tagged on-track / at-risk / blocked / shipped, with a one-line explanation. The execution-pulse view that connects strategy intent to delivery reality. Common pitfall: every initiative defaults to "on track" until two weeks before the deadline, then turns red — a board that only sees binary green-or-red status without intermediate "at-risk" signaling is being managed reactively. Pair with `delivery_predictability` to detect this pattern; require at-risk initiatives to surface a mitigation plan, not just a label.
Formula
Narrative — list of named strategic initiatives, each with (1) status (on-track / at-risk / blocked / shipped / cut), (2) one-line explanation, (3) for at-risk and blocked: a mitigation plan or escalation request.
Why it matters
Connects the strategic narrative to delivery reality at the level the board can act on. Surfaces where engineering needs unblocking, where commitments are slipping, and where the strategy needs revision. The board's most efficient leverage point on the product organization.
Interpretation guidance
Watch for two patterns: (1) chronic "at-risk" without escalation — usually a sign that the team has accepted the slippage and the board is being notified, not asked for help. (2) all-green status alongside declining `delivery_predictability` — usually a sign that the status field is being optimistically managed. The right cadence: at-risk should appear early and resolve via mitigation, not by re-labeling at the deadline.
Source
imboard Editorial
Related KPIs
product.delivery_predictability product.portfolio_strategy product.offensive_roadmap_pct product.defensive_roadmap_pct product.capacity_allocation_pct

Product Portfolio

product.portfolio
text Editorial
Description
Container handle for the field-array of products in the portfolio — each entry tracks the product name, its portfolio classification (e.g. growth engine / cash cow / innovation bet / sunset candidate, or Horizon 1/2/3), ARR contribution, investment thesis, and lifecycle stage. The structured, per-product companion to the `product.portfolio_strategy` narrative: the narrative tells the story, this gallery makes each product line individually visible and trackable. Renders via the CollapsibleFormItemCardGallery widget (the reused gallery pattern shared with sales pipeline deals and HR key hires / openings). Common pitfall: a portfolio gallery that lists products without an explicit classification or investment thesis per item — that is an inventory, not a portfolio.
Formula
Container — field-array of product items (name, classification, ARR contribution, investment thesis, lifecycle stage). No aggregate calculation; the surface makes each material product line individually visible and classifiable at the board level.
Why it matters
Forces explicit, per-product classification — boards offer better strategic guidance when every material product line is named with its game (growth / cash / option-value / sunset) rather than buried in a single narrative. Surfaces whether the company has a real portfolio or a list of products it happens to ship.
Interpretation guidance
Read alongside the `product.portfolio_strategy` narrative and `product.top_product_arr_concentration` — heavy ARR concentration in one item without an explicit diversification thesis on the others is a strategic risk the board should name. A portfolio with every item classified as a "growth engine" is a wishlist; expect at least some cash cows and explicit sunset candidates in a maturing company.
Source
imboard Editorial
Related KPIs
product.portfolio_strategy product.top_product_arr_concentration product.offensive_roadmap_pct product.defensive_roadmap_pct sales.arr

Product Portfolio Strategy

product.portfolio_strategy
text Editorial
Description
Narrative overview of the product portfolio — which products are growth engines, which are cash cows, which are innovation bets, and which are candidates for sunset. The CEO/CPO articulation of "what game each product line is playing." Frequently structured along the McKinsey Three Horizons framing or the classic BCG growth-share matrix (stars / cash cows / question marks / dogs — per Bruce Henderson's "The Product Portfolio", 1970). Common pitfall: the portfolio narrative does not name horizons, life-cycle stages, or sunset candidates — a portfolio described entirely as "growth engines" is not a portfolio strategy, it is a wishlist. Boards should push for explicit classification of every material product.
Formula
Narrative — no calculation. Should cover (1) classification of each material product (e.g. Horizon 1/2/3 or BCG matrix quadrant), (2) ARR concentration by product, (3) investment thesis per product, (4) any sunset candidates and timing, (5) cross-product synergies or cannibalization risks.
Why it matters
Forces explicit articulation of the multi-product story — boards offer better strategic guidance when they understand which product is being optimized for growth vs cash vs option-value. Reveals whether the company has a portfolio strategy or just a list of products it happens to ship.
Interpretation guidance
A portfolio narrative that has not evolved across multiple quarterly updates while the market has shifted is a flag — the strategy is either uncontested or unmonitored. A narrative that pivots every update is also a flag — typically signals over-reactivity. Compare against `top_product_arr_concentration` — heavy concentration without an explicit portfolio-diversification thesis is a strategic risk the board should name.
Source
imboard Editorial
Related KPIs
product.top_product_arr_concentration product.offensive_roadmap_pct product.defensive_roadmap_pct product.key_initiatives_status sales.arr

R&D Efficiency

product.rd_efficiency
number Editorial
Description
Ratio of net-new ARR generated in a period to R&D spend in the same period — answers "how much revenue does each R&D dollar produce?" Distinct from sales-efficiency metrics (Magic Number, CAC payback) which measure sales/marketing productivity. Common pitfall: R&D-driven ARR (new capabilities, expansion features) shows up on a 2–4 quarter lag after the spend — single-period ratios mis-state the relationship. Boards should look at trailing-twelve-month R&D efficiency, not month-over-month, and pair with `innovation_capacity_pct` to understand whether the spend is on growth bets or maintenance.
Formula
rd_efficiency = net_new_arr_in_period / rd_monthly_spend_in_period. Best computed on a trailing-twelve-month basis to absorb the spend-to-revenue lag. Note: not the same as "R&D ROI" (which would deduct R&D cost from revenue); this is a velocity ratio, not a profitability ratio.
Why it matters
Highest-leverage indicator of whether R&D investment is converting into revenue. A persistent decline signals either an over-built team relative to demand, a feature-product fit gap, or accumulated tech debt slowing throughput — each prescribes different board action.
Interpretation guidance
No single published benchmark applies across stages and business models. Industry folk-wisdom, not citation-grade: at growth-stage SaaS, $1 of R&D spend producing $1–2 of net-new ARR is healthy; below $0.5 is a flag; above $3 typically signals either underinvestment in R&D (about to hit a velocity wall) or a one-time price-increase boost. Always pair with `quality_churn_pct` — high efficiency with rising quality-churn means the ratio is borrowing from future periods.
Benchmark
p25 0.15 ratio · median 0.27 ratio · p75 0.4 ratio
Source
imboard Editorial
Related KPIs
product.rd_monthly_spend product.total_engineers product.innovation_capacity_pct product.quality_churn_pct sales.arr

R&D Monthly Spend

product.rd_monthly_spend
currency (/month) Industry-backed
Description
Total monthly cash outflow on research and development — fully-loaded engineering, product, and design payroll plus tooling, infrastructure dedicated to product development, contractors, and direct R&D vendor spend. The "input" side of R&D efficiency. Common pitfall: companies report base-payroll R&D and exclude the loaded cost (benefits, stock comp at cash-cost basis, allocated rent, dev tooling), under-reporting true R&D burn by 25–40%. Boards should always ask whether the number is base-payroll, fully-loaded, or GAAP R&D expense — they tell different stories. The KBCM/Sapphire SaaS Survey reports R&D as a percentage of revenue for its company panel — use that as the benchmarking lens.
Formula
Sum of fully-loaded R&D-team payroll + benefits + allocated stock-comp + R&D-dedicated infrastructure + R&D tooling + R&D vendor spend, expressed as a monthly figure. Different from GAAP R&D expense (which capitalizes some software development costs); footnote the convention.
Why it matters
Largest single line of operating spend at most growth-stage SaaS companies — the input that `rd_efficiency` converts into revenue. The board reads this to gauge whether the company is over- or under-investing in product velocity relative to revenue ramp.
Interpretation guidance
Compare R&D spend to revenue (or ARR run-rate) to derive R&D-as-% of revenue. Per the KBCM/Sapphire SaaS Survey (latest annual edition — see capital-allocation section), median R&D-as-% of revenue runs ~25–35% at early-growth SaaS and compresses with scale. Out-of-band (e.g. 60%+ at a $20M ARR company) usually signals either heavy platform-investment cycles or under-monetization — flag for context. Always pull the current KBCM/Sapphire edition rather than relying on a memorized range.
Related KPIs
product.rd_efficiency product.total_engineers product.innovation_capacity_pct sales.arr finance.net_burn_rate

Revenue Protection %

product.defensive_roadmap_pct
percentage (%) Editorial
Description
Percentage of the planned roadmap allocated to defensive work — platform reliability, security/compliance, scalability rearchitecture, table-stakes parity with competitors, customer-retention features. The complement of `offensive_roadmap_pct`. Common pitfall: defensive work is chronically under-funded (less visible to customers, harder to demo) until a quality-churn or scalability event forces a reactive surge. Boards should treat sustained zero or near-zero defensive allocation in a maturing product as a leading indicator of future quality issues — per the standard product-management argument (Marty Cagan and similar product-leadership writing), a healthy roadmap pays both growth and platform-health rent.
Formula
defensive_roadmap_pct = (roadmap_capacity_allocated_to_defensive_initiatives / total_roadmap_capacity) × 100, where "defensive" includes reliability, security, compliance, scalability, parity, retention features, and tech-debt paydown. Complement of `offensive_roadmap_pct` (they sum to ~100% in a fully-classified roadmap).
Why it matters
Names the investment in not-losing alongside the investment in winning. A defensive % that responds to `quality_churn_pct` and `scalability_headroom` trends (rising when those degrade) is a sign of a healthy operating cadence; a defensive % stuck near zero while quality churn rises is a sign the board needs to push for re-prioritization.
Interpretation guidance
Industry folk-wisdom, not citation-grade: 20–40% defensive at growth-stage SaaS with stable platform health; 40–60% during platform-investment cycles; below 15% rarely sustainable in a maturing product. Read alongside `quality_churn_pct` — a defensive ratio that has not increased while quality churn has been rising for 2+ quarters usually warrants a board-level conversation about reprioritization.
Source
imboard Editorial
Related KPIs
product.offensive_roadmap_pct product.innovation_capacity_pct product.quality_churn_pct product.scalability_headroom product.key_initiatives_status

Time to Capacity Limit

product.scalability_headroom
number (months) Editorial
Description
Months of system capacity remaining at the current growth rate before the platform requires major (not incremental) infrastructure investment — typically driven by the binding bottleneck (database, message bus, single-tenant compute ceiling, regional capacity, or compliance-driven re-architecture). Surfaces the "scale runway" alongside the financial runway. Common pitfall: a single number hides which bottleneck binds. Boards should require the bottleneck to be named ("database shard hot-spot binds at ~150K accounts at current growth, ~4 months out"), not just the headline months — a named bottleneck makes the investment decision concrete.
Formula
Engineering-attested estimate: months_until_binding_bottleneck_at_current_growth_rate. Recompute when growth-rate assumption changes or when capacity work lands. Always surface the binding bottleneck name alongside the months number — `12 months (database write throughput)` not just `12`.
Why it matters
Sequences major infrastructure work against revenue growth. A 6-month scalability headroom against a 9-month financial runway is a foreseeable crisis the board should be addressing now. Pairs naturally with `defensive_roadmap_pct` (which funds the work).
Interpretation guidance
Industry folk-wisdom, not citation-grade: 12+ months of headroom is comfortable; 6–12 months means the rearchitecture project should be in flight; under 6 months means the project is critical-path and may already be late. Compare against `finance.runway_months` — financial runway shorter than scalability headroom means the company will hit the cash wall first; the inverse means the company will hit the scale wall first and should be planning the rearchitecture into the current operating plan.
Benchmark
p25 4 months · median 9 months · p75 18 months
Source
imboard Editorial
Related KPIs
product.defensive_roadmap_pct product.capacity_allocation_pct product.quality_churn_pct finance.runway_months sales.growth_rate_yoy

Top Product ARR Concentration

product.top_product_arr_concentration
percentage (%) Editorial
Description
Percentage of total ARR contributed by the single largest product line. Diversification-risk indicator at the product level (parallel to customer-concentration risk at the GTM level). Common pitfall: concentration risk is dismissed when the dominant product is performing well — but a one-product company is a one-feature-decision-away from existential risk. Boards should track this number alongside the portfolio narrative; sustained 70%+ concentration in a maturing company should pair with a documented diversification thesis or an explicit decision to remain a single-product company. Frames analogous to customer-concentration discussions in venture diligence (NfX / Bessemer founder essays cover the customer-side; the product-side analogue follows the same logic).
Formula
top_product_arr_concentration_pct = (arr_from_largest_product / total_arr) × 100. Define "product" explicitly — a SKU, a billable module, or a packaging unit — and hold the definition stable. Surface both the percentage and the named top product.
Why it matters
Quantifies single-point-of-failure risk in the product portfolio. The board reads this alongside `portfolio_strategy` to assess whether the company has a real second product or is effectively still single-SKU. Drives both strategic (build / buy / partner for diversification) and financial (valuation framing) conversations.
Interpretation guidance
Industry folk-wisdom, not citation-grade: concentration of 60–80% in the flagship product is common at early-growth multi-product SaaS; sustained 90%+ should be paired with an explicit single-product-strategy thesis or a documented diversification plan. The trend matters most — concentration falling from 95% to 70% over 4–6 quarters signals successful diversification; rising concentration during a multi-product strategy is a flag the secondary products are underperforming.
Source
imboard Editorial
Related KPIs
product.portfolio_strategy product.offensive_roadmap_pct sales.arr customers.gross_revenue_retention

Total Engineers

product.total_engineers
number Editorial
Description
Headcount of engineers (software, infrastructure, security, data, ML) in the R&D organization, typically including full-time employees plus contractors at a defined FTE-equivalence factor. The "capacity input" side of all R&D ratios. Common pitfall: definition drift. Some companies include only software engineers, others include product managers and designers, others include all of R&D plus QA, plus support engineers. Boards should anchor the definition once and hold it stable — otherwise quarter-over-quarter comparisons are noise. Pair with `rd_monthly_spend` to derive fully-loaded cost-per-engineer.
Formula
Count of engineering headcount (FTE + contractor FTE-equivalence). Define inclusion explicitly: SWE-only vs SWE+PM+Design vs all-of-R&D-including-QA. Hold the definition stable across quarters; surface the definition in a footnote.
Why it matters
Capacity denominator for every R&D ratio — `rd_efficiency`, ARR-per-engineer, cost-per-engineer, throughput-per-engineer. The board reads this to gauge whether team growth is keeping pace with revenue and product-surface-area growth.
Interpretation guidance
No single benchmark; the right number depends on product complexity, business model, and platform vs. point-solution architecture. The SaaS Capital Annual Survey reports revenue-per-employee for its private SaaS panel (see revenue-per-employee section of the latest edition) — pair `total_engineers` with company-wide headcount and ARR to derive both engineer-density (engineers ÷ total headcount) and ARR-per-engineer. Industry folk-wisdom, not citation-grade: engineer density of 25–40% is typical at product-led growth-stage SaaS; lower at sales-led, higher at infrastructure / platform companies.
Source
imboard Editorial
Related KPIs
product.rd_monthly_spend product.rd_efficiency product.capacity_allocation_pct product.innovation_capacity_pct hr.arr_per_fte

Weighted Feature Adoption

product.feature_adoption
percentage (%) Editorial
Description
Percentage of customers (weighted by ARR) actively using a defined set of strategic features within a measurement window. The "ARR-weighted" framing matters: a feature used by 30% of customers covering 70% of ARR is a different signal than 30% of customers covering 5% of ARR. Common pitfall: defining adoption as "ever used" rather than "actively using" (returning use in the measurement window) — the first metric only goes up and tells the board nothing. Boards should require an active-use definition (e.g. used in 2 of the last 4 weeks) and a per-feature breakdown for the strategic feature set.
Formula
weighted_feature_adoption_pct = Σ (customer_arr × is_actively_using_feature) / Σ (customer_arr) × 100, where "actively using" is defined explicitly (e.g. ≥2 sessions in the last 4 weeks, or domain-appropriate usage threshold). Weight by ARR — not by customer count — to surface the strategic-account signal.
Why it matters
Leading indicator of product-market fit for new capabilities. Adoption that does not reach a critical mass of ARR-weighted customers within 2–3 quarters is the strongest signal that the feature is either mis-targeted, mis-priced, or hidden in the UX. Drives roadmap continue-vs-cut decisions.
Interpretation guidance
Industry folk-wisdom, not citation-grade: for a strategic feature, 30–50% ARR-weighted adoption within 6 months is healthy; below 20% after 6 months usually warrants a retrospective. The product-management literature (Marty Cagan, "INSPIRED"; Pendo / Amplitude product-analytics playbooks) consistently emphasizes the active-use definition over cumulative reach, but does not publish citation-grade numeric ranges by company stage. Always pair this with `quality_churn_pct` — high adoption that coincides with rising quality-churn means the feature is shipping pain alongside use.
Benchmark
p25 40 % · median 60 % · p75 75 %
Source
imboard Editorial
Related KPIs
product.quality_churn_pct product.offensive_roadmap_pct product.portfolio_strategy sales.arr customers.net_revenue_retention

Operations 1 KPI

Rule of 40

operations.rule_of_40
percentage (%) Industry-backed
Description
Composite SaaS health score that sums the company's revenue growth rate and a profitability proxy (commonly EBITDA margin or free-cash-flow margin) into a single percentage. Originally articulated by Brad Feld in 2015 and codified by the SaaS Metrics Standards Board, the rule frames the growth-vs-profitability tradeoff: a company growing at 60% with a −20% margin scores 40, equal to a company growing at 20% with a +20% margin. The board reads it to sanity-check whether growth is being bought at unhealthy burn or whether margin discipline is constraining growth too far. Common pitfall: which profitability proxy is used materially changes the score (FCF margin is the strictest, EBITDA more flattering, "operating margin" inconsistently defined), so pick one and disclose it next to the number.
Formula
Rule of 40 = revenue_growth_rate (%) + profitability_margin (%). Per SMSB, `revenue_growth_rate` is typically YoY ARR or revenue growth; `profitability_margin` is typically EBITDA margin or FCF margin (disclose which). Both inputs are percentages — the output is also a percentage and can be negative when negative margin overwhelms growth.
Why it matters
Single-number readout of the growth-vs-burn tradeoff. Lets the board compare a high-growth / high-burn company to a slow-growth / profitable one on one axis, and surfaces unhealthy growth (high growth paid for with margin much worse than negative growth-rate offset).
Interpretation guidance
Per the rule as originally framed by Brad Feld (2015) and the SaaS Metrics Standards Board, a score at or above 40% is the canonical "healthy" threshold for growth-stage SaaS; below 40% signals either growth or margin is under-delivering. Finer stratifications often cited (>50% strong, >60% best-in-class) are industry folk-wisdom, not citation-grade. Always disclose which profitability proxy is used — comparing an EBITDA-margin Rule of 40 to an FCF-margin Rule of 40 is apples-to-oranges and a frequent board-deck error.
Benchmark
p25 -4 % · median 15 % · p75 31 %
Related KPIs
sales.growth_rate_yoy sales.gross_margin finance.net_burn_rate finance.runway_months
Domain
Tier
Showing 211 of 211 KPIs

Fundraising23 KPIs

Committed Amount

fundraising.committed_amount
currencyEditorial
Description
Capital that investors have agreed to invest — including both soft commitments (verbal / handshake / IOI) and hard commitments (signed term sheet or executed subscription docs). Treat this as the round-progress odometer. Common pitfall: soft commitments are notoriously squishy — every published fundraising postmortem (per First Round Review and Bessemer founder essays) warns that founders over-count soft commits. Board-best-practice is to track soft vs hard separately or to define a haircut convention (e.g. 50% of soft) at the start of the round.
Why it matters
Primary leading indicator for whether the round will close on target and on time. Pacing against `target_raise` and `planned_close_date` tells the board whether intervention is needed.
Source
imboard Editorial

Founder Dilution

fundraising.founder_dilution
percentage (%)Industry-backed
Description
Percentage of founders' fully-diluted ownership that is given up in the new round, including any pre-close option-pool top-up (the "option pool shuffle" — option-pool expansion taken in the pre-money dilutes existing holders rather than new investors). Common pitfall: founders often quote the "investor dilution" (new money / post-money) and forget the option-pool top-up component. The Carta State of Private Markets quarterly reports publish stage-typical dilution ranges that boards should use as a sanity check.
Why it matters
Tracks founder skin-in-the-game over time — sustained ownership matters for long-term motivation and signaling to future investors. Boards balance dilution discipline against capital needs.
Benchmark
p25 12% · median 18% · p75 24%
Source
Carta State of Private Markets Q3 2025

