Time-to-Fill Benchmarks by Search Type: Retained vs Contingent vs Exec Search

Forty-two days. That’s the average time-to-fill you’ll find quoted in almost every recruiting article.

It comes from SHRM’s benchmarking of corporate talent acquisition teams filling their own roles.

If you run a search firm, that number was never about you. You are not filling your own headcount.

You are working someone else’s mandate, on their decision cycle, against two other firms, and getting paid only if you win.

This post covers time-to-fill benchmarks by search type: what good looks like for retained, contingent and executive search, why the same metric behaves so differently across the three, how to calculate it without flattering yourself, and where the days actually disappear.

TL;DR

• The widely quoted 42-day time-to-fill benchmark comes from in-house talent acquisition data. It is not a search firm benchmark, and measuring a retained mandate against it is meaningless.
• Contingent roles broadly track the all-roles baseline. Retained searches target 90 to 120 days to offer. Executive and C-suite searches run 120+ days, with CEO vacancies averaging 149.
• The difference isn’t effort. It’s conversion. Retained searches convert 77.9% from shortlist to interview. Contingent converts 44.3%. Interim converts 15.8%.
• Most firms calculate time-to-fill from the wrong start date, which quietly hides the slowest part of the process.
• The biggest recoverable delay sits before first shortlist, not after. Firms that shorten that stage move the whole number.

The benchmark you’re being measured against isn’t yours

The 42-day figure comes from SHRM’s Human Capital Benchmarking data. It describes an in-house talent acquisition function: one employer, one requisition, one hiring manager, no competing firm working the same brief.

Almost nothing about that setup matches how a search firm works.

A contingent role can be filled and cancelled in the same week. A retained mandate has a calibration meeting before outreach even starts. An executive search can spend three weeks on market mapping before a single approach is made.

Same metric name. Three different businesses.

The practical consequence is that a lot of firms are either reassuring themselves with a benchmark they’re beating for the wrong reasons, or apologising to clients for a number that was always going to look slow next to a corporate average.

Time-to-fill benchmarks by search type

Here is what the available data actually supports. Every figure is sourced.

One caveat before the table: these come from different studies with different methodologies. Treat them as ranges to orient against, not a single unified dataset.

Two things worth pulling out.

Executive searches run two to three times the all-roles median. That is not underperformance. It is the shape of the work: smaller candidate universes, longer approach cycles, more stakeholders on the client side, and a board that meets monthly.

Contingent has no committed timeline, and that is the defining feature. A retained search has a target date because the client has paid for one. Contingent speed is set by whoever moves fastest, which is why contingent time-to-fill has the widest spread of any model.

Time-to-fill vs time-to-hire: the distinction that changes the number

These two get used interchangeably and they measure different things.

Time-to-fill starts when the role is approved or the search is opened, and ends when the offer is accepted. It measures the whole process, including client-side delay.

Time-to-hire starts when a candidate enters the process, and ends when that candidate accepts. It measures your process efficiency on the person who actually got hired.

Time-to-fill is always the longer number, and it’s the one clients feel. Time-to-hire is the one that tells you whether your process is any good.

A firm with excellent time-to-hire and terrible time-to-fill has a pipeline problem, not a process problem. The machine works. It just started late.

Track both. Report time-to-fill to clients, and manage against time-to-hire internally.

Why the benchmark changes by search type

The timeline differences aren’t about how hard anyone is working. They come from where each model loses candidates.

Recruiterflow’s benchmark data across 2,100+ firms breaks conversion down by engagement type (Source: The Economics of Recruiting):

The submission-to-interview row is the whole story.

Retained converts 77.9% from client intro to interview. When a search has been calibrated up front, with profiles reviewed against the brief before any approach, almost every candidate presented gets seen.

The front of the process is slow by design. That investment is exactly what makes the back of it efficient.

Contingent converts 44.3%. Less than half of what gets submitted converts to an interview.

Contingent shortlists are built on an assumption of what the client wants rather than an agreed definition. Speed at the front costs conversion at the back.

Interim converts 15.8%, then 36.4% of interviews become hires. The highest interview-to-hire rate of the three models sits behind the lowest submission-to-interview rate. Contract hiring is a volume filter with a fast close.

Read down the columns and the timelines explain themselves.

Retained is slow at the start and clean at the end. Contingent is fast at the start and leaky in the middle. Interim throws volume at a narrow gate and closes quickly when something fits.

If your retained submission-to-interview sits near contingent’s 44%, your calibration isn’t working. You’re running a retained fee on a contingent process.

