[ { "rogueId": "hr.arr_per_fte", "slug": "arr_per_fte", "domain": "hr", "defaultLabel": "ARR per FTE", "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.", "fieldType": "currency", "unit": null, "maturity": "general", "suggestedForStages": [ "seriesA", "seriesB", "seriesC", "public" ], "defaultOwningFunctions": [ "HR", "Finance" ], "stageRelevance": { "seriesA": "core", "seriesB": "core", "seriesC": "core", "public": "core" }, "definitionSource": { "tier": "published", "sourceName": "SaaS Capital Annual Survey 2025 (14th Annual)", "sourceUrl": "https://www.saas-capital.com/blog-posts/revenue-per-employee-benchmarks-for-private-saas-companies/", "sectionRef": "Revenue per Employee", "publicationDate": "2025-06-01", "attributionNotice": null }, "benchmark": { "p25": 100000, "median": 130000, "p75": 175000, "unit": "$", "sourceName": "SaaS Capital Annual Survey 2025 (14th Annual)", "sourceYear": "2025", "higherIsBetter": true }, "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).", "whyItMatters": "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?", "interpretationGuidance": "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.", "relatedKpiIds": [ "sales.arr", "hr.total_headcount", "hr.fte_metrics", "hr.payroll_run_rate", "operations.rule_of_40" ] }, { "rogueId": "hr.avg_days_to_fill", "slug": "avg_days_to_fill", "domain": "hr", "defaultLabel": "Average Days to Fill", "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.", "fieldType": "number", "unit": "days", "maturity": "general", "suggestedForStages": [ "seriesA", "seriesB", "seriesC", "public" ], "defaultOwningFunctions": [ "HR" ], "stageRelevance": { "seriesA": "recommended", "seriesB": "recommended", "seriesC": "recommended", "public": "recommended" }, "definitionSource": { "tier": "published", "sourceName": "SHRM Talent Acquisition Benchmarking Report", "sourceUrl": "https://www.shrm.org/topics-tools/research/talent-acquisition-benchmarking-report", "sectionRef": "Time-to-Fill", "publicationDate": "2023-01-01", "attributionNotice": null }, "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.", "whyItMatters": "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.", "interpretationGuidance": "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).", "relatedKpiIds": [ "hr.open_positions", "hr.hiring_plan", "hr.new_hires", "hr.key_openings", "hr.key_hires" ] }, { "rogueId": "hr.involuntary_turnover_rate", "slug": "involuntary_turnover_rate", "domain": "hr", "defaultLabel": "Involuntary Turnover Rate", "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.", "fieldType": "percentage", "unit": "%", "maturity": "general", "suggestedForStages": [ "seriesA", "seriesB", "seriesC", "public" ], "defaultOwningFunctions": [ "HR" ], "stageRelevance": { "seriesA": "recommended", "seriesB": "recommended", "seriesC": "recommended", "public": "recommended" }, "definitionSource": { "tier": "published", "sourceName": "Mercer US Turnover Survey 2025", "sourceUrl": "https://www.imercer.com/articleinsights/workforce-turnover-trends", "sectionRef": "Involuntary Turnover", "publicationDate": "2025-03-01", "attributionNotice": null }, "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.", "whyItMatters": "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.", "interpretationGuidance": "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).", "relatedKpiIds": [ "hr.terminations", "hr.performance_watch_count", "hr.voluntary_turnover_rate", "hr.talent_challenges" ] }, { "rogueId": "hr.voluntary_turnover_rate", "slug": "voluntary_turnover_rate", "domain": "hr", "defaultLabel": "Voluntary Turnover Rate", "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.", "fieldType": "percentage", "unit": "%", "maturity": "general", "suggestedForStages": [ "seriesA", "seriesB", "seriesC", "public" ], "defaultOwningFunctions": [ "HR" ], "stageRelevance": { "seriesA": "core", "seriesB": "core", "seriesC": "core", "public": "core" }, "definitionSource": { "tier": "published", "sourceName": "Mercer US Turnover Survey 2025", "sourceUrl": "https://www.imercer.com/articleinsights/workforce-turnover-trends", "sectionRef": "Voluntary Turnover", "publicationDate": "2025-03-01", "attributionNotice": null }, "benchmark": { "p25": 7, "median": 11, "p75": 17, "unit": "%", "sourceName": "Mercer US Turnover Survey 2025", "sourceYear": "2025", "higherIsBetter": false }, "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.", "whyItMatters": "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).", "interpretationGuidance": "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.", "relatedKpiIds": [ "hr.voluntary_exits", "hr.involuntary_turnover_rate", "hr.at_risk_count", "hr.retention_initiatives", "hr.talent_challenges" ] } ]