Fundraising Assumptions

fundraising.assumptions
textEditorial
Description
Explicit assumptions underlying the fundraising plan: valuation expectation, lead-investor probability, time-to-close, post-close runway, and what changes if any assumption breaks. Common pitfall: assumptions are made implicitly and only surface in the postmortem. Boards should require this section to be reviewed each update — a board update where assumptions never change suggests they are not being tested, not that they are correct.
Why it matters
Anchors the fundraising plan to falsifiable beliefs. Lets the board pre-agree on what would constitute a "this is not working, change the plan" trigger.
Source
imboard Editorial

Fundraising Risk Factors

fundraising.risk_factors
textEditorial
Description
Named risks that could prevent the round from closing as targeted — market conditions (general venture sentiment, sector-specific freeze), investor-side risk (anchor investor wobble, partner-meeting drop-off), company-side risk (a metric trending wrong direction, customer concentration concern surfaced in diligence), and timing risk (runway versus close date). Common pitfall: optimistic CEOs under-report risk factors. Boards should expect at least 2–3 named risks even in a healthy round — "no risks" is itself a risk signal.
Why it matters
Surfaces what could go wrong before it does — boards earn their seat by spotting risks the CEO is too close to see. Also a contract between CEO and board on what to watch.
Source
imboard Editorial

Fundraising Strategy

fundraising.strategy
textEditorial
Description
Free-text narrative covering the planned fundraising approach for the current round: target investor types (lead profile, co-investors), timing, sequencing of the conversation, use of proceeds, milestones the round will get the company to, and the alternative scenarios if the primary plan slips. This is the "what is the CEO actually doing" section of the fundraising update. Common pitfall: strategy that does not name a target lead investor profile or use-of-proceeds milestone is not strategy — it is intent. Boards should push for specificity here.
Why it matters
Forces the CEO to articulate "what game we are playing" — boards offer better help when they understand the strategy, not just the numbers.
Source
imboard Editorial

Investors in Pipeline

fundraising.investors_in_pipeline
numberEditorial
Description
Count of distinct investors actively engaged in the current round — defined as taken a first meeting and not yet declined or fully committed. Effectively a fundraising-funnel "qualified leads" number. Common pitfall: rosy pipelines that include investors who ghosted weeks ago — best practice (echoed across NfX, First Round Review, and Bessemer founder essays) is to age-out any investor with no contact in 14+ days. Track separately from total intros taken and from hard commitments to make the conversion math legible.
Why it matters
Healthy round dynamics rest on competitive tension — a thin pipeline means weaker negotiating position on price and terms. Board reads this to gauge whether the CEO needs help with intros.
Source
imboard Editorial

Key Milestones

fundraising.key_milestones
textEditorial
Description
Container handle for the field-array of named fundraising milestones the board should track to the close — each entry tracks milestone name, type (e.g. term-sheet signing, IC presentation, close), target date, status (upcoming / in-progress / completed / at-risk), responsible party, and notes. The "what has to happen, by when, and who owns it" surface that turns the round narrative into a tracked plan. Renders via the CollapsibleFormItemCardGallery widget (the reused gallery pattern shared with sales pipeline deals and HR key hires / openings). Common pitfall: milestones carried forward from prior packs without status updates — these should be refreshed each period so the board sees real progress, not a stale wishlist.
Why it matters
Converts the round narrative into a tracked plan with owners and dates — the board can see at a glance which milestones are slipping and who to press. Without named-milestone visibility, the board only learns of a stalled round when the close date slips.
Source
imboard Editorial

Minimum Close Amount

fundraising.minimum_close_amount
currencyEditorial
Description
Floor — the smallest amount of committed capital required to legally close the round (often set in the subscription agreement) or the strategically smallest amount management would accept before re-pricing or pausing. Common pitfall: a `target_raise` of $10M and a `minimum_close_amount` of $4M tells a very different story than a target of $10M and a minimum of $9M — boards should always see both. Per common practice (NVCA Model Documents allow flexibility here), the minimum is typically 50–75% of target at seed, 70–90% at A+.
Why it matters
Defines the round's "this is enough to ship" line. Pacing relative to the minimum is the worst-case board view; pacing relative to target is the best-case view — both matter.
Source
imboard Editorial

Minimum Valuation

fundraising.minimum_valuation
currencyEditorial
Description
The lowest pre-money valuation management would accept to close the current round — the valuation walk-away floor. Distinct from the precise NVCA-defined `pre_money_valuation` (the single negotiated point that actually prices the round): this is the bottom of the acceptable band the team set going in. Common pitfall: teams anchor only on a target valuation and have no pre-agreed floor, so in a soft market they negotiate against themselves with no board-sanctioned line. Pair with `fundraising.target_valuation` to give the board the band, and read both against stage-relative ranges from quarterly Carta / PitchBook reports.
Why it matters
Gives the board the worst-case price of the round before negotiations start — the line below which the team should pause, re-scope, or consider a bridge rather than accept a down-round-anchoring price. Without a pre-agreed floor, valuation discipline erodes in a soft market.
Source
imboard Editorial

Outstanding Convertible Amount

fundraising.convertible_outstanding
currencyIndustry-backed
Description
Total principal value of SAFEs and convertible notes outstanding that have not yet converted to equity. These convert at the next priced round, typically at a discount or valuation cap (per the standard Y Combinator SAFE templates and the National Venture Capital Association convertible-note model). Common pitfall: a SAFE stack quietly accumulating between rounds can convert into 15–25% dilution at the next priced round, surprising founders who modeled "we only sold 10% in this priced round" math. Boards should always see the fully-diluted cap table including SAFE conversion.
Why it matters
Hidden dilution that hits at the next priced round. A material SAFE stack changes the math on what a "20% Series A" actually costs the founders.
Source
Y Combinator Post-Money SAFE (2018+ standard form)

Planned Close Date

fundraising.planned_close_date
dateEditorial
Description
Calendar date by which the round is expected to close (final wires received, definitive documents signed). Compared against `finance.runway_months` to detect a fundraising-against-the-clock situation. Common pitfall: planned close dates routinely slip 30–90 days in practice (collected founder postmortems on First Round Review) — boards should ask for both an "expected" and a "no-deal" date and watch the gap to actual runway exhaustion.
Why it matters
Single most-important fundraising deadline — drives urgency, board cadence, and bridge-financing decisions. Slippage here is the leading indicator that the round is in trouble.
Source
imboard Editorial

Post-Money Valuation

fundraising.post_money_valuation
currencyIndustry-backed
Description
Company valuation immediately after the new round closes, including the new capital raised — the canonical "valuation" number quoted in TechCrunch headlines. Per NVCA Model Documents, post-money = pre-money + new money raised. Common pitfall: post-money math gets messy with SAFEs — modern post-money SAFEs (the YC 2018+ form, per the Y Combinator SAFE primer) fix dilution at the SAFE's valuation cap regardless of subsequent priced-round pricing, so the board should always reconcile the headline post-money against the fully-diluted cap table.
Why it matters
The headline number the company carries forward — sets the goalposts for the next round (a down-round means raising at a lower post-money) and the strike-price floor for new option grants.
Source
NVCA Model Legal Documents (2024 revision)

Pre-Money Valuation

fundraising.pre_money_valuation
currencyIndustry-backed
Description
Company valuation negotiated with investors immediately before the new round closes — the denominator for the new investors' ownership math. Per the NVCA Model Documents, pre-money = post-money − new money raised. Common pitfall: when convertible instruments (SAFEs, notes) are outstanding, the "headline" pre-money the CEO quotes and the effective pre-money after conversion can differ materially — the board should always ask for both. Equally important: option-pool top-ups taken pre-close come out of the pre-money share count, diluting founders not investors (the "option pool shuffle").
Why it matters
Sets the price for the round. Drives `founder_dilution`, the option-pool top-up math, and the precedent for the next round (down-rounds are punishing to recover from).
Source
NVCA Model Legal Documents (2024 revision)

Round Completion %

fundraising.round_completion_pct
percentage (%)Editorial
Description
Progress of the round expressed as committed capital divided by target. Read alongside `round_status` and elapsed-time-in-round to detect stalls. Common pitfall: percentage progress is misleading when measured against a shifting `target_raise` — when management lowers the target mid-round, the percentage jumps without any new commitments arriving. The board should always be told when this is a target revision vs. a real progress event.
Why it matters
Single-number pacing signal — board members glance at it first when scanning a fundraising update. Pairs naturally with elapsed-time-in-round to surface stalls.
Source
imboard Editorial

Round Status

fundraising.round_status
textEditorial
Description
Current phase of the active fundraising round on a coarse state machine (e.g. not-started, in-progress, term-sheet, closing, closed). The board reads this to know which playbook applies — pipeline-building, diligence, closing, or post-close communications. Common pitfall: the field drifts when a round stalls or pivots, so treat each phase change as a board-update trigger. The PhasePlaybook widget binds to this enum and surfaces the appropriate phase guidance read-only beside the editor.
Why it matters
Anchors every other fundraising number in board context — the same target_raise is read differently mid-pipeline than at closing. Drives which phase playbook the board should be advising on.
Source
imboard Editorial

Target Raise

fundraising.target_raise
currencyEditorial
Description
Target gross capital the company intends to raise in the currently active round (the "ask"). This is the headline number the CEO walks investors through and the board uses to sanity-check dilution and runway implications. Note the distinction from `total_round_size` (which can include third-party participation beyond the company-led ask) and from `minimum_close_amount` (the floor at which the round can close). Common pitfall: the target is updated mid-process when investor demand or strategy shifts — every change deserves a board note.
Why it matters
Defines the contract between management and the board for this round — every downstream KPI (round_completion_pct, founder_dilution, runway extension) is calibrated against it.
Source
imboard Editorial

Target Valuation

fundraising.target_valuation
currencyEditorial
Description
The pre-money valuation the current round is being run to land — the valuation "ask" that anchors the pitch and the dilution math management is targeting. Distinct from `pre_money_valuation` (the precise NVCA-defined price the round actually closes at, known only once a term sheet is signed): this is the aim, set going in. The board reads the target alongside `fundraising.minimum_valuation` as a valuation BAND — the two together tell a very different story than a single point. Common pitfall: a target valuation set on 2021-vintage multiples in a compressed market; always sanity-check against current stage-relative ranges from quarterly Carta / PitchBook / SaaS Capital reports.
Why it matters
Defines the best-case price of the round and the dilution math management is targeting — every downstream economic KPI (`founder_dilution`, option-pool top-up) is calibrated against where the round actually lands relative to this aim.
Source
imboard Editorial

Total Capital Raised to Date

fundraising.total_capital_raised
currencyEditorial
Description
Cumulative gross equity capital raised across all prior rounds (and the current round in-progress). Treated as historical context — investors and board members look at this to gauge capital efficiency (capital raised vs. ARR achieved). Common pitfall: includes all equity but typically excludes convertible debt that has not converted, venture debt principal, and grants — be explicit about what is and is not included when the number is presented. Capital efficiency benchmarks (per KBCM, SaaS Capital, and Bessemer State-of-the-Cloud) compare `total_capital_raised` to current ARR — e.g. "$30M raised, $10M ARR" is efficient at A but lean at B+.
Why it matters
Tracks capital efficiency over time and frames the company's next-round narrative ("we raised $X to get to $Y ARR"). Investors and board members use this for stage-vs-traction sanity-checking.
Source
imboard Editorial

Total Received

fundraising.total_received
currencyEditorial
Description
Cash that has actually been wired and cleared the company's bank account from investors in the current round. This is the cash-in-the-bank version of `committed_amount`. Common pitfall: commitments do not pay the bills — wiring can lag commitments by weeks to months for the second / third closes, and a committed-but-not-received delta of $5M+ can quietly extend the runway forecast incorrectly. Reconcile this against `finance.total_cash_in_bank` increases each period.
Why it matters
The only line of capital the company can actually deploy — runway forecasts based on `committed_amount` rather than `total_received` are aspirational, not operational.
Source
imboard Editorial

Total Round Size

fundraising.total_round_size
currencyIndustry-backed
Description
Total new capital being raised in the current round across all participants — the lead, follow-on investors, employee/strategic allocations, and any side-letter pieces. This is the figure that goes into the post-money math. Common pitfall: companies sometimes confuse `total_round_size` with `target_raise` — the round size is final and used in valuation math, while the target is what management is aiming for and can move during the raise. Boards should expect a specific breakdown by investor when this number is reported.
Why it matters
Determines the round's post-money valuation and dilution math. Also signals investor concentration risk — a round with 80% from one investor differs structurally from a round with 5 equal participants.
Source
NVCA Model Legal Documents (2024 revision)

Venture Debt Available

fundraising.venture_debt_available
currencyEditorial
Description
Undrawn capacity remaining on existing venture debt facilities. Optionality the company can call on quickly without re-pricing. Common pitfall: availability is conditional — most facilities require continued covenant compliance, and an available line can be pulled or frozen by the lender if cash, ARR, or other covenants slip (per the Bessemer venture-debt content and Battery Ventures primer). The board should treat `venture_debt_available` as a soft commitment, not a hard one, until drawn.
Why it matters
Strategic optionality — drawable capacity is a buffer for unexpected burn or a bridge to the next round. But it is contingent on staying inside covenants, so the board needs both this number and `venture_debt_covenant_status`.
Source
imboard Editorial

Venture Debt Covenant Status

fundraising.venture_debt_covenant_status
textEditorial
Description
Stoplight state of the venture-debt facility covenants — typically minimum-cash, minimum-ARR or revenue, maximum-burn, customer-concentration, and material-adverse-change clauses (per the standard Bessemer / Battery Ventures venture-debt primers). A covenant trip can freeze the draw line, accelerate repayment, or both. Common pitfall: covenants are not always actively monitored between board meetings — drift between an internal forecast and a covenant threshold can cross the line silently. Boards should require monthly covenant headroom reporting when material debt is drawn.
Why it matters
A covenant trip can cascade into a liquidity crisis fast — frozen facility, accelerated repayment, MAC clause triggering. Board catches this only if it is on the dashboard explicitly.
Source
imboard Editorial

Venture Debt Drawn

fundraising.venture_debt_drawn
currencyEditorial
Description
Principal currently drawn from venture debt facilities (e.g. Silicon Valley Bank, Hercules Capital, Trinity Capital, Western Alliance, Bridge Bank facilities). Venture debt typically extends runway 6–12 months alongside the equity round — used well, it dilution-efficiently bridges to the next equity event; used poorly, it concentrates default risk into a single covenant covenant trip. Common pitfall: drawn debt creates interest expense and a repayment schedule that compresses runway in 18–24 months even though it extends runway today (per the Battery Ventures venture-debt primer and the Bessemer "venture debt playbook" series).
Why it matters
Drawn debt accelerates cash burn through interest plus principal amortization (typically 24–36 month amortization after a 6–18 month interest-only period). Misjudging the trade-off between dilution avoided and forced repayment is a common venture-backed startup failure mode.
Source
imboard Editorial

Sales48 KPIs

ARR

sales.arr
currencyIndustry-backed
Description
Annual Recurring Revenue — the value of all recurring subscription revenue normalized to a one-year run-rate as of the period close. The headline operating metric for a subscription business; every growth and efficiency ratio (NRR, GRR, magic number, CAC payback, Rule of 40) is calibrated against it. Excludes one-time fees, professional services, and non-contractual usage. Common pitfall: confusing ARR (contracted recurring) with revenue (recognized) or with CARR (contracted incl. not-yet-live) — the SMSB standard draws sharp lines between them, and boards expect the same discipline. The KpiVarianceTable widget surfaces forecast / actual / variance / status / future-forecast columns against the same field.
Why it matters
Headline operating number that every other SaaS metric calibrates against — growth rate, efficiency ratios (CAC ratio, magic number, Rule of 40), retention math (NRR, GRR), and valuation multiples all read off ARR. Boards use the period-over-period ARR delta as the first-pass health check for the business.
Source
SaaS Metrics Standards Board

Average Contract Value

sales.avg_contract_value
currencyIndustry-backed
Description
Average annualized contract value across new-customer deals signed during the period (ACV). Defines where the company plays on the SaaS deal-size spectrum and dictates the operating model — high-ACV businesses tolerate longer sales cycles and direct sales motions; low-ACV businesses must run product-led or inside-sales motions to keep CAC payback short. Common pitfall: blending new and expansion ACV obscures the new-logo deal-size trend that boards actually want to see. Anchored to KBCM/Sapphire SaaS Survey 2024 §Average Contract Value for cross-company benchmarking.
Why it matters
Sets the cost ceiling for the sales motion — at $5k ACV the company cannot afford a field sales team; at $250k ACV inside sales alone usually leaves money on the table. The board uses ACV trend to validate up-market or down-market strategy bets.
Benchmark
p25 25000$ · median 62000$ · p75 100000$
Source
KBCM/Sapphire SaaS Survey 2024 (15th Annual)

Average Deal Size

sales.average_deal_size
currencyEditorial
Description
Mean dollar value across active pipeline opportunities (Pipeline Value / Pipeline Deal Count). Distinct from sales.avg_contract_value (ACV) which measures closed-won deals — average_deal_size is forward-looking pipeline-shape, ACV is realized output. Common pitfall: a few oversized deals materially skew the average — always inspect median_deal_size alongside; a large gap between average and median signals a few mega-deals that drive most of the projected number, which concentrates pipeline risk.
Why it matters
Forward-looking signal for ACV mix-shift before it appears in closed-won numbers — pipeline-size trending up usually shows up in closed-won-size 1–2 cycles later. Boards use the lead-time to ask "is this an intentional up-market move or a mix drift to correct?"
Source
imboard Editorial

Average Sales Cycle (Days)

sales.avg_sales_cycle_days
number (days)Editorial
Description
Average number of days from opportunity creation to closed-won status — measured only on won deals (lost deals are tracked separately). The motion-velocity metric — directly determines how much pipeline coverage is needed, how quickly investment in new reps pays back, and how feedback loops on packaging or pricing experiments compound. Common pitfall: blending segment cycles (SMB and Enterprise often differ 5–10×) into a single average hides material trend signals — segment-cut the metric where deal-volume permits.
Why it matters
Determines required pipeline coverage (a 90-day cycle needs ~1 quarter of forward pipeline; a 270-day cycle needs ~3 quarters), and is the leading indicator of ICP fit — strong fit shortens cycles; mismatched fit lengthens them.
Benchmark
p25 40days · median 84days · p75 150days
Source
imboard Editorial

Blended CAC Ratio

sales.blended_cac_ratio
numberIndustry-backed
Description
Total fully-loaded S&M spend in the period divided by the dollars of new CARR generated in the period (new-customer + expansion CARR combined). Per the SMSB standard, the headline efficiency ratio for the full sales-and-marketing motion — answers "how many cents do we spend on S&M to add one dollar of contracted ARR." Common pitfall: blending without separately reporting New CAC Ratio and Expansion CAC Ratio hides which side of the motion is driving efficiency — for a healthy SaaS company expansion CAC is usually 3–5× cheaper per dollar than new-logo CAC.
Why it matters
The portfolio-level efficiency number — one ratio that summarizes the full S&M engine. Boards use it to track quarter-over-quarter efficiency improvement as the motion matures.
Source
SaaS Metrics Standards Board

Bookings Backlog

sales.bookings_backlog
currencyEditorial
Description
Total value of signed contracts that have not yet been recognized as revenue — future revenue locked into the books. Equivalent to "remaining performance obligation" (RPO) in public-SaaS disclosures, though private companies often track only the in-period portion. Board reads this as the visibility horizon: a healthy backlog means recognized revenue is largely already-sold and not dependent on Q-end heroics. Common pitfall: confusing backlog with pipeline — backlog is contractually committed, pipeline is unsigned opportunity. Surface the two on the same dashboard but never sum them.
Why it matters
The single best read on next-period revenue predictability — high backlog means the revenue line for the coming quarter is largely contractual, not pipeline-dependent. Boards use it to gauge whether the team is selling for in-quarter close or building durable forward visibility.
Source
imboard Editorial

