Recruitment KPI Dashboard
- Source
- sample-data.csv · 5 rows
Time to fill
34 days
-5 daysOffer acceptance
89%
+2.0ppCost per hire
$3,420
-$280Open roles
12
-3Hires by source
Sample data — this quarter
Recruiting is a funnel, and its dashboard should read like one: where candidates come from, where they stall, what each hire costs, and how long roles stay open. Funnel math beats gut feel on every one of those.
Time to fill
34 days
-5 daysOffer acceptance
89%
+2.0ppCost per hire
$3,420
-$280Open roles
12
-3Hires by source
Sample data — this quarter
Live render with sample data — upload your own export and this structure regenerates from your numbers, with the computation attached to every figure.
Everything you need to know about using AnalyzeData.
Time to fill, pipeline conversion by stage, source effectiveness, offer acceptance, and cost per hire — the funnel, its speed, and its economics.
Export candidates and stage history from Greenhouse, Lever, or your ATS as CSV, upload, and ask for the funnel review. Conversion math computes from the actual stage rows.
Look at pipeline conversion, the pass-through rate from stage to stage: screen to interview to offer to hire. A funnel that looks slow overall usually has one weak joint, and stage-to-stage rates expose it. Then split time to fill by stage rather than reading the single total, since the bottleneck stage matters far more than the headline number when you are deciding what to fix first.
Measure source effectiveness: hires, and ideally quality-of-hire, per source set against the cost of each source. Volume alone misleads, because a cheap channel producing weak candidates loses to referrals that convert. In practice this usually reveals that referrals are underinvested while one job board is close to pure waste, letting you move budget toward the sources that actually close hires rather than just fill the top of the funnel.
Upload the export you already have — the dashboard computes itself, verifiably.
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