07 Hiring Analytics
Time-to-Fill
Without Illusions
Hiring speed matters — but context matters more.
The time-to-fill system
Role complexity
Specialist or generalist, rare or common
Process design
Friction and evaluation structure
Speed
Calendar days from open to offer
Outcome
Quality of the hire at the given speed
Time-to-fill is most useful as a diagnostic — not as a performance target. The number means very different things in different contexts.
Why this matters
Time-to-fill is one of the most commonly tracked and most commonly misinterpreted hiring metrics.
Tracking how long it takes to fill a role feels like a natural measure of hiring efficiency. Companies set targets — 30 days, 45 days — and measure themselves against them. When they miss the target, they feel inefficient. When they hit it, they feel effective. Neither conclusion is necessarily correct.
Time-to-fill is highly context-dependent. A principal ML engineer in a specialist domain takes longer to fill than a junior sales development rep — not because the company is less efficient, but because the market is fundamentally different. A role filled in 20 days that produces a mis-hire has a worse time-to-fill story than a role filled in 50 days that produces an exceptional hire.
The useful interpretation of time-to-fill is contextual and comparative: how does this search's duration compare to similar searches we've run, given role complexity, market conditions, and process design? Absolute targets are less useful than relative understanding.
Founder reality
Reframe time-to-fill as a diagnostic before using it as a target:
Do we segment our time-to-fill data by role type — or compare all roles to a single target regardless of complexity?
When a search takes longer than expected, do we understand whether the cause is market difficulty, process friction, or clarity failures?
Have we ever filled a role quickly and had it result in a poor hire — and counted that as a success?
What proportion of our time-to-fill duration is evaluation (signal-gathering) versus process friction (scheduling, approvals, alignment)?
Are we tracking time-to-fill alongside quality-of-hire — or as a standalone metric?
Time-to-fill as a standalone metric rewards speed without accounting for outcome. Pair it with quality-of-hire to make it useful.
The framework
Four factors that legitimately affect time-to-fill
Segment by these factors before benchmarking. Comparison without segmentation produces misleading conclusions.
01
Role complexity — specialist roles take longer than generalist ones
A compiler engineer, a regulatory affairs specialist, or a head of security have fundamentally smaller talent pools than a product manager or a business development executive. Benchmarking these roles against the same time-to-fill target is not useful. Create role complexity tiers and benchmark within tiers.
02
Process design — friction adds time without adding signal
A significant proportion of most time-to-fill duration is process friction: scheduling delays, waiting for internal approvals, slow debrief decisions, and late offer preparation. This time is reducible without affecting evaluation quality. Map the time distribution across the search and identify which components are signal-generating versus friction-generating.
03
Market conditions — talent supply and demand vary by role type and timing
Time-to-fill for ML infrastructure engineers in a period of high market demand is structurally longer than in a period of contraction. Benchmarking against internal targets without accounting for market conditions produces targets that are either always too easy or always too hard. Track market signals alongside internal performance.
04
Hiring quality — speed achieved at the cost of decision quality is negative ROI
A hire made in 21 days that leaves or underperforms within 6 months has an effective time-to-fill that includes the replacement search. When time-to-fill is tracked in isolation, fast-but-wrong hires look like wins. Track time-to-fill alongside 6-month retention and performance data to understand the real cost.
Common mistakes
01
Universal time-to-fill targets across all role types
A 30-day target that applies equally to a junior customer success hire and a principal security engineer is not a useful target for either. Segment by role complexity. Benchmark within segments.
02
Optimising for time-to-fill at the expense of quality
When time-to-fill becomes a primary performance metric, the pressure to hit it produces fast decisions without adequate evaluation. The metric then counts mis-hires as wins until the performance data tells a different story.
03
Counting offer-extended as fill, not offer-accepted or start-date
Time-to-fill measured at offer extension overstates speed. If the candidate declines, the clock resets. Measure from search open to candidate start date. This is the accurate vacancy duration.
04
No post-fill outcome tracking
Time-to-fill without a paired quality-of-hire metric is an incomplete signal. Track time-to-fill alongside 90-day performance ratings and 6-month retention. The combined picture is what makes the metric useful.
Example scenario
A Series B infrastructure company. Hiring report showed average time-to-fill of 38 days — leadership pleased with efficiency. Closer analysis revealed the picture was more complex.
The segmented analysis
Generalist roles (sales, ops, marketing): average time-to-fill 22 days. Post-hire 6-month retention: 89%.
Specialist technical roles (senior engineers, architects): average time-to-fill 54 days. Post-hire 6-month retention: 71%.
Average across both: 38 days — a number that concealed the specialist role underperformance and overrepresented the generalist success.
Two specialist hires who had left within 6 months: both filled in under 30 days — the fastest specialist searches in the period. Both had abbreviated evaluation processes.
The reframe
Time-to-fill targets separated by role tier.
Specialist roles: 60-day target introduced (previously 45). Evaluation investment increased.
Quality-of-hire tracking introduced alongside time-to-fill as the paired metric.
The outcome
Specialist role 6-month retention improved to 88% in the following year. Average specialist search time: 52 days — below the new target. No deterioration in generalist search metrics. The reframe made the metrics more honest — and more useful.
Takeaway
Time-to-fill is a signal, not a verdict.
Segment by role complexity, separate friction time from evaluation time, and pair every time-to-fill number with a quality-of-hire outcome. The metric becomes useful when it drives better decisions — not when it drives faster ones.
Related thinking
Hiring Analytics
The Cost of an Empty Role
An unfilled role quietly costs more than most companies realise.
Hiring Analytics
Hiring Speed vs Hiring Quality
Fast hiring and good hiring are not enemies.
Hiring Analytics
Metrics That Actually Matter
Not every hiring metric deserves attention.
Continue reading
The Funnel Problem