Bookings Backlog Changes

sales.bookings_backlog_changes
textEditorial
Description
Structured bridge that reconciles opening bookings backlog to closing backlog through the period's new bookings, conversions to revenue, post-contract losses, and value adjustments (starting + new − converted − lost + increases − decreases = ending). The bespoke sales card reads this typed object to show the backlog motion. Distinct from the editor's `sales.bookings_backlog_total` FlowSubform container — this is the typed `IBookingsBacklog` the feed card consumes. Common pitfall: an ending value that does not reconcile because conversions to recognized revenue were not netted out.
Why it matters
Makes signed-but-not-yet-recognized revenue auditable — a growing backlog is forward revenue visibility; a shrinking one (conversions outpacing new bookings) is an early top-of-funnel warning.
Source
imboard Editorial

Bookings Backlog Total

sales.bookings_backlog_total
currencyEditorial
Description
Total dollar value of all signed contracts that have not yet been recognized as revenue — the visibility window into future revenue at a point in time. Closely related to sales.bookings_backlog; this entry serves as the FlowSubform `start` slot for the per-period bookings-backlog flow (open + new bookings − recognized − cancellations = close). Common pitfall: omitting cancellations from the flow leaves a phantom backlog that overstates future revenue visibility — every backlog flow needs an explicit cancellation line even when zero.
Why it matters
Quantifies how much of forward revenue is already contracted — high ratios of backlog to forward plan = high revenue predictability. Boards use it to assess whether the business has visibility or is running quarter-to-quarter on pipeline conversion.
Source
imboard Editorial

CAC Payback Period

sales.cac_payback_period
number (months)Industry-backed
Description
Number of months required for the gross profit generated from a new customer's ARR to recover the fully-loaded S&M spend used to acquire them. The single most decision-useful efficiency metric at the board level — it directly connects acquisition cost, ACV, and gross margin into one "how long until we break even on this customer" answer. Per the SMSB standard, the calculation must use gross-margin-adjusted ARR in the denominator (not raw ARR) to be cross-company comparable. Common pitfall: using raw ARR understates payback by ~25–30 percentage points and breaks comparability with peer benchmarks.
Why it matters
The decision-relevant single number for "is the acquisition motion working" — sub-24 months signals capital-efficient growth; > 36 months means each dollar of S&M is locking up cash for too long to justify scaling spend.
Source
SaaS Metrics Standards Board

CARR

sales.carr
currencyIndustry-backed
Description
Contracted Annual Recurring Revenue — recognized MRR × 12 plus the annualized value of contracts that are signed but not yet live (i.e. implementation, ramp, deferred-start). Per the SMSB standard, CARR sits between ARR (live only) and pipeline (unsigned) on the revenue-certainty spectrum: contractually committed but not yet delivered. Boards reading CARR > ARR gap can quantify the in-flight implementation backlog and the leading indicator of next-period ARR. Common pitfall: counting verbal commitments or LOIs as CARR — only signed contracts qualify under the SMSB definition.
Why it matters
A leading indicator that ARR alone misses — if CARR is growing faster than ARR, an implementation backlog is building and ARR will accelerate as those contracts go live. Boards use the CARR-to-ARR ratio to interrogate the implementation engine.
Source
SaaS Metrics Standards Board

Churned ARR

sales.churn_arr
currencyEditorial
Description
Annualized recurring revenue lost during the period from customers who fully cancelled — terminating their contract or letting it lapse without renewal. The "leak" line of the ARR waterfall and the denominator of Gross Revenue Retention. Distinct from Downgrade ARR (sales.downgrades) which captures contractions where the customer stays. Common pitfall: lumping mid-term cancellations with non-renewals masks two very different retention failures — surface them separately when material. The KpiVarianceTable widget tracks period forecast vs actual; a widening miss against forecast is the earliest signal of a retention problem.
Why it matters
Direct read on Gross Revenue Retention (GRR) — the floor of the retention math, since downgrades and churn cannot be offset by upsell in GRR. A board can tolerate slow new-logo growth if churn is low, but cannot tolerate high churn at any growth rate — it compounds against valuation.
Source
imboard Editorial

Competitive Alerts

sales.competitive_alerts
textEditorial
Description
Narrative read on competitive dynamics affecting the sales motion — material wins / losses to specific competitors, observed pricing or packaging moves in the market, new entrants, M&A in the competitive set. Boards use this surface to bring outside intelligence (their other portfolio companies, advisors) to bear on the competitive picture. Common pitfall: listing competitor names without quantifying how often they show up in deal cycles — a "Competitor X is being aggressive" entry without "we saw them in 8 of 20 active deals last quarter, up from 3 of 18" is too vague to act on.
Why it matters
Competitive intelligence is the most under-shared information in board packs and the most useful for cross-portfolio learning — boards can validate or refute observations from other companies they sit on.
Source
imboard Editorial

Customer Acquisition Cost

sales.cac
currencyIndustry-backed
Description
Fully-loaded sales-and-marketing (S&M) expense incurred to acquire one new customer during the period. Per the SMSB standard, the CAC numerator includes salaries + commissions + benefits + travel + marketing programs + tooling — i.e. all S&M costs, not just direct-attribution paid acquisition. The denominator is new logos, not deals. Common pitfall: omitting fully-loaded comp (especially BDR/SDR base salary and CS-team cost-of-sale where they participate in expansion) understates CAC and inflates every downstream efficiency metric. The board cares about CAC alongside CAC Payback and the CAC Ratio family — single-number CAC is a building block, not a verdict.
Why it matters
The cost side of the customer-unit economics ledger — paired with ACV and gross margin, determines whether each customer is a profitable transaction over a reasonable horizon. Boards read CAC alongside payback period before debating S&M investment levels.
Source
SaaS Metrics Standards Board

Deals Lost

sales.closed_lost_count
numberEditorial
Description
Count of opportunities that transitioned to closed-lost during the period — the volume side of pipeline disqualification. The other half of the win rate denominator; without tracking it explicitly you cannot compute or benchmark win rate. Common pitfall: stale "open" deals that should be marked lost are left open, inflating pipeline value while suppressing the lost count — a hygiene problem that compounds because next-period coverage looks fine while win rates silently degrade. Every CRM hygiene policy should specify a max-age before deals auto-flag for lost-or-update review.
Why it matters
Denominator input for win rate and direct read on pipeline hygiene. Cluster analysis of loss reasons feeds product / pricing / positioning decisions that boards expect to see referenced in strategic_context.
Source
imboard Editorial

Deals Lost Value

sales.closed_lost_value
currencyEditorial
Description
Total dollar value of opportunities closed-lost during the period — the opportunity-cost view on the pipeline motion. Useful for sizing the "what we missed" gap and prioritizing post-mortem efforts on the highest-value losses. Common pitfall: post-mortems on small lost deals waste time relative to insight; tier the post-mortem cadence by value (e.g. every loss above the 80th-percentile deal size gets a written debrief). Boards expect the largest 2–3 losses to be explained explicitly in commentary.
Why it matters
Quantifies the realized opportunity cost — useful for justifying packaging changes, ICP refinement, or product investment that would have closed specific tier-1 losses. Drives loss-reason prioritization.
Source
imboard Editorial

Deals Summary (Won / Lost)

sales.deals_summary
textEditorial
Description
Container handle for the period's notable deals split into WON and LOST arrays — each deal carries name, account, amount, owner, deal type, source, and competitor, plus a win reason + close date (won) or a loss reason (lost). The bespoke sales feed card renders this as the "Notable Deals" won/lost breakdown the demo design shows. This is RICHER than the flat `sales.pipeline_key_deals` editor gallery (which has no won/lost split, reason, or close date). Common pitfall: carrying the same list forward each quarter — refresh to the actual period's closes.
Why it matters
Turns the quarter's win/loss outcomes into board-readable narrative — why deals were won (superior product / service) and lost (features / price / competitor) is the qualitative signal raw pipeline numbers miss.
Source
imboard Editorial

Deals Won

sales.closed_won_count
numberEditorial
Description
Count of opportunities that reached closed-won status during the period — the volume side of the period's sales output. Paired with closed_won_value gives the period's average won-deal size, a critical mix-shift indicator. Common pitfall: counting opportunity stage transitions rather than discrete deal closes (re-opened deals inflate the count). Boards read the trend over 4+ quarters to detect motion-volume stability — sharp drops while pipeline holds usually mean late-stage conversion has broken.
Why it matters
The most direct sales-execution volume signal — separates "we sold lots of small things" from "we sold a few big things" when paired with deal-size lines. Inputs win rate and ASP analysis.
Source
imboard Editorial

Deals Won Value

sales.closed_won_value
currencyEditorial
Description
Total dollar value of all opportunities closed-won during the period — the period's realized bookings from the pipeline motion. Reconciles to (sales.new_business + sales.expansion) when split by deal type. Common pitfall: reporting TCV (total contract value) here when the rest of the dashboard uses ACV — pick one and apply it consistently across closed_won_value, weighted_forecast, and pipeline_value, or the dashboard math stops reconciling.
Why it matters
Realized bookings — the period's actual sales output. Sum across periods should reconcile to total new-customer + expansion CARR additions; gaps indicate either revenue-recognition policy or stage-data-quality issues.
Source
imboard Editorial

Downgrade ARR

sales.downgrades
currencyEditorial
Description
Annualized recurring revenue lost from existing customers who reduced spend mid-term or at renewal (seat reductions, tier downgrades, removed modules) — without leaving entirely. The "contraction" line of the ARR waterfall, distinct from full churn. Often a more sensitive leading indicator than churn because customers tend to contract before they cancel. Common pitfall: lumping downgrades into churn obscures the early-warning signal — boards looking only at logo churn miss the slow-bleed pattern. Surfaces in the KpiVarianceTable widget alongside expansion and churn so the net-retention math is auditable.
Why it matters
Earliest leading indicator of retention risk — customers usually contract before they cancel, so a rising downgrade line predicts churn 1–2 quarters out. Inputs NRR (subtracts from expansion) and CCO/CS comp models that gate on Net Retention.
Source
imboard Editorial

Expansion ARR

sales.expansion
currencyEditorial
Description
Annualized recurring revenue added during the period from existing customers — through upsell (more seats / higher tier), cross-sell (additional products), or price increases. The "farm" line of the ARR waterfall. Boards read this as the leading indicator that product-market fit has translated into product-account fit and that the post-sale motion is creating compound growth. Common pitfall: classifying contractual price-step-ups (CPI escalators baked into the original contract) as expansion overstates new selling motion. Expansion CAC Ratio and Net Revenue Retention are derived from this number.
Why it matters
A high expansion line is the single best predictor of capital-efficient compounding growth — the SaaS playbook depends on existing customers expanding faster than new ones churn. Drives NRR, which is the metric public-market investors weight most heavily on the retention side of the model.
Source
imboard Editorial

Expansion CAC Ratio

sales.expansion_cac_ratio
numberIndustry-backed
Description
Fully-loaded S&M plus Customer Success expense attributable to expansion divided by expansion CARR generated in the period. Per SMSB, the efficiency read on the upsell / cross-sell / land-and-expand motion. Distinct from the new-logo CAC ratio because the cost base often includes CSMs whose primary metric is retention but whose secondary metric is expansion — boards expect to see that allocation called out. Common pitfall: excluding CS comp entirely understates the true cost of expansion; including all of CS overstates it. The SMSB standard prescribes a documented allocation rule (typically tied to expansion-quota OTE share).
Why it matters
Validates the financial logic of "expansion is cheaper than acquisition" — when this is healthy, the company should bias growth investment toward post-sale; when it inverts (Expansion CAC ≥ New CAC), the expansion motion is broken and acquisition is the only available lever.
Source
SaaS Metrics Standards Board

Gross Margin

sales.gross_margin
percentage (%)Industry-backed
Description
Recognized revenue minus cost of goods sold (COGS), divided by recognized revenue, expressed as a percentage. The single best read on whether the business model can ever generate operating leverage — a low gross margin caps every downstream efficiency metric (CAC payback, LTV/CAC, Rule of 40). For SaaS, COGS includes hosting, third-party software, customer support, and customer-success cost-of-service. Common pitfall: omitting customer success from COGS inflates the margin and breaks comparability with peer benchmarks. Anchored to KBCM/Sapphire SaaS Survey 2024 §Gross Margin.
Why it matters
Caps every long-term efficiency metric — Rule of 40, LTV/CAC, CAC payback all run off contribution margin which derives from gross margin. Board uses it to verify the unit economics are real before debating S&M investment levels.
Benchmark
p25 65% · median 72% · p75 81%
Source
KBCM/Sapphire SaaS Survey 2024 (15th Annual)

Growth Rate (YoY)

sales.growth_rate_yoy
percentage (%)Industry-backed
Description
Year-over-year percentage growth in ARR (or recognized revenue, if explicitly anchored) — comparing the current period to the equivalent period 12 months prior. The single most-watched investor metric and the largest single driver of SaaS valuation multiples. Common pitfall: comparing to the prior quarter (QoQ) and reporting it as "growth rate" — boards and investors mean YoY unless explicitly noted otherwise. Anchored to KBCM/Sapphire SaaS Survey 2024 §YoY ARR Growth for cross-company benchmarking.
Why it matters
Direct input to public-comparable valuation multiples (EV / NTM ARR multiples are sliced by growth band). Boards use it to triangulate stage-appropriate pace and to flag deceleration early.
Benchmark
p25 12% · median 19% · p75 27%
Source
KBCM/Sapphire SaaS Survey 2024 (15th Annual)

Median Deal Size

sales.median_deal_size
currencyEditorial
Description
Median dollar value across active pipeline opportunities — the typical deal in the pipeline, robust against the few-big-deals skew that distorts the average. The honest read on the "core motion" deal-size; if the team is winning a few oversized deals but the median is shrinking, the underlying motion is degrading even though the topline numbers look fine. Common pitfall: omitting median in dashboards in favor of just the average lets concentration risk hide. A best-practice board pack always shows both.
Why it matters
The most honest read on the typical motion — distinguishes "we have a real scalable motion" (high median) from "we have a few oversized deals carrying everything else" (low median, high average).
Source
imboard Editorial

New Business ARR

sales.new_business
currencyEditorial
Description
Annualized recurring revenue booked from net-new logos (first-time customers) during the period. This is the "hunt" line of the ARR waterfall — the output of the new-customer acquisition motion, distinct from expansion (existing-customer upsell) and from churn / downgrades. Common pitfall: counting renewals or expansion deals as new business inflates the new-logo conversion engine and hides a stalled acquisition motion. The KpiVarianceTable widget shows period forecast vs actual; downstream views compare it to S&M spend to derive new-business CAC and CAC payback.
Why it matters
Direct read on the health of the new-customer acquisition engine — separates "are we winning new logos" from "are existing customers expanding." Inputs the New CAC Ratio and CAC Payback calculations the board uses to judge sales efficiency.
Source
imboard Editorial

New CAC Ratio

sales.new_cac_ratio
numberIndustry-backed
Description
S&M expense attributable to new-customer acquisition divided by the new-customer CARR generated in the period. Per SMSB, the cleanest read on the new-logo acquisition engine's efficiency — strips out the expansion motion which has materially different unit economics. Common pitfall: failing to split AE comp time correctly between new and expansion activities — when the same AE owns both motions, an allocation rule (often the % of OTE tied to new-vs-expansion quota) is required and must be applied consistently quarter-over-quarter.
Why it matters
Isolates the new-logo engine — when blended CAC Ratio is moving, this is the first line of split-out diagnosis. Boards use it to evaluate whether to invest more in acquisition or shift weight toward expansion.
Source
SaaS Metrics Standards Board

New Customers Added

sales.new_customers_added
numberEditorial
Description
Count of net-new logo customers signed during the period (a customer is a discrete buying entity — typically an account, not a seat). Paired with sales.new_business gives Average Selling Price (ASP) — a primary input to ICP / segment-fit conversations. Early-stage boards read the logo count as a sanity check on top-of-funnel and PMF before ARR-density grows enough to matter. Common pitfall: counting expansion deals or new contracts from existing customers as "new" inflates the acquisition signal — the count must match the same "first-time customer" criterion as New Business ARR.
Why it matters
Logo count is the most direct read on acquisition-motion volume before contract-value mix dominates the ARR view. Early-stage boards read it before ARR; growth-stage boards pair it with ASP to spot segment drift (e.g. up-market mix-shift where logo count falls while ARR rises).
Source
imboard Editorial

New Opportunities Added

sales.new_opps_added_value
currencyEditorial
Description
Total dollar value of new opportunities entering the pipeline during the period — the top-of-funnel inflow line in the pipeline flow. The single best read on the marketing-and-SDR engine's output. Common pitfall: counting inflated, un-qualified opportunities (e.g. every demo request) overstates the engine's output; restrict to opportunities that pass a defined qualification stage (typically SQL or higher) before counting. Boards expect this number to track forward quota — a quarter's top-of-funnel should be ~1× the same quarter's quota for a normal sales-cycle business.
Why it matters
Marketing/SDR output measured in dollars — directly determines whether future periods will have sufficient pipeline coverage. Trending down with stable conversion = future-period miss baked in 1–2 cycles out.
Source
imboard Editorial

Opening Pipeline Value

sales.opening_pipeline_value
currencyEditorial
Description
Total pipeline value at the start of the period — the baseline against which the period's pipeline flow (+ new opportunities − won − lost = closing) reconciles. Equal to the prior period's closing pipeline by construction. Surfaces in sales.pipeline_flow as the `start` slot. Common pitfall: restating opening pipeline to retroactively "clean up" stale deals masks the hygiene problem rather than addressing it; cleanup should happen via explicit "old-deal scrub" lines in the flow, not by editing the opening baseline.
Why it matters
Without an explicit opening line, the pipeline flow has no anchor and the additions / removals cannot be audited. Boards expect the flow to reconcile to the penny period-over-period.
Source
imboard Editorial

Pipeline Assumptions

sales.pipeline_assumptions
textEditorial
Description
Narrative documenting the key assumptions underlying the pipeline forecast — conversion rates by stage, expected sales-cycle length, segment-mix expectations, and any deal-specific dependencies (e.g. "we assume Acme renews their POC by end of month and signs the upgrade in Q3"). Common pitfall: leaving assumptions implicit makes the forecast non-falsifiable — if you don't list the assumptions, you can't identify which one broke when the forecast misses. Renders side-by-side with sales.pipeline_risk_factors in the TwoColumnTextarea widget (sales.pipeline_context_notes container).
Why it matters
Makes the forecast falsifiable and post-mortem-able — without an assumptions list, missed quarters get attributed to vague "execution" rather than specific assumption failures the next plan should correct.
Source
imboard Editorial

Pipeline Context Notes

sales.pipeline_context_notes
textEditorial
Description
Container handle for the side-by-side contextual notes — pairs sales.pipeline_assumptions (left slot) with sales.pipeline_risk_factors (right slot) in the TwoColumnTextarea widget. Visually positions the "what we're assuming" narrative directly next to the "what could break those assumptions" narrative, forcing the team to write them in concert (rather than as two independent surfaces that drift apart over quarters). Common pitfall: writing assumptions without their corresponding risks (or vice versa) means the forecast is incomplete — every assumption should pair to a risk factor that captures the failure mode.
Why it matters
Forces a discipline that significantly improves forecast quality — assumption / risk pairs are more useful than either alone because each risk has a sensitivity (how much the forecast moves if the corresponding assumption breaks).
Source
imboard Editorial

Pipeline Deal Count

sales.pipeline_deal_count
numberEditorial
Description
Total number of active opportunities in the pipeline (open stages only — excludes closed-won and closed-lost). The volume side of pipeline coverage; paired with pipeline_value gives the average deal size and the deal-count vs deal-size ratio that characterizes the motion shape. Common pitfall: counting non-bona-fide opportunities (orphaned trials, demo requests that never converted to a real evaluation) inflates the number — apply a stage-floor cutoff (e.g. SQL or higher) so the count reflects committed evaluation activity.
Why it matters
Volume-side health of the funnel — when value rises with falling count, deal sizes are growing (often deliberate up-market motion); when count falls without value compensation, top-of-funnel is the problem.
Source
imboard Editorial

