FTE Metrics
Definition
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.
How it's calculated
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. How to interpret it
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
Stage relevance
Typically owned by
Related KPIs
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. 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.
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.
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.
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.
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.
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