TL;DR

Fill rate is the percentage of job requisitions (or shifts) that a staffing agency or internal recruiting team successfully fills within a defined time period. It is calculated as filled orders divided by total orders received, expressed as a percentage. Fill rate is one of the most direct measures of a staffing operation's effectiveness, and low fill rates are the primary driver of client attrition in temporary staffing.

How Fill Rate Is Calculated and Interpreted

Fill rate = (Number of positions filled / Number of positions requested) x 100. If a staffing agency receives 200 shift requests in a week and fulfills 170 of them, the fill rate is 85%. The calculation sounds simple, but what counts as "filled" and when the measurement window closes are critical to making the metric meaningful.

A fill that arrives two days late is technically a fill, but it may not prevent the client from calling a competing agency. Many agencies track "on-time fill rate" separately, measuring only positions filled by the time the client needed coverage. A 90% overall fill rate that hides a 65% on-time fill rate tells a very different story about operational quality.

Fill rate varies significantly by sector and role type. Light industrial staffing agencies typically target 85% to 95% fill rates for standard roles. Healthcare staffing agencies filling per diem nursing shifts often operate at 70% to 80%, because the supply of available licensed clinicians is constrained and no-shows are higher. Technology staffing firms placing contractors in specialized roles may have lower fill rates by volume but generate more revenue per filled position. Benchmarks should always be interpreted within the relevant vertical.

Why It Matters for Recruitment

Fill rate is the primary performance metric clients use to evaluate a staffing agency, and it directly determines whether a client stays or leaves. Most enterprise staffing agreements include fill rate SLAs, commonly 85% or higher. Agencies that consistently miss SLA thresholds face financial penalties, reduced order flow, or contract termination.

From an internal operations perspective, fill rate diagnoses supply-side health. A declining fill rate typically signals one of three problems: a shrinking talent pool (not enough candidates in the database or on the bench), a matching problem (candidates are available but the agency is not efficiently connecting them to open orders), or a quality problem (candidates are placed but are cancelling shifts, not showing up, or being turned away at the client site). Each root cause requires a different response.

Fill rate also interacts with gross margin in ways that matter for profitability decisions. An agency might choose to decline certain order types that have historically low fill rates because unfilled orders consume dispatcher and recruiter time with no revenue outcome. Tracking fill rate by client, role type, and geography lets operations managers make informed decisions about where to allocate sourcing effort.

In Practice

A light industrial staffing agency manages 12 client accounts, averaging 180 shift requests per week. Their overall fill rate is 81%, below the 87% target in their largest client's contract. That client accounts for 40% of revenue and has flagged the shortfall in a quarterly review.

The agency pulls fill rate data by shift type. Day shifts fill at 94%. Evening shifts fill at 73%. Overnight shifts fill at 58%. The supply problem is time-specific, not general. The agency launches a targeted overnight shift differential: workers who accept overnight shifts receive an additional $1.50 per hour, funded by a 3% bill rate increase the client approves given the documented supply constraint.

Over eight weeks, overnight fill rate climbs from 58% to 79%. Overall fill rate moves from 81% to 88%, exceeding the contract SLA. The client renews the contract for a second year and adds two additional sites to the agreement, growing that account from $1.1 million to $1.6 million in annual revenue.

The agency also introduces a fill rate dashboard updated every four hours during the business day, allowing dispatchers to see which open shifts are at risk of going unfilled and prioritize outreach accordingly. Average time-to-fill drops by 22% within 60 days of the dashboard going live.

Key Facts