Pipeline Flow

sales.pipeline_flow
textEditorial
Description
Container handle for the additive / subtractive pipeline-flow bridge — reconciles opening pipeline to closing pipeline through the period's adds, wins, and losses (opening + new_opps − closed_won − closed_lost = closing) with dual count + value columns. Renders via the FlowSubform widget. The audit trail of the pipeline motion — without this, period-over-period pipeline changes are unexplained. Common pitfall: a "scrub" line (deals reclassified from open to lost mid-period) is needed to keep the math reconciling when CRM hygiene happens; without it the flow appears not to balance and trust in the underlying numbers erodes.
Why it matters
Makes the period's pipeline changes auditable line-by-line — boards can immediately see whether closing pipeline shrank because deals closed (good) or because deals were lost / scrubbed (bad). Without the flow, only the net change is visible and the underlying motion is opaque.
Source
imboard Editorial

Pipeline Key Deals

sales.pipeline_key_deals
textEditorial
Description
Container handle for the field-array of key in-flight deals — each entry tracks deal name, current stage, dollar value, and confidence/commit status. Renders via the CollapsibleFormItemCardGallery widget (a reused gallery pattern shared with HR keyHires / keyOpenings). The "named deals the board should know about" surface — typically the top 5–10 deals by value or strategic importance. Common pitfall: a static list that doesn't reflect the current quarter — these should be refreshed each period to reflect actual top-of-mind deals, not carried forward from prior packs.
Why it matters
Concentrates board attention on the specific deals whose outcomes will determine the quarter — sales leaders often have valuable context (executive relationships, partnership levers) that only the board can deploy. Without named-deal visibility, board help on big deals happens reactively.
Source
imboard Editorial

Pipeline Quarterly Forecasts

sales.pipeline_quarterly_forecasts
textEditorial
Description
Container handle for the addable per-quarter forecast rows — each row tracks quarter, totalPipelineValue, weightedPipelineValue, expectedCloses (committed forecast), and dealCount. Rendered via the AddableQuarterlyForecastTable widget. Provides the multi-quarter forward visibility view the board reviews to validate the next 2–4 quarters of revenue, not just the current quarter. Common pitfall: filling in only the current quarter and treating future quarters as "we'll figure it out" — multi-quarter forecasting forces honest top-of-funnel planning for the periods beyond the immediate one.
Why it matters
Forward-quarter coverage view — the board needs to see whether next-quarter and next-next-quarter pipelines look credible, not just current. Many revenue misses are visible 2 quarters out if the multi-quarter pipeline view is honest; without this surface, the only data point is "current quarter looks ok."
Source
imboard Editorial

Pipeline Risk Factors

sales.pipeline_risk_factors
textEditorial
Description
Narrative listing the material risks to pipeline conversion or deal timing — specific deal slips, segment headwinds, budget freezes, competitive entry, ICP-fit misses on late-stage deals. Distinct from sales.key_concerns (which covers the whole sales motion) — this is specifically about the forecast / pipeline conversion math. Common pitfall: vague risks ("market is choppy") aren't actionable; a useful entry quantifies the at-risk dollar amount and names specific deals or segments. Renders side-by-side with sales.pipeline_assumptions in the TwoColumnTextarea widget.
Why it matters
Surfaces the forecast tail risk early enough for the board to engage — large-deal slip risks often have customer-side levers (CEO outreach, partnership offer) that only the board can pull. Without this surface those interventions happen reactively at quarter-end.
Source
imboard Editorial

Pipeline Stage Metrics

sales.pipeline_stage_metrics
textEditorial
Description
Container handle for the per-stage pipeline metrics grid — for each pipeline stage (qualification, discovery, evaluation, proposal, negotiation, closing) tracks dealCount, totalValue, closingProbability, winRateFromStage, and avgTimeToClose. The most diagnostic surface in the pipeline view: where deals are bunching, which stage is the bottleneck, where conversion math is breaking. Rendered via the StageMetricsGrid widget seeded from PipelineStageValues. Common pitfall: trusting unchanged stage probabilities even as the deal mix shifts — re-calibrate the per-stage close rates quarterly against actuals or the weighted forecast drifts unreliably.
Why it matters
Localizes pipeline problems to specific stages — flat pipeline value with a stage-2 buildup means lead-qualification is too loose; stage-5 stall means closing-skill or pricing-objection issues. Without this surface, the weighted forecast is opaque.
Source
imboard Editorial

Pipeline Value

sales.pipeline_value
currencyEditorial
Description
Sum of the dollar value of all active deals currently in the sales pipeline — unweighted (raw deal-value sum, not probability-weighted). Boards read this as the top-of-funnel sufficiency check: if pipeline coverage (pipeline value / forecast) drops below the historic conversion-rate-implied threshold, the forecast is at risk. Common pitfall: confusing pipeline value with weighted forecast — the unweighted number always exceeds the weighted, often by 3–5× depending on the stage mix. Always report both and the implied conversion ratio.
Why it matters
The capacity number for the forecast — without sufficient pipeline value, the forecast is structurally unachievable regardless of close-rate execution. Coverage ratio (pipeline / quota) is the first read on whether the team can hit the period.
Source
imboard Editorial

Quarterly Forecast

sales.quarterly_forecast
currencyEditorial
Description
The team's expected closed-won dollars for the current quarter — usually a sales-leader judgment call informed by weighted forecast but adjusted for deal-by-deal commit confidence. Distinct from weighted_forecast (which is mechanical, stage × probability). Boards read both: a quarterly_forecast materially below weighted_forecast means the team has explicit negative judgment on specific big deals; above it means they're calling deals stronger than the stage probabilities suggest. Common pitfall: anchoring the call to plan rather than reality — boards quickly learn to discount "we will hit plan" forecasts and reward calibrated commit-vs-actual track records.
Why it matters
The number the board commits against — quarter-end attainment vs this number is the primary execution scorecard. Track forecast-accuracy (forecast vs actual) over time to calibrate trust in the call.
Source
imboard Editorial

Recognized Revenue

sales.total_revenue
currencyEditorial
Description
Total revenue recognized under the company's accounting standard (ASC 606 / IFRS 15) during the period — distinct from billings (what was invoiced) and from ARR (an annualized run-rate snapshot). The income-statement top line and the basis for GAAP reporting. Common pitfall: confusing recognized revenue with ARR — for a company with mid-year contract starts, ARR exit will exceed recognized revenue for that year; the gap shrinks as the cohort matures. Boards reviewing a recognition-heavy investor pack should always see ARR alongside revenue to avoid mis-pricing growth.
Why it matters
The audited top line that anchors every GAAP-based valuation multiple, debt covenant, and tax filing. Boards need it to track the path to profitability (revenue − cost), which subscription ARR alone cannot show.
Source
imboard Editorial

Sales Base Currency

sales.base_currency
textEditorial
Description
The ISO-4217 currency code (e.g. `USD`) the sales/pipeline feed displays its money metrics in. The bespoke sales and pipeline feed cards read this to choose the currency symbol/format; absent it, they fall back to USD. This is a board-level reporting-currency constant rather than a measured metric. Common pitfall: leaving it unset on a non-USD board — the feed then silently renders USD symbols over non-USD figures.
Why it matters
Ensures the sales/pipeline feed renders money in the board's actual reporting currency instead of a silent USD default.
Source
imboard Editorial

Sales Cycle Quarter-to-Quarter

sales.pipeline_sales_cycle
textEditorial
Description
Container handle for the three-section quarter-over-quarter compare object that tracks average days-to-close trend (lastQuarter / thisQuarter / improvement). Renders via the QuarterToQuarterImprovementGrid widget with three slots. The "is the motion getting faster or slower" diagnostic — cycle length trend is one of the most reliable leading indicators of ICP fit and packaging quality. Common pitfall: comparing without controlling for deal-size mix — if up-market mix is shifting, a flat cycle is actually an improvement (because up-market cycles are inherently longer). Note the mix context in commentary if material.
Why it matters
Cycle compression compounds dramatically — a 20% reduction in cycle time roughly translates to a 20% capacity increase for the same headcount. Cycle expansion does the inverse and usually predicts future-period coverage stress.
Source
imboard Editorial

Sales Focus Areas

sales.focus_areas
textEditorial
Description
Forward-looking narrative naming the next-period (typically next-quarter) sales priorities — segment bets, pipeline-coverage actions, hiring focuses, enablement themes, ICP refinements. The "what we're changing or doubling-down on" surface, complementing strategic_context (which is past-tense) and key_concerns (which is present-tense). Common pitfall: listing too many focus areas (3 is the practical maximum a team can actually execute against; 7+ means everything is a priority, i.e. nothing is). Boards use this to track promise-vs-delivery quarter over quarter.
Why it matters
Creates accountability across periods — the board can ask "you said X was the focus last quarter, what happened?" Without an explicit list, every quarter looks like a fresh strategy reset.
Source
imboard Editorial

Sales Key Concerns

sales.key_concerns
textEditorial
Description
Free-text narrative of the critical issues, pipeline risks, or blockers in the sales motion that require board attention this period. Distinct from sales.pipeline_risk_factors (which is forecast-specific) — this is the full-stack sales-org concerns list including hiring, comp, churn-cluster patterns, large-deal slippage, and competitive losses. Common pitfall: under-reporting concerns because the team wants to show progress — boards explicitly invite this surface so they can help, and a board pack with no concerns surfaces is itself a yellow flag (either the team is hiding something or not introspecting deeply enough).
Why it matters
Lets the board pre-load the discussion topics that need their judgment or network — the most leveraged use of board time. Absent this surface, the conversation drifts to whatever board members notice in the numbers, which is rarely the highest-leverage issue.
Source
imboard Editorial

Sales Strategic Context

sales.strategic_context
textEditorial
Description
Executive-summary narrative for the sales section of the board pack — the CRO/CEO's one-screen synthesis of overall sales performance, market dynamics, and the story behind the quarter's numbers. Categorical state derived from operational reporting — no calculation. Renders via ExecutiveCommentary widget as multi-section tabbed prose with per-section word counts. Common pitfall: writing it as a numbers-recap repeats what the KPI table already shows; the goal is the connective tissue — why the numbers moved, what changed in the market, what the next 90 days look like. Boards read this first when scanning the deck.
Why it matters
Provides the interpretive frame that turns the raw KPI table into a story the board can debate. Without it, board members default to their own (often wrong) interpretation of the numbers.
Source
imboard Editorial

Starting ARR

sales.starting_arr
currencyEditorial
Description
Opening ARR at the beginning of the period — the baseline against which the period's ARR waterfall (new + expansion − downgrades − churn) reconciles to ending ARR. Equal to the prior period's closing ARR by construction. The FlowSubform widget binds starting_arr as the `start` slot of the ARR-bridge flow, and the ending position is computed as start + Σ(deltas). Common pitfall: restating starting_arr mid-period to "fix" a prior-period reporting error breaks the period-over-period audit trail; corrections should land as a separate restatement note, not by editing the opening balance.
Why it matters
The anchor of the ARR waterfall — without an explicit starting point, the period's net-new ARR cannot be audited. Boards expect the waterfall to reconcile to the penny, period over period.
Source
imboard Editorial

Weighted Pipeline Forecast

sales.weighted_forecast
currencyEditorial
Description
Total pipeline value with each deal multiplied by its stage-based close probability — the canonical probabilistic forecast number. More forecasting-useful than raw pipeline value because it accounts for the conversion-likelihood mix across stages (early-stage deals weighted ~10–25%, mid-stage ~40–60%, late-stage ~70–90%). Common pitfall: using globally-flat probabilities (e.g. always 50%) instead of stage-specific calibrated ones — a reliable weighted forecast requires the stage probabilities to be back-tested against actual close rates from prior periods.
Why it matters
The single most-cited number in the weekly forecast call — the team's probabilistic answer to "what will we close." Boards compare it to commit and quota to assess delivery risk.
Source
imboard Editorial

Win Rate

sales.win_rate
percentage (%)Editorial
Description
Percentage of closed opportunities that resulted in closed-won (vs closed-lost) during the period. The single best read on bottom-of-funnel execution and the most direct input to pipeline-coverage math (required coverage = 1 / win rate). Common pitfall: computing win rate without disqualifying "no decision" outcomes inflates losses and depresses the rate artificially; the SaaS norm is to either bucket no-decisions separately or track a two-rate view (raw win rate vs ICP-fit win rate excluding no-decisions). Stage-segment cuts (SMB vs Enterprise) usually differ 2×–4× and should be reported separately when volume permits.
Why it matters
Reciprocal of required pipeline coverage — a 25% win rate requires 4× pipeline coverage to hit quota. Drives capacity planning, quota setting, and the pipeline-coverage commit conversation.
Source
imboard Editorial

Customers26 KPIs

% ARR at Risk

customers.percent_arr_at_risk
percentage (%)Editorial
Description
Share of total ARR flagged as at-risk for churn or contraction — the proportional view that complements the absolute `arr_at_risk` dollar figure. Computed as `arr_at_risk ÷ total ARR`. The board reads this as the worst-case-near-term-NRR-impact ceiling: if every at-risk account actually churned in-period, NRR would drop by roughly this percentage (before expansion offset). Common pitfall: the "at-risk" definition is internal and varies by company — a 12% percent_arr_at_risk under a conservative flagging rule is a very different signal than 12% under an aggressive rule. Document the flag rule and hold it constant.
Why it matters
Normalizes the at-risk dollar figure so it scales with the business. A 10% at-risk share is the same proportional threat at $5M ARR as at $50M ARR; the absolute figure alone hides that.
Source
imboard Editorial

ACV Trend

customers.acv_trend_pct
percentage (%)Editorial
Description
Period-over-period percent change in Average Contract Value (mean ARR per active customer logo). A rising ACV trend signals pricing power, successful tier upgrades, or a mix-shift toward larger customers; a falling ACV trend signals seat compression, discounting pressure, or a mix-shift toward smaller customers. The board reads this alongside `total_customers` and `customers.net_revenue_retention` to disambiguate which lever is moving — logo growth vs. expansion vs. price. Common pitfall: ACV mix-shifts (a wave of new SMB logos at low ACV) can drag the average down even when existing-customer ACV is rising — segment-cut ACV is more diagnostic than the blended number.
Why it matters
Separates "more customers" from "bigger customers" in growth narrative. Combined with logo count, isolates the pricing-power signal that NRR and ARR alone can blur.
Source
imboard Editorial

ARR at Risk

customers.arr_at_risk
currencyEditorial
Description
Sum of ARR from customers flagged "at-risk" by the customer-success team — typically driven by usage decline, low health score, executive turnover at the customer, missed milestones, or explicit churn intent. The board reads this as the worst-case near-term churn exposure if no intervention happens. Common pitfall: the "at-risk" definition drifts across CSMs and quarters; standardize the criteria (e.g. health score below threshold OR 30-day usage drop > X% OR cancellation request received) and version-control the playbook so the absolute number is comparable period-over-period. Pair with `percent_arr_at_risk` for the proportional read.
Why it matters
Converts the qualitative CS pipeline into a board-readable dollar exposure. Forces the team to put a number on hand-wavy customer-health concerns and surfaces concentration risk (a single $500K at-risk account is a different conversation than fifty $10K accounts).
Source
imboard Editorial

Average Contract Value (ACV)

customers.avg_contract_value
currencyEditorial
Description
Average annualized contract value across the active customer base (or across new logos, depending on the board's convention — document which). Expressed in the reporting currency. The board reads ACV alongside total customers to disambiguate logo-led vs. value-led growth, and against `customers.prior_quarter_acv` to see whether deal sizes are trending up (enterprise mix shift) or down (SMB dilution). Common pitfall: mixing new-logo ACV and blended-base ACV across periods — they trend differently; pick one and hold it.
Why it matters
The unit-economics lever beneath ARR — rising ACV with flat logo growth signals a successful move up-market; falling ACV signals SMB dilution or discounting.
Source
imboard Editorial

Churn Risks

customers.churn_risks
textEditorial
Description
Named at-risk accounts, root-cause analysis of why they're at risk, and the mitigation plan in flight. Pairs with the quantitative `arr_at_risk` and `percent_arr_at_risk` and gives the board the names + the playbook. Common pitfall: listing the at-risk accounts without the diagnosis or the plan — the board reader needs to see what the team is doing about it, not just what the team is worried about. Also: avoid using this surface as a generic "things are bad" venting forum — keep it account-specific and action-specific.
Why it matters
Converts the dollar exposure (`arr_at_risk`) into a board-readable narrative of named accounts and concrete plans. Forces the CS team to articulate the diagnosis, not just the symptom.
Source
imboard Editorial

Customer Segments

customers.customer_segments
textEditorial
Description
Container handle for the field-array of customer segments — each entry carries a segment name, its customer count, and its ARR. Feeds the bespoke customers card's segmentation stack-bars (customer-count + revenue distribution across segments). The "where does the revenue / logo base concentrate" surface (e.g. Enterprise / Mid-Market / SMB). Common pitfall: segment definitions drifting over time, or segment ARR not summing to total ARR because a segment is missing — keep the segmentation exhaustive and the cut stable.
Why it matters
Shows where the book concentrates by logo and by revenue — a base that is logo-heavy in SMB but revenue-heavy in Enterprise has a very different risk profile than its headline ARR suggests.
Source
imboard Editorial

Customer Success Initiatives

customers.key_initiatives
textEditorial
Description
Active programs the CS / Product / Sales team is running to improve customer health, NPS, retention, or expansion — onboarding revamps, health-score model updates, success-plan rollouts, expansion playbooks, advocacy programs, executive-business-review cadence changes. The board reads this as the "what are we doing about it" companion to the metric pages and the at-risk narrative. Common pitfall: listing initiatives without owner, target metric movement, or checkpoint date — the board cannot follow up on vague programs.
Why it matters
Closes the loop between the metrics page and the "what we're doing" board narrative. Lets the board hold the team accountable to the actions, not just the outcomes — especially valuable when KPI movement lags initiative launch by a quarter or two.
Source
imboard Editorial

Customers Churned

customers.customers_churned
numberEditorial
Description
Count of customer logos that ended their subscription/contract during the period. Includes voluntary cancellations and non-renewals. Some companies separately track downgrade-to-zero as churn — be explicit about whether downgrades that drop ARR to $0 count as churn (typical: yes) vs. material contraction that keeps ARR > 0 (typical: tracked under contraction, not churn). The board reads this as the raw count behind `logo_churn_rate`; the percentage tells you the rate, the absolute count tells you the volume of CS pain. Common pitfall: counting customers that re-activate (sometimes called "boomerang" or resurrection) — settle the rule (typical: count each cancellation event, do not net resurrection).
Why it matters
The absolute volume read on customer loss. The percentage (`logo_churn_rate`) tells you the rate; the count tells you the CS team load and the number of post-mortem conversations needed.
Source
imboard Editorial

Expansion Opportunities

customers.expansion_opportunities
textEditorial
Description
Identified upsell, cross-sell, and seat-expansion opportunities inside the existing customer base, with deal size and timing where known. This is the qualitative narrative behind the expansion component of NRR — what the CS / Sales team sees in the pipeline that has not yet converted. The board reads this as forward-looking signal on whether NRR will trend up or down next quarter. Common pitfall: confusing "opportunities" (real conversations with named accounts) with "addressable upside" (theoretical TAM uplift) — keep this field anchored in actual pipeline.
Why it matters
Forward-looking signal on NRR trajectory. A thin expansion pipeline is the leading indicator of NRR compression — boards catch it here before it shows up in the metric next quarter.
Source
imboard Editorial

Gross Revenue Retention (GRR)

customers.gross_revenue_retention
percentage (%)Industry-backed
Description
Recurring revenue retained from the cohort of customers present at the start of the period, excluding expansion — so the metric captures only churn and contraction. Per the SaaS Metrics Standards Board (SMSB) GRR standard. GRR is bounded at 100% (cannot exceed it) and reads as the "no-defense-against-churn" floor on retention. The board reads GRR alongside NRR (`customers.net_revenue_retention`) — the gap between them is the expansion contribution. Common pitfall: treating GRR and NRR as substitutes — they answer fundamentally different questions, and a healthy NRR with sliding GRR signals churn masked by upsell.
Why it matters
Isolates the "do customers stay and not shrink" signal from expansion noise. GRR is the true downside floor on retention — boards use it to spot product or onboarding deterioration that NRR can hide.
Benchmark
p25 82% · median 91% · p75 95%
Source
SaaS Metrics Standards Board