How to calculate time-to-fill correctly

The formula is simple. The start date is where firms deceive themselves.

Time-to-fill = (Date offer accepted minus Date search opened) ÷ number of placements, averaged across the period.

Three rules that make the number honest.

Start the clock when the client engages, not when you post the role. If a mandate lands on Monday and the search brief isn’t agreed until Thursday, those days belong in the number. They are the days the client is waiting.

Don’t exclude searches you failed to fill. A firm that only counts placements will always look faster than it is, because the slow searches are the ones that die. Report fill rate alongside time-to-fill or the metric flatters you.

Segment by search type before you average. A blended average across contingent and retained is a number that describes neither. Every benchmark in this post is useless the moment you average across models.

Then track the internal stage that actually predicts the total: days to first shortlist.

It is the earliest reliable signal of whether a search is on track, and the one you can still do something about.

Where the days actually go, and how to get them back

Break a slow search down and the lost time is rarely in interviewing.

It sits in three places: finding out who’s available, getting the first shortlist out, and keeping the process warm while the client decides.

The database you already have is the fastest source of candidates, if you know who’s still relevant. Recruiterflow’s benchmark data found that 71% of placements come from candidates already in the CRM before the job ever opened (Source: The Economics of Recruiting).

The delay isn’t that the people aren’t there. It’s that nobody knows which of them moved, got promoted, or became reachable since you last spoke.

The AIRA Job Change Alert Agent monitors your database for exactly those movements, and firms using it cut time to first submittal by 34%.

Shortlist assembly is where AI moves the needle most. AIRA Matchmaker matches candidates against a brief by meaning rather than keyword.

The AIRA Submission Agent drafts and sends branded shortlists, cutting submission time by 70%. Both compress the stage that determines the total.

Keeping a search warm is a discipline problem, not a speed problem. Multichannel sequences ensure follow-up actually happens across email, LinkedIn and phone.

Partners and associates still craft the message. The platform makes sure it gets sent and tracked.

The proof is worth sitting with. Mercury Hampton cut average time-to-fill from 106 days to 37 over nine months, a 65% reduction.

They did it while client submissions rose 164%. Faster and more, not faster instead of more.

That’s the tell that the gain came from removing delay rather than lowering the bar.

Frequently asked questions

What is the time-to-fill industry standard?

Roughly 42 days across all roles, based on SHRM’s Human Capital Benchmarking data. That figure describes in-house talent acquisition teams. Search firms should benchmark against their own model instead: contingent near the baseline, retained 90 to 120 days, executive 120+.

What is a good time-to-fill for executive search vs contingent?

For executive search, 90 to 120 days to offer is a healthy target, rising to 120+ days at C-suite level and around 149 days for CEO vacancies. Contingent roles broadly track the 42-day all-roles baseline but vary far more widely, because no committed timeline exists.

What is SHRM’s average time-to-fill?

SHRM’s benchmarking data puts average time-to-fill at approximately 42 days, or about six weeks. It covers corporate talent acquisition functions filling their own roles, not the work a search firm does for a client.

What is the average time to fill by industry?

It varies widely, and industry is usually a weaker predictor than role seniority and search model. A director-level search runs 42 to 84 days and a C-suite search 84 to 112+ days regardless of sector, so segment by search type and seniority before you segment by industry.

What’s the difference between time-to-fill and time-to-hire?

Time-to-fill measures from when the search opens to offer acceptance, including client-side delay. Time-to-hire measures only from when the successful candidate entered the process. Time-to-fill is what the client experiences. Time-to-hire is what your process controls.

What AI recruiting tools reduce time-to-fill?

The tools that compress the stages before first shortlist. Job change monitoring surfaces who in your database became available, AI matching assembles shortlists in minutes rather than days, and automated submission drafting removes the write-up delay. Recruiterflow’s AIRA agents cover all three, and firms using Job Change Alerts cut time to first submittal by 34%.

How do you calculate time-to-fill?

Subtract the date the search opened from the date the offer was accepted, then average across placements in the period. Start the clock when the client engages rather than when you post the role, include unfilled searches in your reporting, and segment by search type before averaging.

The number only matters if it’s the right number

Time-to-fill is worth measuring. It is not worth measuring against a benchmark built from someone else’s business model.

Segment it by search type, start the clock honestly, and track days to first shortlist as the leading indicator. Compare yourself to firms doing the work you actually do.

Then the metric stops being a stick your clients beat you with. It starts being the thing that tells you which search is about to go wrong, while there’s still time to fix it.