Actual Burn Rate (Past Period)
Definition
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.
How it's calculated
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. How to interpret it
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
Stage relevance
Typically owned by
Related KPIs
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.
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.
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.
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).
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