Logo Churn Rate

customers.logo_churn_rate
percentage (%)Industry-backed
Description
Share of customer logos lost during the period — the inverse of logo retention. Numerator is logos that churned during the period; denominator is logos present at period start. Per the KBCM/Sapphire Private SaaS Company Survey definition (treated as the de-facto private-SaaS reporting convention). The board reads this as the simplest churn signal — independent of revenue-weighting. Common pitfall: confusing annualized vs. period-rate (monthly churn × 12 ≠ annualized churn for a compounding base) — be explicit about the time window and annualization method.
Why it matters
Direct read on whether customers are walking away. Independent of revenue-weighting, so it cannot be masked by a few large expansions.
Benchmark
p25 5% · median 13% · p75 20%
Source
KBCM/Sapphire SaaS Survey 2024 (15th Annual)

Logo Retention Rate

customers.logo_retention_rate
percentage (%)Industry-backed
Description
Share of customer logos retained from the prior period, counted by logo (not by revenue). Per the SaaS Metrics Standards Board (SMSB) Logo Retention standard: numerator is logos present at both period start and period end; denominator is logos present at period start. New logos acquired during the period are excluded from both. The board reads this as a "stickiness" signal independent of ACV: high logo retention with weak NRR points to flat/contracting expansion; weak logo retention with strong NRR points to high concentration risk. Common pitfall: conflating logo retention with revenue retention — they answer different questions and routinely diverge.
Why it matters
Isolates retention quality from revenue-weighting effects. A handful of large expansions can mask high logo churn in NRR — logo retention surfaces it directly.
Source
SaaS Metrics Standards Board

Net Revenue Retention (NRR)

customers.net_revenue_retention
percentage (%)Industry-backed
Description
Recurring revenue retained from the cohort of customers present at the start of the period, including expansion (upsell, cross-sell, price increases) and net of churn and contraction — but excluding revenue from net-new logos acquired in-period. Per the SaaS Metrics Standards Board (SMSB) NRR standard. NRR above 100% means the cohort grew faster than it lost — a hallmark of strong product-led expansion. The board reads NRR alongside GRR (`customers.gross_revenue_retention`) to separate the "keep + expand" signal from the "just keep" signal. Common pitfall: mixing GAAP revenue and ARR in numerator vs. denominator, or letting net-new logo revenue leak in — both inflate the number; SMSB is explicit that the cohort is closed at period start.
Why it matters
The single best read on whether existing customers love the product enough to pay more over time. Strong NRR (>100%) compounds — it lets growth come from inside the install base, lowering reliance on new-logo acquisition and improving capital efficiency.
Benchmark
p25 96% · median 101% · p75 109%
Source
SaaS Metrics Standards Board

New Customers Added

customers.new_customers_added
numberEditorial
Description
Count of net-new customer logos acquired during the period (excludes expansion of existing accounts and re-activated churned logos unless they signed a fresh contract). The board reads this alongside `customers.customers_churned` to derive the net logo change and alongside `customers.prior_quarter_total_customers` to reconcile the logo bridge (prior total + new − churned = current total). Common pitfall: counting signed-but-not-yet-live logos here while counting them as live in `customers.total_customers` — keep the activation cut-off consistent across both.
Why it matters
The acquisition half of the logo bridge — pairs with churn to show whether the customer base is growing by count, independent of ARR mix.
Source
imboard Editorial

NPS Responses

customers.nps_responses
numberEditorial
Description
The number of survey responses the current `customers.nps_score` is computed from — the confidence qualifier the board must read alongside any NPS value. Per the NPS methodology (Reichheld/Bain), a score from a small or unrepresentative sample is unreliable; surfacing the response count lets the board discount low-n scores. Common pitfall: celebrating (or alarming at) an NPS swing that is actually a sample-size artifact — always read the score and the response count together.
Why it matters
The confidence denominator under NPS — an NPS based on <50 responses or <10% response rate should be flagged low-confidence rather than trended.
Source
imboard Editorial

NPS Score

customers.nps_score
numberIndustry-backed
Description
Net Promoter Score — % of survey respondents who are promoters (score 9–10) minus % detractors (0–6), passives (7–8) excluded. Per the original NPS methodology (Reichheld, Bain & Company, 2003). The score ranges from −100 to +100. The board reads NPS as one read on product-market fit and word-of-mouth potential, not as a precise customer-loyalty measurement — the methodology is well-known for being sensitive to sample bias, response rate, and survey timing. Common pitfall: comparing NPS across companies without normalizing for industry — B2B SaaS NPS distributions sit much higher than consumer-app NPS, and the absolute number means little without a peer cohort.
Why it matters
A coarse-grained directional read on customer affection and word-of-mouth potential. Sustained movement (especially regressions) is the signal the board should focus on, not absolute values — the methodology is too noisy for fine comparisons across companies.
Benchmark
p25 20count · median 36count · p75 50count
Source
Retently NPS Benchmarks 2025

NPS Trend

customers.nps_trend
numberEditorial
Description
Period-over-period change in NPS score — the trajectory signal that matters more than any single absolute score. A 5-point swing between adjacent quarters is usually more informative than a "good" or "bad" absolute label, because the methodology's noise floor is high enough that absolute comparisons across companies (or even across quarters with different sample sizes) are unreliable. The board reads this to spot deterioration early — a persistent multi-quarter decline is one of the leading indicators of pending churn. Common pitfall: comparing periods with very different sample sizes or response rates — a "decline" from 45 to 35 means very different things at n=30 vs. n=300.
Why it matters
NPS's methodology noise makes absolute scores hard to interpret across companies. The trend within a single company's own measurement cadence is more reliable — a sustained decline is a leading indicator of churn risk even when the absolute score still reads "good".
Source
imboard Editorial

Prior-Quarter ACV

customers.prior_quarter_acv
currencyEditorial
Description
The average contract value reported in the PRIOR period — the comparison anchor for the current `customers.avg_contract_value`. The board reads the two together to render the ACV trend chip on the bespoke customers card (delta + direction) without recomputing it. Common pitfall: comparing a prior new-logo ACV to a current blended-base ACV — keep the population definition identical across the two periods or the trend is an artifact.
Why it matters
Lets the board read ACV direction at a glance — the single most useful framing of an ACV number is its own trajectory.
Source
imboard Editorial

Prior-Quarter Concentration

customers.prior_quarter_concentration
percentage (%)Editorial
Description
The top-customer ARR concentration reported in the PRIOR period — the comparison anchor for the current `customers.top_customer_concentration`. The board reads the two together to render the concentration trend on the bespoke customers card (rising concentration = growing single-account dependency risk). Common pitfall: comparing a top-1 concentration to a top-5 concentration across periods — keep the "top-N" cut identical.
Why it matters
Direction is the signal — rising concentration means a single account's health increasingly drives the whole book's risk.
Source
imboard Editorial

Prior-Quarter GRR

customers.prior_quarter_grr
percentage (%)Editorial
Description
Gross Revenue Retention reported in the PRIOR period — the comparison anchor for the current `customers.gross_revenue_retention`. The board reads the two together to render the GRR trend on the bespoke customers retention grid. Per the SMSB GRR definition (excludes expansion, capped at 100%), both periods must use the same cohort basis for the delta to mean anything. Common pitfall: reading a GRR trend without the matching NRR trend — the gap between them is the expansion signal.
Why it matters
A declining GRR is the truest early churn signal — it cannot be masked by expansion the way NRR can.
Source
imboard Editorial

Prior-Quarter NRR

customers.prior_quarter_nrr
percentage (%)Editorial
Description
Net Revenue Retention reported in the PRIOR period — the comparison anchor for the current `customers.net_revenue_retention`. The board reads the two together to render the NRR trend on the bespoke customers retention grid. Per the SMSB NRR cohort definition, both periods must use the same closed-start-cohort methodology for the delta to be meaningful. Common pitfall: comparing an NRR computed on a different cohort window across the two periods.
Why it matters
Direction matters more than level for retention — a slipping NRR is an early expansion-engine warning even while still above 100%.
Source
imboard Editorial

Prior-Quarter Total Customers

customers.prior_quarter_total_customers
numberEditorial
Description
The total active customer-logo count at the END of the prior reporting period — the opening balance for the current period's logo bridge. The board reads this so the bespoke customers card can show beginning vs. ending logos and the net change without having to re-derive the opening balance from `total_customers − new + churned`. Common pitfall: silently re-stating the prior total after a definitional change to "customer" — hold the counting unit constant or footnote the restatement.
Why it matters
Gives the board the explicit beginning-of-period logo balance so the logo bridge reconciles without inference.
Source
imboard Editorial

Retention Insights

customers.retention_insights
textEditorial
Description
Free-form commentary from the CS / Sales leadership on retention trends, cohort behavior, and underlying drivers of loyalty (or its absence). Pairs with the quantitative retention KPIs (NRR, GRR, logo retention) and gives the board the "why" behind the numbers — which cohorts are strong, which are weak, what feature engagement correlates with retention, what onboarding changes are landing. Common pitfall: filler prose that restates the numbers without adding causal insight — a board reader should learn something here they could not infer from the metrics page alone.
Why it matters
Adds causal explanation to the retention numbers — boards optimize for diagnoses, not just descriptions. Reading "NRR slipped from 115% to 108%" is half the story; reading "NRR slipped because two large customers cut seat counts as they integrated us with an acquired vendor — non-recurring" is the actionable version.
Source
imboard Editorial

Retention Reporting Method

customers.reporting_method
textEditorial
Description
Whether the company TRACKS cohort retention (NRR/GRR) or does NOT yet track it — the explicit signal the bespoke customers retention grid reads to choose between the tracked 4-card retention view and the not-tracked empty state. Value is `tracked` or `not_tracked`. Without this canonical field the card has to INFER "tracked" and can never honestly render the not-tracked state. Common pitfall: leaving NRR/GRR blank to mean "not tracked" — that is ambiguous with "tracked but zero"; this explicit enum removes the ambiguity.
Why it matters
Lets the board distinguish "retention is bad" from "retention is not yet measured" — two very different early-stage situations that a blank NRR cannot tell apart.
Source
imboard Editorial

Top Customer Concentration

customers.top_customer_concentration
percentage (%)Editorial
Description
Share of total ARR contributed by the top N customers — typically top 5 or top 10. Measures revenue concentration risk: a high concentration means losing one big customer would materially dent ARR. The board reads this alongside `arr_at_risk` and the customer list to gauge how much of the company's future is tied to a handful of accounts. Common pitfall: hiding parent-account aggregation — if three "customers" are subsidiaries of the same parent, true concentration is higher than the count-by-logo view shows; settle parent-rollup rules and document them in `customer_definition_note`.
Why it matters
Quantifies "single-account risk." For early-stage companies, high concentration is expected and not necessarily a problem; for growth-stage companies, it constrains valuation multiples and is a frequent due-diligence flag in fundraising and M&A.
Source
imboard Editorial

Total Customers

customers.total_customers
numberEditorial
Description
Count of active paying customer logos at the end of the period. "Active" means the customer has a live paid subscription or contract on the reporting date — not trial, not cancelled, not zero-revenue. The board reads this alongside ARR to triangulate whether growth is logo-driven (more customers at similar ACV) or expansion-driven (existing customers paying more). Common pitfall: definitions of "customer" drift over time as the company sells to subsidiaries, parent accounts, or self-serve users — settle the counting unit (parent vs. account vs. seat) and document it in `customer_definition_note` so cross-period comparisons stay honest.
Why it matters
The denominator beneath every retention, churn, and concentration metric — and the simplest read on whether the company is winning new logos at the headline level. Boards use it to disambiguate logo-led vs. ARR-led growth.
Source
imboard Editorial

Finance67 KPIs

Actual Burn Rate (Past Period)

finance.burn_rate_actual
currency (/month)Editorial
Description
The single past-period observed burn — gross and net — that anchors the forecast-scenario matrix. The "we just lived through this" baseline against which conservative / most-likely / best-case forecasts are projected. Differs from `finance.gross_burn_rate` and `finance.net_burn_rate` in being explicitly a point-in-time historical anchor with both components paired in one object, rather than the standalone monthly KPI values. Common pitfall: anchoring forecasts off a single month with a known one-off (large bill, prepayment received) bakes a distortion into all scenarios — pick a representative period or document the adjustment.
Why it matters
Anchors the credibility of the forecast matrix — scenarios that diverge wildly from the actual baseline without explicit drivers are not credible. Boards typically interrogate any scenario whose burn differs from actual by more than ~20% without a named driver.
Source
imboard Editorial

Bank Accounts

finance.bank_accounts_list
textEditorial
Description
FX-aware enumeration of the company's bank, brokerage, and money-market accounts — each with bank name, account type, restricted flag, currency, balance, as-of date, and notes. The underlying data source for `finance.total_cash_in_bank`, `finance.total_restricted_cash`, `finance.total_unrestricted_cash`, and the FX conversion that turns multi-currency holdings into a single reporting-currency number. Common pitfall: a single forgotten account (often a legacy operational account or a money-market sweep) silently misstates the total — boards should ask for a checklist reconciliation against the prior board pack each cycle. Best practice: include account-number last-4 (not full numbers, for security) and the FX rate used per non-functional-currency account.
Why it matters
The auditable line-item basis for every aggregate cash KPI on the board pack. Without it, the headline numbers cannot be reconciled and a missing account cannot be detected.
Source
imboard Editorial

Burn Rate Scenarios

finance.burn_rate_scenarios
textEditorial
Description
Forecast burn-rate matrix across three scenarios — conservative (defensive cost plan, slow revenue), mostLikely (current best-estimate), bestCase (aggressive investment with strong revenue) — with gross + net burn for each. Bound to the ScenarioBurnRateMatrix widget alongside the historical `finance.burn_rate_actual` anchor. The board reads this to understand what range of cash trajectories the company is planning for and which one management has chosen as the base case. Common pitfall: the three scenarios cluster tightly (all within ±10% of each other) — that's not three scenarios, it's one scenario with rounding error. Real scenarios should reflect meaningfully different operating decisions and produce visibly different runways.
Why it matters
Forces explicit scenario thinking and surfaces the risk-adjusted range of outcomes the board should plan for — without this, the single-number forecast invites false confidence.
Source
imboard Editorial

Cloud / Hosting

finance.cloud_hosting
currencyEditorial
Description
Direct cost of cloud infrastructure, hosting, storage, and compute used to deliver the product (AWS, GCP, Azure, CDN). A cost-of-revenue line because it scales with serving customers. For infrastructure-heavy and AI products this is often the largest COGS component.
Why it matters
A primary driver of gross margin, especially for infrastructure- and AI-heavy products.
Source
imboard Editorial

Contractors / Outsourcing

finance.contractors_outsourcing
currencyEditorial
Description
Cost of freelancers, dev shops, outsourced QA, and temporary engineering help for the period. Often used to flex capacity without permanent headcount.
Why it matters
A flexible-capacity lever; sustained high spend can signal an under-hired team.
Source
imboard Editorial

CS Payroll

finance.cs_payroll
currencyEditorial
Description
Fully-loaded compensation for customer success managers, onboarding, and account management (where not sales-owned) for the period. The retention/expansion-oriented people cost, distinct from cost-of-revenue support.
Why it matters
The investment behind retention and expansion; pairs with NRR.
Source
imboard Editorial

CS Tools / Software

finance.cs_tools_software
currencyEditorial
Description
Cost of customer-success tooling for the period — customer-health platforms, support/ticketing, chat, and onboarding tools.
Why it matters
The tooling overhead of the retention motion.
Source
imboard Editorial

Current Asset Adjustments

finance.current_asset_adjustments
currencyEditorial
Description
Signed cash effect of period-over-period changes in current assets — accounts receivable, prepaid expenses, deposits, and other short-term assets. Positive when assets are converting back to cash (AR collections, prepaid expenses being consumed); negative when assets are growing and absorbing cash (AR balance up, new prepayments made). Half of the `finance.net_working_capital_adjustment` rollup. Common pitfall: a one-off enterprise prepayment to a vendor (e.g. 12-month infra commit) shows up here as a large negative without the P&L showing the cost yet — flag it explicitly so the board does not read deterioration where there is none.
Why it matters
Surfaces the cash impact of growing receivables and prepayments separately from operating spend — important when DSO is moving or large prepaid commitments are taken.
Source
imboard Editorial

Current Liability Adjustments

finance.current_liability_adjustments
currencyEditorial
Description
Signed cash effect of period-over-period changes in current liabilities — accounts payable, accrued payroll/taxes/bonuses, deferred revenue from customer prepayments, and other short-term liabilities. Positive when liabilities grow and absorb less cash than the matched expense suggests (e.g. AP balance growing means vendor cash payments lag); negative when liabilities are being paid down faster than they accrue. Deferred revenue is the most powerful component in SaaS — a large annual prepayment received increases deferred revenue and supplies cash now against expense recognized later. Common pitfall: a board reading this as straight cash improvement misses that deferred revenue must still be earned out, and a stretched AP balance signals supplier strain. Best practice: footnote large components (deferred revenue, accrued bonus) separately.
Why it matters
Captures the cash benefit (or drag) of working-capital liability movements — deferred revenue inflows in particular can mask underlying cash burn at SaaS companies that book annual upfront.
Source
imboard Editorial

Customer Support & Delivery

finance.customer_support_delivery
currencyEditorial
Description
Direct cost of supporting and serving customers that is part of cost-of-revenue (front-line support, delivery operations tied to the product). Distinct from the Customer Success OpEx section, which covers retention/expansion-oriented account management.
Why it matters
Captures the service cost embedded in gross margin, separate from go-to-market CS.
Source
imboard Editorial

Depreciation & Amortization

finance.depreciation_amortization
currencyEditorial
Description
Non-cash expense allocating the cost of capitalized assets (equipment, capitalized software, intangibles) over their useful life for the period. Below the EBITDA line precisely because EBITDA excludes it; usually small for early-stage software companies.
Why it matters
Bridges EBITDA to net income; non-cash, so it affects accounting profit but not burn.
Source
imboard Editorial

EBITDA

finance.ebitda
currencyEditorial
Description
Earnings before interest, taxes, depreciation, and amortization for the period — gross profit minus total operating expense. The core operating result for a startup P&L: the clean view of operating profit or loss before non-operating and non-cash effects.
Why it matters
The headline operating-result line the board reads for profitability/loss before financing and accounting effects.
Source
imboard Editorial

Events / Conferences

finance.events_conferences
currencyEditorial
Description
Cost of conferences, booths, sponsorships, and event-linked travel for the period. Often lumpy quarter to quarter around event calendars.
Why it matters
A material, lumpy GTM line worth isolating so it does not distort the marketing trend.
Source
imboard Editorial

Financial Assumptions

finance.assumptions
textEditorial
Description
Narrative listing of the key inputs the forecast rests on — growth-rate assumptions, churn assumptions, hiring plan, FX rates, expected timing of large bookings, planned price changes, capitalized-vs-expensed R&D treatment, etc. Without this field, the board cannot tell whether a forecast change reflects a real-world update or a quietly changed assumption. Common pitfall: assumptions are written once at planning and never updated when the underlying reality shifts — track explicitly which assumption changed each quarter and why. Best practice (per "Venture Deals" by Feld & Mendelson, and standard board-pack guidance): every material variance vs. forecast should be traceable to either an executed plan or a changed assumption.
Why it matters
Makes the forecast auditable across periods. Boards cannot challenge or endorse a number whose assumptions are invisible — and quietly changing assumptions is the single most common source of forecast drift.
Source
imboard Editorial

Financial Risk Factors

finance.risk_factors
textEditorial
Description
Material risks that could break the forecast or the cash position — customer concentration, contract renewal risk in the next 2 quarters, debt-covenant proximity, FX exposure on multi-currency revenue/cost mix, payment-processor concentration, audit/tax adjustments under review, regulatory changes affecting revenue recognition. Distinct from `risk_factors` at the operations level — this is explicitly financial. Common pitfall: this field becomes boilerplate ("market risk, execution risk") instead of naming the specific risks the board can act on this quarter. Best practice (per the standard board-pack guidance reflected in NVCA Model Investor Rights Agreement information-rights conventions): name the top 3–5 risks with a probability/impact note and a current mitigation status.
Why it matters
Gives the board a defensible answer to "what should worry us next quarter" — and creates an audit trail of which risks management saw coming vs. which surprised them. Frequently the highest-signal part of the cash dashboard at growth stage.
Source
imboard Editorial

Forecast Commentary

finance.forecast_notes
textEditorial
Description
Executive narrative on what the latest forecast says and how it has changed since prior reporting — which scenarios were considered, which was picked as "most likely" and why, what changed since last quarter, and what would push the forecast into a different scenario. Pairs with `finance.burn_rate_scenarios` (the numeric scenarios) to provide the qualitative "why" beside the quantitative "what". Common pitfall: this becomes a restatement of the numbers rather than commentary — every paragraph should add interpretation the numbers do not by themselves convey (drivers, decisions taken, decisions deferred).
Why it matters
Gives the board the interpretation layer that raw scenario numbers lack — without it, the burn-rate-scenarios table is data without meaning. Disciplined commentary also creates a record of management's rationale that can be re-examined when reality plays out.
Source
imboard Editorial

FX Gain / Loss

finance.fx_gain_loss
currencyEditorial
Description
Foreign-exchange gain (positive) or loss (negative) for the period from revaluing non-functional-currency balances and transactions. A SIGNED line; relevant for companies holding cash or transacting in multiple currencies.
Why it matters
Can swing net income for multi-currency companies even when operations are stable.
Source
imboard Editorial

G&A Payroll

finance.ga_payroll
currencyEditorial
Description
Fully-loaded compensation for finance, HR, operations, admin, and executive/admin allocation for the period. The people cost of running the company.
Why it matters
Overhead that should grow slower than revenue as the company scales.
Source
imboard Editorial

Gross Burn Rate

finance.gross_burn_rate
currency (/month)Editorial
Description
Average monthly cash outflow before any inflows are netted off — essentially the company's monthly cost base in cash terms. Tracked alongside net burn because net burn alone can mask a structural problem when revenue is masking high cost. The board reads gross burn to understand the absolute cost commitment (mostly payroll, infra, COGS, sales spend) regardless of revenue mix. Common pitfall: founders often optimize the net burn narrative ("we cut burn 30%") via a one-time inflow without addressing the gross-burn cost base — the next quarter without that inflow re-exposes the underlying spend. Always present gross and net side-by-side.
Why it matters
Strips revenue volatility from the survival picture — shows the cost commitment the company must support each month regardless of bookings outcomes. A widening gap between gross and net burn that depends on a single deal or one-off inflow is a fragility signal.
Source
imboard Editorial

Gross Margin %

finance.gross_margin_pct
percentageEditorial
Description
Gross profit as a percentage of total revenue for the period — the headline quality-of-revenue and delivery-efficiency metric. Expressed 0–100. The P&L-statement margin computed from the revenue/COGS split; complements the GTM-level `sales.gross_margin`.
Why it matters
Signals revenue quality and how much each revenue dollar contributes to covering OpEx.
Source
imboard Editorial

Gross Profit

finance.gross_profit
currencyEditorial
Description
Total revenue minus total cost of revenue for the period — the profit left to fund operating expenses. The dollar complement to gross margin and the starting point for the operating-result section.
Why it matters
The dollars available to cover OpEx — the bridge from revenue to operating result.
Source
imboard Editorial

Insurance / Compliance

finance.insurance_compliance
currencyEditorial
Description
Cost of D&O and cyber insurance, SOC 2, and regulatory compliance for the period. Rises with company size, customer requirements, and financing stage.
Why it matters
A step-fixed cost driven by stage, customer requirements, and risk posture.
Source
imboard Editorial

Interest Income / Expense

finance.interest_income_expense
currencyEditorial
Description
Net interest for the period as a SIGNED line: interest earned on cash/deposits (positive) net of interest paid on loans, venture debt, or other financing (negative). For cash-rich post-raise companies this is often net positive income.
Why it matters
Surfaces financing effects below the operating line; meaningful for companies with venture debt or large cash balances.
Source
imboard Editorial

Legal, Accounting & Professional Services

finance.legal_accounting_professional
currencyEditorial
Description
Cost of legal, accounting, audit, tax, fractional CFO, and outside consultants for the period. Often spikes around financings, audits, and major contracts.
Why it matters
A lumpy overhead line; spikes usually map to financing or compliance events.
Source
imboard Editorial

Marketing Payroll

finance.marketing_payroll
currencyEditorial
Description
Fully-loaded compensation for marketing leadership, demand generation, content, and growth for the period.
Why it matters
The people cost of demand generation, distinct from paid media spend.
Source
imboard Editorial

Net Burn Rate

finance.net_burn_rate
currency (/month)Editorial
Description
Average monthly net cash outflow over the reporting period — total cash spent minus total cash collected, divided by the number of months in the period. The headline survival number for venture-backed startups: it pairs with `finance.total_cash_in_bank` to produce runway, and pairs with revenue growth to produce the Bessemer "burn multiple". Common pitfall: net burn is volatile — large quarterly bills (annual SaaS renewals, employer-tax true-ups), enterprise prepayments, and FX swings can mask the underlying trend. Smoothing over a trailing 3-month average is standard board practice. Equally important: do not silently include one-off cash events (acquisitions, settlements, large prepayments received) without flagging them — boards prefer a "core burn" and "headline burn" pair when the period is noisy.
Why it matters
Single most-watched metric below revenue at venture-backed companies — drives runway, valuation reads (via the burn multiple), and the calculus on when to fundraise vs. cut.
Source
imboard Editorial

Net Income / Loss

finance.net_income
currencyEditorial
Description
The accounting bottom line for the period — EBITDA less depreciation & amortization and tax, plus the signed interest and FX lines. The final result of the income statement. Distinct from cash burn (an accrual figure, not a cash-flow measure).
Why it matters
The statutory bottom line; read alongside burn/runway, since net income is accrual and does not equal cash consumed.
Source
imboard Editorial

Net Working Capital Adjustment

finance.net_working_capital_adjustment
currencyEditorial
Description
Signed net effect on cash of changes in current assets and current liabilities — receivables coming in (positive), payables going out (negative), prepaid expenses (negative when paid, positive when burned down), and accrued liabilities (positive when accrued, negative when settled). The rollup of `finance.current_asset_adjustments` and `finance.current_liability_adjustments`. Common pitfall: at early stage this is dominated by payroll-cycle noise and is near zero — once the company adds enterprise contracts with annual prepayments or 60-day net terms, this can swing 1–3 months of burn either direction. Becomes material at Series A+; ignored before that.
Why it matters
Bridges the gap between accrual-basis P&L and cash-basis runway. A board reading the P&L alone can miss a working-capital headwind that is materially shortening runway.
Source
imboard Editorial

Office / Facilities

finance.office_facilities
currencyEditorial
Description
Cost of rent, coworking, and office facilities for the period. Smaller for remote-first companies; a fixed commitment where leased.
Why it matters
A fixed cost and lease commitment that affects runway flexibility.
Source
imboard Editorial

Operationally Available Cash

finance.operationally_available_cash
currencyEditorial
Description
Unrestricted cash adjusted for near-term working-capital effects — i.e. the cash that is actually deployable after accounting for receivables coming in, payables going out, and accrued obligations crystallizing in the next reporting period. More conservative than `finance.total_unrestricted_cash` because it nets out the cash a healthy AR/AP cycle is already promising or claiming. The board reads this as the "real" cash position when working capital is material to the business (typical at Series A+, when AR/AP cycles get sizeable). Common pitfall: at early stage AR is small and AP is mostly payroll/SaaS, so this collapses to unrestricted cash — once enterprise deals or 60-day net terms appear, the gap widens fast.
Why it matters
Best single-number answer to "how much cash do we really have to deploy this quarter" once working capital is material. Substituted for unrestricted cash in the runway denominator at growth stage.
Source
imboard Editorial

Other COGS

finance.other_cogs
currencyEditorial
Description
Direct cost-of-revenue items not captured by the named COGS lines — a catch-all kept small by design. If it becomes material it should be split into a named line.
Why it matters
Keeps total COGS complete without distorting the primary cost categories.
Source
imboard Editorial

Other CS

finance.other_cs
currencyEditorial
Description
Customer-success operating costs not captured by the named CS lines for the period — a catch-all kept small by design.
Why it matters
Keeps Total Customer Success complete without distorting the primary lines.
Source
imboard Editorial

Other G&A

finance.other_ga
currencyEditorial
Description
G&A operating costs not captured by the named G&A lines for the period — the roll-up home for minor overhead (bank fees, office supplies, small licenses). Kept small by design.
Why it matters
Keeps Total G&A complete and absorbs the many small overhead items.
Source
imboard Editorial

Other R&D

finance.other_rnd
currencyEditorial
Description
R&D operating costs not captured by the named R&D lines for the period — a catch-all kept small by design.
Why it matters
Keeps Total R&D complete without distorting the primary lines.
Source
imboard Editorial

Other Revenue

finance.other_revenue
currencyEditorial
Description
Recognized revenue not captured by the subscription, usage, or services lines — a catch-all for small or unusual revenue items. Kept small by design; if it becomes material it should be split into a named line.
Why it matters
Keeps total revenue complete without polluting the primary revenue categories.
Source
imboard Editorial

Other S&M

finance.other_sm
currencyEditorial
Description
Sales & marketing operating costs not captured by the named S&M lines for the period — a catch-all kept small by design.
Why it matters
Keeps Total S&M complete without distorting the primary lines.
Source
imboard Editorial

Paid Marketing

finance.paid_marketing
currencyEditorial
Description
Paid demand-generation spend for the period — search, social, performance marketing, and sponsorships. The variable media component of go-to-market.
Why it matters
A directly-tunable growth lever; the core input to paid CAC.
Source
imboard Editorial

Payment / Transaction Costs

finance.payment_transaction_costs
currencyEditorial
Description
Direct payment-processing and transaction fees attributable to delivering revenue (card processing, gateway fees, marketplace take rates). A cost-of-revenue line where relevant; omitted or near-zero for invoice-only businesses.
Why it matters
Directly reduces gross margin on transaction- or consumer-billed revenue.
Source
imboard Editorial

Product / Design Payroll

finance.product_design_payroll
currencyEditorial
Description
Compensation for product managers and designers for the period. Can be folded into R&D payroll at smaller companies; kept separate where product/design is a distinct cost center.
Why it matters
Separates product/design investment from pure engineering for a clearer R&D mix.
Source
imboard Editorial

R&D Payroll

finance.rd_payroll
currencyEditorial
Description
Fully-loaded compensation for engineering, data, QA, DevOps, and technical leadership for the period (salary, employer taxes, benefits). The largest R&D cost for most software companies.
Why it matters
The dominant input to R&D spend and a primary driver of total burn.
Source
imboard Editorial

R&D Tools / Software

finance.rd_tools_software
currencyEditorial
Description
Cost of engineering tooling and platforms for the period (source control, CI/CD, testing, observability, developer platforms). Operating expense — distinct from cloud/hosting COGS that serves customer traffic.
Why it matters
Scales with team size; a useful efficiency read per engineer.
Source
imboard Editorial

Recruiting

finance.recruiting
currencyEditorial
Description
Cost of agencies, job boards, and referral bonuses for the period. Scales with the pace of hiring and is lumpy around growth pushes.
Why it matters
A leading indicator of headcount growth and future payroll.
Source
imboard Editorial

Restricted Cash

finance.total_restricted_cash
currencyEditorial
Description
Cash on the balance sheet that is not available for general operating use because it is contractually pledged or held for a specific purpose — typical examples include landlord lease-deposit escrows, customer-funds collateral, security deposits backing letters of credit, payment-processor reserves, and debt-covenant minimum-balance requirements. Per IFRS and US GAAP balance-sheet presentation, restricted cash must be disclosed separately from unrestricted cash; the board should treat this number as removed from runway. Common pitfall: payment-processor "reserve" balances and large customer-deposit floats are often missed when reporting unrestricted cash, inflating apparent runway.
Why it matters
Excluded from operationally available cash and from the runway calculation — reporting it inside total cash without flagging the restriction overstates runway and can mask a covenant or liquidity issue.
Source
imboard Editorial

Runway (Months)

finance.runway_months
number (months)Industry-backed
Description
Estimated number of months the company can operate at the current net burn before unrestricted cash reaches zero, holding everything else constant. The single most consequential survival input for venture-backed companies — it sets the urgency of every fundraising, hiring, and cost decision. Common pitfall: runway is often quoted off `finance.total_cash_in_bank` and a single-month spot-burn instead of operationally-available cash and a 3-month-trailing burn — the result is a runway that looks 2–4 months longer than it actually is when working capital tightens. Boards should ask which cash and which burn went into the calculation.
Why it matters
Drives the timing of every fundraise, hire, and budget cut — and is the number investors lead with in diligence. Crossing under stage-typical thresholds usually triggers a board-level cost or fundraising conversation.
Source
KBCM/Sapphire SaaS Survey 2024 (15th Annual)

S&M Tools / Software

finance.sm_tools_software
currencyEditorial
Description
Cost of go-to-market tooling for the period — CRM, enrichment, outbound, attribution, and sales-engagement platforms.
Why it matters
The tooling overhead of the GTM motion; scales with team size.
Source
imboard Editorial

Sales Commissions

finance.sales_commissions
currencyEditorial
Description
Variable sales compensation earned on bookings for the period. Separated from sales payroll because it scales with deals closed and explains period-to-period variance differently.
Why it matters
Ties go-to-market cost to bookings; a key input to CAC.
Source
imboard Editorial

Sales Payroll

finance.sales_payroll
currencyEditorial
Description
Fully-loaded base compensation for account executives, SDRs, and sales leadership for the period. Excludes commissions, which are tracked separately because they scale with bookings.
Why it matters
The fixed component of go-to-market cost; pairs with commissions for full sales cost.
Source
imboard Editorial

Services / Implementation Revenue

finance.services_revenue
currencyEditorial
Description
Recognized non-recurring revenue from implementation, onboarding, or professional services for the period. Kept separate from recurring revenue because it is lower-margin and does not compound — a services-heavy quarter can grow total revenue while ARR stays flat.
Why it matters
Separating services keeps recurring revenue clean and exposes margin dilution from delivery-heavy periods.
Source
imboard Editorial

Services Delivery Costs

finance.services_delivery_costs
currencyEditorial
Description
Direct cost of delivering implementation and professional services — the cost paired with services/implementation revenue. Tracking it against `finance.services_revenue` reveals whether services are run at, above, or below cost.
Why it matters
Pairs with services revenue to show services margin — often a board question.
Source
imboard Editorial

Software & IT

finance.software_it
currencyEditorial
Description
Cost of internal software, IT, security, devices, and admin tooling for the period (company-wide SaaS not specific to R&D or GTM).
Why it matters
A quiet cost-creep area as headcount and tool sprawl grow.
Source
imboard Editorial

Subscription Revenue

finance.subscription_revenue
currencyEditorial
Description
Recognized recurring software revenue for the period — the recurring subscription fees earned under contract, recognized on an accrual basis over the service period. The core revenue line for a SaaS P&L; kept separate from usage and services so the board can read the recurring-vs-non-recurring mix. Distinct from ARR (a forward run-rate) and from cash collected (a financing-timing view).
Why it matters
Isolates durable recurring revenue — the basis of SaaS quality-of-revenue and gross-margin reads.
Source
imboard Editorial

Tax

finance.tax
currencyEditorial
Description
Corporate income tax, withholding tax, or other tax expense for the period. Often minimal for loss-making startups, but can be non-trivial with multi-jurisdiction operations or specific tax regimes.
Why it matters
Completes the path to net income; can surprise multi-entity companies even while loss-making.
Source
imboard Editorial

Third-Party / API / Data Costs

finance.third_party_data
currencyEditorial
Description
Direct cost of external APIs, data providers, enrichment, and model/LLM inference consumed to deliver the product. Broken out from cloud/hosting because for AI products these costs can move gross margin materially and scale with usage rather than headcount.
Why it matters
For AI-native products this can be the swing factor in gross margin and deserves its own board line.
Source
imboard Editorial

Total Cash in Bank

finance.total_cash_in_bank
currencyEditorial
Description
Sum of all bank account balances at the reporting cut-off, expressed in a single reporting currency after FX conversion. This is the gross top-of-house cash number — it does not net out restrictions, near-term liabilities, or commitments. The board reads this as the absolute denominator for runway and as a checksum against the cap table (capital raised − cumulative net burn ≈ cash). Common pitfall: founders sometimes report a USD figure that silently includes ILS/EUR accounts at stale FX rates — always reconcile against the bank-accounts list (per FX-aware MultiCurrencyAccountList) and tag the rate date.
Why it matters
The denominator of runway and the single most important survival input — every other cash KPI is read in proportion to this number. Also the basic cap-table sanity check: capital raised minus cumulative net burn should reconcile to total cash within working-capital noise.
Source
imboard Editorial

Total COGS

finance.total_cogs
currencyEditorial
Description
Total cost of revenue for the period — the sum of the COGS lines. Subtracted from total revenue to produce gross profit. Includes only direct delivery costs; operating expenses (R&D, S&M, CS, G&A) sit below the gross-profit line.
Why it matters
The direct-cost base that determines gross profit and gross margin.
Source
imboard Editorial

Total Customer Success

finance.total_cs
currencyEditorial
Description
Total customer-success operating expense for the period — the sum of the CS lines. Shown as its own OpEx section (some companies fold CS into COGS; the default here is OpEx). One of the four OpEx section totals.
Why it matters
Isolates the retention investment so the board can weigh it against NRR.
Source
imboard Editorial

Total G&A

finance.total_ga
currencyEditorial
Description
Total general & administrative operating expense for the period — the sum of the G&A lines. One of the four OpEx section totals.
Why it matters
Overhead the board expects to grow sublinearly with revenue.
Source
imboard Editorial

Total Operational Inflow

finance.total_operational_inflow
currencyEditorial
Description
Sum of cash actually received from operating activities for the period — customer collections (subscription, services, transactional revenue), refunds claimed back from vendors, and any operating tax credits. Excludes financing activities (debt draws, equity proceeds) and investing activities (asset sales, investment income). This is the numerator-side of the net-burn equation, and the cash-basis counterpart to recognized revenue on the P&L. Common pitfall: companies sometimes book annual SaaS prepayments here as a single-month inflow, masking the underlying monthly run-rate — split lumpy items out or smooth over a trailing 3 months.
Why it matters
Inputs the cash-basis revenue side of net burn. A growing inflow at flat-or-falling outflow is the textbook "earning its runway" trajectory; the reverse means the company is more dependent on the cash balance than on revenue.
Source
imboard Editorial

Total Operational Outflow

finance.total_operational_outflow
currencyEditorial
Description
Sum of cash actually paid for operating activities for the period — payroll and benefits, employer taxes, vendor payments (infra, tooling, contractors), sales and marketing spend, rent, professional services, refunds issued. Excludes financing activities (debt repayment, dividend payments) and investing activities (acquisitions, capex). Direct input to gross burn. Common pitfall: capitalized R&D and long-term capex sometimes get bucketed here; if so they distort gross burn. Keep this strictly operating-cash and surface investing/financing outflows separately so the board can see "ongoing cost base" vs. "discretionary capital deployment".
Why it matters
The denominator-side of net burn and the basis of gross burn — controlling the structural cost base is the lever most boards can directly act on between fundraises.
Source
imboard Editorial

Total OpEx

finance.total_opex
currencyEditorial
Description
Total operating expense for the period — R&D + Sales & Marketing + Customer Success + G&A. Subtracted from gross profit to produce EBITDA. Excludes COGS (above the gross-profit line) and below-EBITDA items.
Why it matters
The full operating cost base below gross profit — the lever between gross profit and EBITDA.
Source
imboard Editorial

Total R&D

finance.total_rnd
currencyEditorial
Description
Total research & development operating expense for the period — the sum of the R&D lines. One of the four OpEx section totals that roll into Total OpEx.
Why it matters
The headline R&D investment line the board tracks against revenue and plan.
Source
imboard Editorial

Total Revenue

finance.total_revenue
currencyEditorial
Description
Total recognized revenue for the period — the sum of subscription, usage, services, and other revenue. The P&L revenue subtotal and the denominator for gross margin. Distinct from `sales.total_revenue` (the GTM recognized-revenue metric) and from ARR; this line is the statement total built from the revenue split.
Why it matters
The accounting top line and the basis for gross margin and every margin ratio.
Source
imboard Editorial

Total Sales & Marketing

finance.total_sm
currencyEditorial
Description
Total sales & marketing operating expense for the period — the sum of the S&M lines (payroll, commissions, marketing, paid media, events, tools, other). One of the four OpEx section totals.
Why it matters
The headline go-to-market spend line; the numerator behind blended CAC.
Source
imboard Editorial

Travel & Entertainment

finance.travel_entertainment
currencyEditorial
Description
Cost of travel, meals, customer travel, and board travel for the period. Rolls up minor items; kept as one readable line.
Why it matters
A discretionary line that is an early lever when tightening burn.
Source
imboard Editorial

Unrestricted Cash

finance.total_unrestricted_cash
currencyEditorial
Description
Cash that the company can freely deploy for any operational purpose — total bank balances minus any contractually restricted balances. This is the input most boards actually want when judging runway, because it strips out escrows, security deposits, and processor reserves that cannot be spent on payroll or vendors. The distinction matters more as the company adds enterprise contracts (deposit obligations), debt facilities (covenant balances), and payment processing volume (rolling reserves). Common pitfall: at early stage, restricted cash is often near zero so teams equate this with `finance.total_cash_in_bank` — track them separately from day one to avoid surprise reclassifications later.
Why it matters
The right cash number to divide by net burn when computing the spendable runway a board can act on — restricted cash cannot bridge a payroll gap.
Source
imboard Editorial

Usage / Consumption Revenue

finance.usage_revenue
currencyEditorial
Description
Recognized revenue tied to usage- or consumption-based pricing for the period (metered API calls, compute, seats-on-demand, overages). Separated from subscription revenue because it scales with customer activity rather than contracted commitments and is typically more volatile period to period.
Why it matters
Surfaces how much revenue depends on variable customer activity vs. fixed commitments — a key durability signal.
Source
imboard Editorial

Working Capital Adjustments

finance.working_capital_adjustments_list
textEditorial
Description
Itemized list of working-capital adjustments with explicit sign-prefix driving the additive-vs-subtractive multiplier — e.g. "+ AR collected: $250k", "− Prepaid infra: $80k", "+ Deferred revenue: $600k". The line-item basis for `finance.net_working_capital_adjustment` and its child KPIs (current_asset_adjustments, current_liability_adjustments). The signed-prefix UI convention prevents the most common working-capital reporting bug — sign-flips that double-count or invert the cash effect. Common pitfall: lumping unrelated items into a single "other working capital" line loses the diagnostic value; break out the top 3–5 components.
Why it matters
Makes the working-capital aggregate auditable — the board can see exactly which items moved the number and which direction. Critical at Series A+ when working capital is material.
Source
imboard Editorial

HR28 KPIs

Approved Headcount Budget

hr.approved_headcount_budget
numberEditorial
Description
Board-approved end-of-period headcount target. The contractual reference point against which `hr.total_headcount` and `hr.open_positions` are read — drift means either hiring under plan (typically a growth concern) or over plan (typically a burn-discipline concern). Common pitfall: silent in-year adjustments — boards approve a number, the CEO informally expands or contracts to it, and the variance never gets reconciled. Best practice is to treat changes to this number as board-action items, recorded in `hr.board_actions`.
Why it matters
The single number that converts strategic intent into operating constraint. Variance against this number drives the budget-vs-actual conversation that anchors most board meetings' HR section.
Source
imboard Editorial

ARR per FTE

hr.arr_per_fte
currencyIndustry-backed
Description
Annual Recurring Revenue divided by total FTE-equivalent workforce — the canonical SaaS workforce-productivity ratio anchored to the SaaS Capital Annual Survey methodology (revenue per employee benchmarks). A high-signal denominator for "are we over- or under-staffed for our revenue scale?" Common pitfall: choosing different ARR conventions (ending vs average, GAAP-reconciled vs raw) without locking in a board-level standard. Best practice is to pair this with `sales.arr` so the numerator is unambiguous and to disclose whether contractors are included in the FTE denominator.
Why it matters
Investors use this as a quick scalability and operating-leverage proxy — companies with higher ARR/FTE at a given scale typically command premium multiples. Internally, the metric anchors hiring-plan discipline: does each net new FTE earn its keep?
Benchmark
p25 100000$ · median 130000$ · p75 175000$
Source
SaaS Capital Annual Survey 2025 (14th Annual)

At-Risk Employees

hr.at_risk_count
numberEditorial
Description
Count of employees actively flagged as flight risk by managers, based on engagement signals (skip-level surveys, manager 1:1s, counter-offer activity, tenure-curve risk). A leading indicator that complements the lagging `hr.voluntary_exits` number. Common pitfall: stale flags that never get cleared — at-risk lists tend to drift toward "every senior IC ever" without manager discipline. Best practice is a quarterly refresh with explicit add/remove notes and an action attached to each flag.
Why it matters
Converts manager intuition into a board-readable risk count, and pairs naturally with `hr.retention_initiatives` so the board sees risk and response together. A rising at-risk count without rising retention activity is a yellow flag.
Source
imboard Editorial

Average Days to Fill

hr.avg_days_to_fill
number (days)Industry-backed
Description
Mean elapsed days between requisition opening (approved and posted) and offer acceptance, averaged across requisitions filled in the period. The headline recruiting-velocity KPI commonly tracked in the SHRM Talent Acquisition Benchmarking Report. Common pitfall: choosing between time-to-fill (req-opened to offer-accepted) and time-to-hire (first-applicant to offer-accepted) without locking the convention — the two can differ by weeks. Best practice is to standardize on time-to-fill (the SHRM benchmark convention) and document any deviation.
Why it matters
A stretching time-to-fill is one of the earliest leading indicators of either comp-band misfit, role-spec creep, or recruiter capacity exhaustion. Combined with `hr.open_positions`, it projects when promised capacity actually arrives.
Source
SHRM Talent Acquisition Benchmarking Report

Departments

hr.departments
textEditorial
Description
Field-array of per-department rows — department name, leader status (resolved against `hr.leader_status`), and headcount metrics with stable-count auto-calc — rendered as a drag-sortable table grouped by department. Common pitfall: department boundaries drift over time (Eng+R&D merging, GTM splitting into Sales/Marketing/CS) — when boundaries change, prior-period comparisons need an explicit reconciliation note. This KPI is structural, not numeric — no formula applies.
Why it matters
Shows the org map at a glance — where capacity is allocated, which departments are short-staffed, where leader-vacancies are concentrated. Boards use this to validate the strategy-vs-investment alignment ("we say we are product-led but R&D is 20% of headcount").
Source
imboard Editorial

FTE Metrics

hr.fte_metrics
textEditorial
Description
Derived triple — effective FTE, cost-per-FTE, and annualized payroll — computed from `hr.payroll_run_rate` + `hr.total_contractors` and a contractor-to-FTE conversion factor. Lets the board see capacity in normalized terms even when the staffing mix shifts. Common pitfall: choosing a contractor-to-FTE factor without explicit board agreement — some companies use 1.0 (1 contractor = 1 FTE for capacity), others use 0.8 (account for ramp / partial-engagement), others use cost-equivalent ratios. Lock the convention.
Why it matters
Normalizes capacity and cost across companies with very different contractor strategies, making `hr.arr_per_fte` and `hr.payroll_as_pct_of_burn` more comparable over time. Surfaces hidden cost inflation when contractor headcount grows faster than employee headcount.
Source
imboard Editorial

Hiring Plan

hr.hiring_plan
textEditorial
Description
Forward-looking narrative on next-period hiring priorities — target roles, sequence, sourcing strategy, and any unusual asks (executive search, specialized recruiter spend, location flexibility shifts). Anchors the board's understanding of where capacity is heading and what approvals or help are needed. Common pitfall: a stale plan that gets copy-pasted across quarters — the hiring plan should evolve with strategy shifts. Best practice is to lead with the 2–3 highest-priority hires and their justification, then a brief on backfills and bench-builds.
Why it matters
Converts `hr.approved_headcount_budget` (a number) into a board-relevant sequence (a story). Without this, board members lack the context to help with intros, validate strategic-role timing, or push back on questionable role specs.
Source
imboard Editorial

HR Board Actions

hr.board_actions
textEditorial
Description
Explicit list of HR items requiring board attention, approval, or decision in this meeting — executive comp changes, headcount-budget changes, equity-pool top-ups, employment-policy approvals, and any items needing a board resolution. Common pitfall: burying decisions inside other narrative sections — boards consistently miss requests that are not explicitly tagged as "decision required." Best practice is to label each item as approval-required vs awareness-only and give a one-line ask.
Why it matters
The single most under-served section on most board packs. CEOs often expect their narrative to drive a decision; boards often miss the implicit ask. An explicit "board actions" section is a low-cost forcing function for board-level decision hygiene.
Source
imboard Editorial

HR Executive Commentary

hr.executive_commentary
textEditorial
Description
Stacked commentary editor with per-section icon and live word count, hosting the four canonical HR narrative slots (talent highlights, talent challenges, hiring plan, retention initiatives) under a single base path — each section persists under `<basePath>.<sectionKey>`. The composite container for the narrative side of the HR scorecard, paired with `hr.departments` and `hr.risk_items` for the structured side. Common pitfall: writing each section in isolation — strong commentary cross-references the numbers ("voluntary turnover up 4 points QoQ, here is what we are doing").
Why it matters
Centralizes the narrative around the HR numbers in one editing surface, with word-count feedback that prevents both under- and over-writing. The structure mirrors how boards expect HR to be presented: positives, concerns, plan, mitigations.
Source
imboard Editorial

HR Risk Items

hr.risk_items
textEditorial
Description
Structured field-array of board-attention items, each with type / department / action / narrative quartet (problem / impact / proposal / ask). Chip color follows boardActionNeeded: approval=red, assistance=yellow, awareness=blue. The structured-table version of `hr.board_actions` — preferred when the board has adopted the formal risk-item pattern. Common pitfall: drift toward vague "we are working on it" entries — strong items name a specific action with a date.
Why it matters
Forces explicit categorization of each item (approval / assistance / awareness) so the board cannot accidentally skip a decision item. The color chips make scanning faster than narrative text alone.
Source
imboard Editorial

Involuntary Turnover Rate

hr.involuntary_turnover_rate
percentage (%)Industry-backed
Description
Annualized rate of company-initiated separations as a percentage of average headcount. Complement to `hr.voluntary_turnover_rate`; together they form the total turnover picture per the Mercer US Turnover Survey methodology. Common pitfall: lumping one-time RIFs into the steady-state rate, which makes the trend unreadable. Best practice is to report steady-state involuntary turnover and call out any RIF events separately in `hr.board_actions` with the headcount delta.
Why it matters
A read on performance-management cadence and any active restructuring. Sustained near-zero raises questions about management discipline; sustained-elevated raises questions about hiring quality or strategy thrash.
Source
Mercer US Turnover Survey 2025

Key Hires

hr.key_hires
textEditorial
Description
Field-array of notable individual hires that warrant board-level visibility — typically C-1 executives, director-level functional leaders, and strategic specialist hires. Per-item shape: name, level, role, start status, days-to-fill. Rendered via the T2 collapsible-card gallery pattern. Structural, not numeric — formula does not apply. Common pitfall: listing every hire instead of the strategic few — boards lose signal quickly when this section turns into a directory.
Why it matters
Gives the board context for the headline `hr.new_hires` count — five generalist engineers reads very differently from five senior staff engineers plus a new VP Eng. Boards routinely volunteer reference checks and network help when key hires are surfaced.
Source
imboard Editorial

Key Openings

hr.key_openings
textEditorial
Description
Field-array of priority open roles the board should be aware of and may be able to accelerate — typically C-1 executives, hard-to-fill specialists, and any role open >60 days. Per-item shape: title, department, level, urgency, owner. Rendered via the T2 collapsible-card gallery pattern. Structural, not numeric. Common pitfall: padding the list with every open req — boards add the most value on the 3–8 strategic openings, not on backfilling the next IC.
Why it matters
Turns the scalar `hr.open_positions` count into board-actionable context. Every entry is an opportunity for the board to help with intros, references, or compensation reality-checks. The owner field also surfaces accountability for the search.
Source
imboard Editorial

Leader Status

hr.leader_status
textEditorial
Description
Tri-state leader status (permanent / interim / vacant) for each board-tracked department. Permanent shows name+title; interim shows the covering person; vacant shows the gap explicitly. The single most board-relevant org-design signal — an extended interim or vacant status in a strategic function is almost always a board-level concern. Common pitfall: leaving "interim" indefinitely as a way to avoid the search-and-hire conversation — boards should set a maximum interim duration and treat overruns as board-action items. Structural KPI; no formula.
Why it matters
Leader-coverage gaps in strategic functions (e.g., engineering, sales, product) are leading indicators of execution risk. Boards routinely under-weight this signal because the headcount number can look healthy while the leadership layer is hollow.
Source
imboard Editorial

Net Headcount Change

hr.headcount_change
numberEditorial
Description
Net change in employee headcount during the period — new hires minus (voluntary exits + terminations). The bottom-line growth-or-contraction number on the HR scorecard. Common pitfall: reporting net change without showing the gross-in / gross-out components — boards can't diagnose a flat net number caused by 5 hires and 5 exits the same way they'd diagnose a flat number from zero on each side. Best practice is to surface the four components (new hires, voluntary exits, terminations, net change) together.
Why it matters
Single-number summary of HR's execution in the period and the simplest reconciliation point between this period's `hr.total_headcount` and the prior period's. Variance from `hr.hiring_plan` is the board-conversation trigger.
Source
imboard Editorial

New Hires

hr.new_hires
numberEditorial
Description
Count of employees whose first day fell within the reporting period. The growth-input side of the headcount equation, paired with `hr.voluntary_exits` and `hr.terminations` on the loss side. Common pitfall: counting accepted offers vs actual start dates — these can diverge by weeks (notice period) or fall through entirely (offer rescind, candidate ghosting). The board number should be actual starts, not signed offers; pipeline movement belongs in `hr.hiring_plan` narrative.
Why it matters
Directly drives `hr.headcount_change` and validates execution against `hr.hiring_plan`. Persistent gaps between hiring-plan targets and actual new hires usually indicate either a pipeline problem or compensation-market mismatch — both board-action triggers.
Source
imboard Editorial

Open Positions

hr.open_positions
numberEditorial
Description
Count of board-approved roles that are currently posted and unfilled (requisition open, offer not yet accepted). The leading-edge indicator for upcoming hiring capacity demand. Common pitfall: "approved" drift — roles that were verbally green-lit but never went through the approval gate get counted here, inflating the number. The board number should match the approved headcount budget; everything else belongs in narrative as "pipeline ideas."
Why it matters
Quantifies the hiring debt — every open role is unrealized capacity. Combined with `hr.avg_days_to_fill`, it projects when capacity actually arrives. A growing open-position count while time-to-fill stretches is a recruiting-capacity yellow flag.
Source
imboard Editorial

Payroll as % of Burn

hr.payroll_as_pct_of_burn
percentage (%)Editorial
Description
Monthly fully-loaded payroll cost as a percentage of `finance.gross_burn_rate`. Tells the board what share of cash outflow funds people vs everything else (infra, GTM spend, professional services, facilities). Common pitfall: comparing this ratio across companies without normalizing for stage and capex intensity — a pure-software seed company will run very payroll-heavy; a hardware-or-bio company will not. Best practice is to read this in conjunction with the burn-rate trend, not in isolation.
Why it matters
Cost-structure shape indicator — pairs naturally with runway math. A rising share without rising headcount can signal comp-band drift; a falling share with rising headcount often signals contractor / GTM expansion. Boards use this for the people-vs-program trade-off conversation.
Source
imboard Editorial

Payroll Run Rate

hr.payroll_run_rate
currencyEditorial
Description
Annualized fully-loaded payroll cost based on current employee compensation — wages plus employer-paid taxes, benefits, and typical equity refresh allocation. Used as the dominant input into `hr.payroll_as_pct_of_burn` and the projection for `hr.fte_metrics`. Common pitfall: reporting base-salary-only and missing employer payroll taxes, benefits, and bonus accrual — this can understate true cost by 15–30%. Document the loading convention (typically wages × 1.20–1.30 for US fully-loaded) and apply consistently.
Why it matters
The single largest line in most operating budgets — drives runway calculus, dilution sensitivity at fundraise, and the unit economics conversation. Visible upward steps without a corresponding revenue or headcount-plan justification are board-action triggers.
Source
imboard Editorial

Performance Watch

hr.performance_watch_count
numberEditorial
Description
Count of employees currently on a formal Performance Improvement Plan (PIP) or equivalent performance-bar process. Leading indicator for `hr.terminations` — most PIPs that do not resolve with measurable improvement convert to involuntary exits within one quarter. Common pitfall: confusing PIPs with informal coaching — only employees on a written, time-bound plan with defined exit criteria should be counted here. Informal "we need to talk" relationships belong in the at-risk count, not this number.
Why it matters
Leading indicator for `hr.terminations` and a read on management discipline — managers who avoid PIPs accumulate B-players, managers who over-use them are training-out coachable performers. The trend matters more than the snapshot.
Source
imboard Editorial

Retention Initiatives

hr.retention_initiatives
textEditorial
Description
Narrative on the programs and actions in flight to retain key talent and reduce voluntary turnover — refresh grants, comp-band adjustments, manager training, career-pathing programs, and similar. The response side of the `hr.at_risk_count` and `hr.voluntary_turnover_rate` story. Common pitfall: listing perks (snacks, swag) instead of actions tied to retention drivers. Best practice is to name the initiative, the at-risk population it targets, and the leading-indicator metric you'll watch.
Why it matters
Shows the board that retention risk is being actively managed, not just measured. Initiatives without measurement plans are typically performative — pairing each initiative with a leading-indicator KPI (engagement score, manager 1:1 cadence, refresh-grant acceptance) shows operational rigor.
Source
imboard Editorial

Talent Challenges

hr.talent_challenges
textEditorial
Description
Narrative on key hiring difficulties, attrition concerns, comp-market pressure, and market-driven talent risks that the board should weigh in on or be aware of. The "watch this" companion to `hr.talent_highlights`. Common pitfall: sanitizing this section to avoid uncomfortable conversations — but talent challenges are precisely where boards add the most value (warm intros, comp benchmarking, executive search). Best practice is to name the specific role, team, or risk and the ask explicitly.
Why it matters
The mechanism by which the board's network and judgment get applied to talent gaps. Numbers in `hr.voluntary_exits` and `hr.at_risk_count` show the symptom; this section names the cause and the ask.
Source
imboard Editorial

Talent Highlights

hr.talent_highlights
textEditorial
Description
Free-form narrative on notable hires, promotions, internal moves, and other positive organizational developments the board should be aware of. The "good news" companion to `hr.talent_challenges`. Common pitfall: listing every internal move and burying the genuinely important signals (key executive hires, strategic team-build milestones). Best practice is 3–5 bulleted items per period, each tied to a board-relevant outcome or risk-it-mitigates rather than a generic celebration.
Why it matters
Gives the board context for the headline numbers — a flat headcount with a major engineering leader hired tells a different story than a flat headcount with no narrative. Also primes board members for warm-intro asks and reference checks.
Source
imboard Editorial

Terminations

hr.terminations
numberEditorial
Description
Count of company-initiated employee separations during the period — performance-management exits, layoffs, redundancies, and for-cause terminations. The numerator of `hr.involuntary_turnover_rate` and the inverse of `hr.voluntary_exits` on the attrition page. Common pitfall: bundling layoff events (often one-time, board-known) with normal performance-management churn (steady-state, manager-driven). Best practice is to break out layoffs in `hr.talent_challenges` narrative and reserve this number for the recurring stream.
Why it matters
A direct read on performance-management cadence and any organizational restructuring activity. Spikes correlate with strategy pivots, post-fundraise rebalancing, or recovery from over-hiring — each implies different board narratives.
Source
imboard Editorial

Total Contractors

hr.total_contractors
numberEditorial
Description
Count of active 1099 contractors, consultants, agencies-of-record, and similar non-employee labor at period end. Tracked separately from `hr.total_headcount` because the cost structure, retention dynamics, and classification risk are different. Common pitfall: under-counting agencies that bill on a project basis without per-head visibility — these often slip out of HR systems and surface only in finance AP detail. A contractor-to-FTE ratio above ~30% sustained typically warrants a classification audit and a deliberate "build vs rent" board conversation.
Why it matters
Hidden capacity and hidden cost — contractors expand effective capacity without going through the headcount-approval gate, but they carry classification risk and tend to convert into permanent cost without explicit board approval. Surfacing the count counters that quiet expansion.
Source
imboard Editorial

Total Headcount

hr.total_headcount
numberEditorial
Description
Total number of employees (W-2 / direct-employment equivalents) across all departments at period end. The base denominator for nearly every other HR ratio — turnover rate, revenue per FTE, payroll as % of burn — so getting the snapshot date and the FTE-vs-headcount convention right matters. CANONICAL HEADCOUNT (#2056): this single KPI carries BOTH the plan and the reported figure via the scenario axis (#2019) — scenario=`budget` is the board-approved headcount plan (formerly the separate, now-deprecated `hr.approved_headcount_budget`), and scenario=`actual` is the reported end-of-period count. Budget-vs-actual variance is read off the two scenarios of this one definition. Common pitfall: mixing headcount (people) with FTE (capacity) — they diverge whenever part-time, contractor, or shared-services arrangements exist. Document the convention (typically "FTE-equivalent, employees only, end-of-period") at the board level once and apply consistently.
Why it matters
The denominator for every HR ratio the board reads — turnover %, revenue/FTE, payroll as % of burn. Drift in this number without a corresponding hiring-plan update is a leading signal of unmanaged growth or quiet attrition.
Source
imboard Editorial

Voluntary Exits

hr.voluntary_exits
numberEditorial
Description
Count of employees who resigned during the period (initiated by employee, not the company). The numerator of the `hr.voluntary_turnover_rate` calculation and the headline "are we losing people" number boards anchor on. Common pitfall: ambiguous "mutually agreed" exits — companies sometimes log managed-out exits as voluntary to keep the visible number low. Define the test: if the employee initiated the conversation and there was no formal performance trigger, it is voluntary; otherwise log as termination.
Why it matters
The leading indicator the board reads for retention health and culture risk. Concentration in a single team, level, or tenure cohort is more informative than the absolute number — investigate the pattern, not just the headline.
Source
imboard Editorial

Voluntary Turnover Rate

hr.voluntary_turnover_rate
percentage (%)Industry-backed
Description
Voluntary exits over a trailing period, expressed as an annualized percentage of average headcount — the headline attrition number on the HR scorecard. Anchored to the Mercer US Turnover Survey methodology (Mercer reports voluntary vs involuntary turnover annually). Common pitfall: comparing a single quarter's annualized rate against an annual benchmark — short-window annualization is noisy. Best practice is trailing-12-months for benchmark comparison and trailing-3 or trailing-6 for trend reads. Per #1426: stage-specific industry norms here are folk-wisdom unless tied to a specific Mercer or comparable published cut.
Why it matters
The canonical retention KPI investors and boards benchmark against. Tracks the cost of churn — every voluntary exit triggers a replacement-cost cycle (recruiting + onboarding + ramp), commonly estimated at 0.5–2× the role's annual salary depending on level (industry folk-wisdom, not citation-grade).
Benchmark
p25 7% · median 11% · p75 17%
Source
Mercer US Turnover Survey 2025

Product18 KPIs

Capacity Allocation

product.capacity_allocation
textEditorial
Description
Container handle for the structured 5-category engineering-capacity object — total engineers plus the per-bucket split across innovation, maintenance, tech debt, customer support, and sales support (each a percentage), with an optional innovation target. The bespoke product feed card renders this as the capacity-allocation donut the demo design shows. RICHER than the 3-way `product.capacity_allocation_pct` percentage triple — this carries the headcount + the five operating buckets the donut needs. Common pitfall: the five buckets not summing to 100% because a category (e.g. sales support) was omitted.
Why it matters
Shows where engineering headcount actually goes across all five operating modes — the gap between innovation and its target is the clearest read on whether the company is investing in the future or absorbed by run-the-business work.
Source
imboard Editorial

Capacity Allocation

product.capacity_allocation_pct
percentage (%)Editorial
Description
Breakdown of engineering capacity across new features, maintenance, and tech debt — typically reported as a three-way split summing to 100%. The execution-level view of where engineering hours are actually going (vs. `innovation_capacity_pct` which is a single percentage for new-capabilities work, and vs. `offensive_roadmap_pct` which is a roadmap-classification percentage). Common pitfall: capacity allocation reported in plan rather than actuals. The plan can say 60% new features but the actuals can be 30% new features and 50% support work — the gap is the operating signal. Boards should require both planned and actual splits, at least quarterly.
Why it matters
Names where engineering hours actually go. The plan-versus-actual gap is one of the highest-signal operational metrics for the board — a persistent 20+ point gap between planned and actual new-feature allocation is the loudest possible flag that the company is under-investing in platform health (the missing hours are going to firefighting).
Source
imboard Editorial

Churn from Quality Issues

product.quality_churn_pct
percentage (%)Editorial
Description
Percentage of customer churn (logo or ARR, define explicitly) where the primary stated reason is product or quality problems — bugs, performance, missing core functionality, reliability incidents. Distinguishes product-driven churn from pricing-driven, competitor-driven, or use-case-fit-driven churn. Common pitfall: relying on free-text exit-survey reasons. Customers commonly cite "price" when the underlying issue was reliability or missing features — boards should require both the customer-stated reason and the CSM/Account-Manager-assigned root cause, and watch the gap. The Pendo "Product-Led Growth Benchmark" and similar product-analytics publishers cover product-driven churn qualitatively, not as published numeric ranges.
Why it matters
Isolates the share of revenue loss the R&D organization can directly act on. High and rising quality-churn is the loudest signal that engineering investment should shift from new-feature to platform-hardening. Low quality-churn alongside high overall churn signals the problem is GTM or product-market-fit, not engineering.
Benchmark
p25 5% · median 10% · p75 20%
Source
imboard Editorial

Commitments Roadmap %

product.commitments_roadmap_pct
percentage (%)Editorial
Description
Share of roadmap capacity allocated to CUSTOMER COMMITMENTS — the third slice of the offensive / defensive / commitments roadmap mix (the slice the bespoke product card needs to complete the roadmap-mix bar). Offensive (`product.offensive_roadmap_pct`) is net-new market expansion, defensive (`product.defensive_roadmap_pct`) is retention/churn-prevention work, and this commitments slice is contractually or relationship-committed deliverables (e.g. enterprise SCIM/audit-log promises). The three should sum to 100%. Common pitfall: commitments work going untracked, so the roadmap looks more offensive than the team actually is.
Why it matters
Makes contractually-committed roadmap work visible as its own category — a high commitments share means the roadmap is increasingly dictated by sales promises rather than product strategy.
Source
imboard Editorial

Delivery Predictability

product.delivery_predictability
percentage (%)Editorial
Description
Percentage of committed deliverables shipped on or before the originally-promised date within a measurement window (typically a quarter). Surfaces whether the engineering organization can be trusted to hit commitments the company makes externally — to customers in contracts, to the board in quarterly plans, to GTM teams sequencing launches. Common pitfall: gaming. Teams over-deliver by under-promising (predictability climbs while velocity drops) or move the goalposts (re-baseline mid-quarter so "on-time" stays high). Boards should ask for "predictability against original commitment", not "against current plan", and pair with throughput trends.
Why it matters
Predictability is the contract between engineering and the rest of the business. When it slips, GTM cannot sequence launches, sales cannot promise dates, and the board cannot trust the quarterly plan. Sustained low predictability is a leading indicator of either capacity mismatch, planning hygiene problems, or accumulated technical debt.
Benchmark
p25 55% · median 70% · p75 85%
Source
imboard Editorial

Growth & Differentiation %

product.offensive_roadmap_pct
percentage (%)Editorial
Description
Percentage of the planned roadmap (typically next 1–2 quarters) allocated to offensive bets — net-new capabilities, market expansion, differentiation moats, new monetization. The "what proportion of the plan is about winning" view. Common pitfall: counting "improvements to existing features" as offensive when the change is really table-stakes parity work. Boards should expect a McKinsey-style horizon framing (Horizon 1 = core, Horizon 2 = adjacent, Horizon 3 = transformational) or an equivalent classification, and apply it consistently. Per the original McKinsey "Three Horizons" framing (Baghai/Coley/White, "The Alchemy of Growth", 1999), a healthy portfolio funds all three — over-indexing on any one is a strategic risk.
Why it matters
Encodes the company's strategic posture in one number. Boards use this to check the roadmap against the strategy narrative — a company saying it is "going on offense" while showing a 30% offensive roadmap has a story-versus-execution gap worth flagging.
Source
imboard Editorial

Innovation Capacity %

product.innovation_capacity_pct
percentage (%)Editorial
Description
Percentage of R&D capacity (typically measured in engineering-weeks or story points over a quarter) allocated to net-new capabilities, as opposed to maintenance, bug fixes, internal tooling, or customer-support engineering. The "available bandwidth for offense" view. Common pitfall: confusing innovation capacity (input — how much team-time is available for new work) with `offensive_roadmap_pct` (output — what proportion of the planned roadmap is growth-oriented). A team can have 60% innovation capacity allocated entirely to defensive work if the roadmap demands it. Boards should look at both together.
Why it matters
Surfaces structural friction. A team with only 20% innovation capacity is being eaten by maintenance and reactive work — the board should be asking why (platform debt, support load, headcount mismatch) before approving new feature commitments.
Source
imboard Editorial

Key Initiatives

product.key_initiatives
textEditorial
Description
Container handle for the field-array of strategic product initiatives committed for the current quarter / half — each entry tracks the initiative name, status (on-track / at-risk / blocked / shipped / cut), owner, target date, and a one-line explanation or mitigation plan. The structured, per-initiative companion to the `product.key_initiatives_status` narrative: the narrative gives the execution-pulse story, this gallery makes each initiative individually trackable with its own owner and status. Renders via the CollapsibleFormItemCardGallery widget (the reused gallery pattern shared with sales pipeline deals and HR key hires / openings). Common pitfall: every initiative defaults to "on-track" until two weeks before its deadline — require an explicit at-risk state with a mitigation plan, not a re-label at the deadline.
Why it matters
Connects the strategic narrative to delivery reality at the level the board can act on — surfaces where engineering needs unblocking and where commitments are slipping, per named initiative with a named owner. The board's most efficient leverage point on the product organization.
Source
imboard Editorial

Key Initiatives Status

product.key_initiatives_status
textEditorial
Description
Stoplight-plus-narrative status of the strategic product initiatives committed for the current quarter / half — each initiative ideally tagged on-track / at-risk / blocked / shipped, with a one-line explanation. The execution-pulse view that connects strategy intent to delivery reality. Common pitfall: every initiative defaults to "on track" until two weeks before the deadline, then turns red — a board that only sees binary green-or-red status without intermediate "at-risk" signaling is being managed reactively. Pair with `delivery_predictability` to detect this pattern; require at-risk initiatives to surface a mitigation plan, not just a label.
Why it matters
Connects the strategic narrative to delivery reality at the level the board can act on. Surfaces where engineering needs unblocking, where commitments are slipping, and where the strategy needs revision. The board's most efficient leverage point on the product organization.
Source
imboard Editorial

Product Portfolio

product.portfolio
textEditorial
Description
Container handle for the field-array of products in the portfolio — each entry tracks the product name, its portfolio classification (e.g. growth engine / cash cow / innovation bet / sunset candidate, or Horizon 1/2/3), ARR contribution, investment thesis, and lifecycle stage. The structured, per-product companion to the `product.portfolio_strategy` narrative: the narrative tells the story, this gallery makes each product line individually visible and trackable. Renders via the CollapsibleFormItemCardGallery widget (the reused gallery pattern shared with sales pipeline deals and HR key hires / openings). Common pitfall: a portfolio gallery that lists products without an explicit classification or investment thesis per item — that is an inventory, not a portfolio.
Why it matters
Forces explicit, per-product classification — boards offer better strategic guidance when every material product line is named with its game (growth / cash / option-value / sunset) rather than buried in a single narrative. Surfaces whether the company has a real portfolio or a list of products it happens to ship.
Source
imboard Editorial

Product Portfolio Strategy

product.portfolio_strategy
textEditorial
Description
Narrative overview of the product portfolio — which products are growth engines, which are cash cows, which are innovation bets, and which are candidates for sunset. The CEO/CPO articulation of "what game each product line is playing." Frequently structured along the McKinsey Three Horizons framing or the classic BCG growth-share matrix (stars / cash cows / question marks / dogs — per Bruce Henderson's "The Product Portfolio", 1970). Common pitfall: the portfolio narrative does not name horizons, life-cycle stages, or sunset candidates — a portfolio described entirely as "growth engines" is not a portfolio strategy, it is a wishlist. Boards should push for explicit classification of every material product.
Why it matters
Forces explicit articulation of the multi-product story — boards offer better strategic guidance when they understand which product is being optimized for growth vs cash vs option-value. Reveals whether the company has a portfolio strategy or just a list of products it happens to ship.
Source
imboard Editorial

R&D Efficiency

product.rd_efficiency
numberEditorial
Description
Ratio of net-new ARR generated in a period to R&D spend in the same period — answers "how much revenue does each R&D dollar produce?" Distinct from sales-efficiency metrics (Magic Number, CAC payback) which measure sales/marketing productivity. Common pitfall: R&D-driven ARR (new capabilities, expansion features) shows up on a 2–4 quarter lag after the spend — single-period ratios mis-state the relationship. Boards should look at trailing-twelve-month R&D efficiency, not month-over-month, and pair with `innovation_capacity_pct` to understand whether the spend is on growth bets or maintenance.
Why it matters
Highest-leverage indicator of whether R&D investment is converting into revenue. A persistent decline signals either an over-built team relative to demand, a feature-product fit gap, or accumulated tech debt slowing throughput — each prescribes different board action.
Benchmark
p25 0.15ratio · median 0.27ratio · p75 0.4ratio
Source
imboard Editorial

R&D Monthly Spend

product.rd_monthly_spend
currency (/month)Industry-backed
Description
Total monthly cash outflow on research and development — fully-loaded engineering, product, and design payroll plus tooling, infrastructure dedicated to product development, contractors, and direct R&D vendor spend. The "input" side of R&D efficiency. Common pitfall: companies report base-payroll R&D and exclude the loaded cost (benefits, stock comp at cash-cost basis, allocated rent, dev tooling), under-reporting true R&D burn by 25–40%. Boards should always ask whether the number is base-payroll, fully-loaded, or GAAP R&D expense — they tell different stories. The KBCM/Sapphire SaaS Survey reports R&D as a percentage of revenue for its company panel — use that as the benchmarking lens.
Why it matters
Largest single line of operating spend at most growth-stage SaaS companies — the input that `rd_efficiency` converts into revenue. The board reads this to gauge whether the company is over- or under-investing in product velocity relative to revenue ramp.
Source
KBCM/Sapphire SaaS Survey 2024 (15th Annual)

Revenue Protection %

product.defensive_roadmap_pct
percentage (%)Editorial
Description
Percentage of the planned roadmap allocated to defensive work — platform reliability, security/compliance, scalability rearchitecture, table-stakes parity with competitors, customer-retention features. The complement of `offensive_roadmap_pct`. Common pitfall: defensive work is chronically under-funded (less visible to customers, harder to demo) until a quality-churn or scalability event forces a reactive surge. Boards should treat sustained zero or near-zero defensive allocation in a maturing product as a leading indicator of future quality issues — per the standard product-management argument (Marty Cagan and similar product-leadership writing), a healthy roadmap pays both growth and platform-health rent.
Why it matters
Names the investment in not-losing alongside the investment in winning. A defensive % that responds to `quality_churn_pct` and `scalability_headroom` trends (rising when those degrade) is a sign of a healthy operating cadence; a defensive % stuck near zero while quality churn rises is a sign the board needs to push for re-prioritization.
Source
imboard Editorial

Time to Capacity Limit

product.scalability_headroom
number (months)Editorial
Description
Months of system capacity remaining at the current growth rate before the platform requires major (not incremental) infrastructure investment — typically driven by the binding bottleneck (database, message bus, single-tenant compute ceiling, regional capacity, or compliance-driven re-architecture). Surfaces the "scale runway" alongside the financial runway. Common pitfall: a single number hides which bottleneck binds. Boards should require the bottleneck to be named ("database shard hot-spot binds at ~150K accounts at current growth, ~4 months out"), not just the headline months — a named bottleneck makes the investment decision concrete.
Why it matters
Sequences major infrastructure work against revenue growth. A 6-month scalability headroom against a 9-month financial runway is a foreseeable crisis the board should be addressing now. Pairs naturally with `defensive_roadmap_pct` (which funds the work).
Benchmark
p25 4months · median 9months · p75 18months
Source
imboard Editorial

Top Product ARR Concentration

product.top_product_arr_concentration
percentage (%)Editorial
Description
Percentage of total ARR contributed by the single largest product line. Diversification-risk indicator at the product level (parallel to customer-concentration risk at the GTM level). Common pitfall: concentration risk is dismissed when the dominant product is performing well — but a one-product company is a one-feature-decision-away from existential risk. Boards should track this number alongside the portfolio narrative; sustained 70%+ concentration in a maturing company should pair with a documented diversification thesis or an explicit decision to remain a single-product company. Frames analogous to customer-concentration discussions in venture diligence (NfX / Bessemer founder essays cover the customer-side; the product-side analogue follows the same logic).
Why it matters
Quantifies single-point-of-failure risk in the product portfolio. The board reads this alongside `portfolio_strategy` to assess whether the company has a real second product or is effectively still single-SKU. Drives both strategic (build / buy / partner for diversification) and financial (valuation framing) conversations.
Source
imboard Editorial

Total Engineers

product.total_engineers
numberEditorial
Description
Headcount of engineers (software, infrastructure, security, data, ML) in the R&D organization, typically including full-time employees plus contractors at a defined FTE-equivalence factor. The "capacity input" side of all R&D ratios. Common pitfall: definition drift. Some companies include only software engineers, others include product managers and designers, others include all of R&D plus QA, plus support engineers. Boards should anchor the definition once and hold it stable — otherwise quarter-over-quarter comparisons are noise. Pair with `rd_monthly_spend` to derive fully-loaded cost-per-engineer.
Why it matters
Capacity denominator for every R&D ratio — `rd_efficiency`, ARR-per-engineer, cost-per-engineer, throughput-per-engineer. The board reads this to gauge whether team growth is keeping pace with revenue and product-surface-area growth.
Source
imboard Editorial

Weighted Feature Adoption

product.feature_adoption
percentage (%)Editorial
Description
Percentage of customers (weighted by ARR) actively using a defined set of strategic features within a measurement window. The "ARR-weighted" framing matters: a feature used by 30% of customers covering 70% of ARR is a different signal than 30% of customers covering 5% of ARR. Common pitfall: defining adoption as "ever used" rather than "actively using" (returning use in the measurement window) — the first metric only goes up and tells the board nothing. Boards should require an active-use definition (e.g. used in 2 of the last 4 weeks) and a per-feature breakdown for the strategic feature set.
Why it matters
Leading indicator of product-market fit for new capabilities. Adoption that does not reach a critical mass of ARR-weighted customers within 2–3 quarters is the strongest signal that the feature is either mis-targeted, mis-priced, or hidden in the UX. Drives roadmap continue-vs-cut decisions.
Benchmark
p25 40% · median 60% · p75 75%
Source
imboard Editorial

Operations1 KPI

Rule of 40

operations.rule_of_40
percentage (%)Industry-backed
Description
Composite SaaS health score that sums the company's revenue growth rate and a profitability proxy (commonly EBITDA margin or free-cash-flow margin) into a single percentage. Originally articulated by Brad Feld in 2015 and codified by the SaaS Metrics Standards Board, the rule frames the growth-vs-profitability tradeoff: a company growing at 60% with a −20% margin scores 40, equal to a company growing at 20% with a +20% margin. The board reads it to sanity-check whether growth is being bought at unhealthy burn or whether margin discipline is constraining growth too far. Common pitfall: which profitability proxy is used materially changes the score (FCF margin is the strictest, EBITDA more flattering, "operating margin" inconsistently defined), so pick one and disclose it next to the number.
Why it matters
Single-number readout of the growth-vs-burn tradeoff. Lets the board compare a high-growth / high-burn company to a slow-growth / profitable one on one axis, and surfaces unhealthy growth (high growth paid for with margin much worse than negative growth-rate offset).
Benchmark
p25 -4% · median 15% · p75 31%
Source
SaaS Metrics Standards Board

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