2026 Benchmark Report

The State of Workforce
Platforms.

How workers onboard, activate, get paid, and stay engaged. Data aggregated from Zeal-powered workforce platforms across January–December 2025.

200K+
Worker signups tracked
12
Months of data
5
Report chapters
2
Worker types (W2 + 1099)
Contractor Onboarding
~90%
contractor onboarding completion rate — consistent across 2025
↑ High activation
Speed to First Shift
~10 days
typical time from signup to first shift
↑ Fast activation
30-Day Return Rate
~88%
of first-shift workers return within 30 days
↑ Strong retention
Time to Pay
~96 hrs
typical hours between finishing a shift and money in the bank
↑ Fast pay cycle
Executive Summary

To be a high-performing workforce platform, operational execution is key.

Workforce platforms take many forms including staffing agencies, gig marketplaces, contractor networks, direct-dispatch platforms. What they share is the same operating challenge: activate workers fast, pay them reliably, and keep them coming back. The demand side of the market peaked sharply in 2022 and has been contracting since. In a stabilizing market, the gap between high-performing platforms and the rest comes down to operational fundamentals.

U.S. Staffing Revenue, 2019–2026F
The staffing segment is the most measurable proxy for the broader workforce platform market. COVID collapse → rebound → normalization → contraction → stabilization → modest recovery.
Source: Staffing Industry Analysts (SIA) — Global Staffing Industry Forecast, 2025. Revenue in USD billions.
Independent Workers in the U.S., 2011–2025
Full-time, part-time, and diversified independent workers. Structural growth has settled near 73M.
Source: MBO Partners — State of Independence in America Report, 2025. Figures in millions.
Key Findings
01 — Onboarding
~10 days
Median time to first shift — top platforms activate workers in under 9 days
02 — Payments
96-115 hrs
Time-to-pay range across platforms — fastest deliver funds within hours
03 — Retention
17-28%
Of workers do not return after their first shift — the second shift is the real signal
04 — Wages
~$9/hr
Wage spread from lowest to highest state — geography sets the floor
05 — Seasonality
34%
Swing between peak and trough worker utilization
Section 01 — Onboarding & Activation
This is some text inside of a div block.

~10 days

median time from signup to first shift
Onboarding & Activation

The first shift is the biggest drop-off point

Time to first shift varies meaningfully across platforms. That gap determines how quickly workers activate and whether they ever come back.

So much energy is put into signing up new workers, but activation doesn't happen at signup. It happens at the first shift. The longer it takes to get a worker from onboarding to their first shift, the higher the drop-off risk.

In our dataset, time to first shift varies meaningfully across platforms. Some platforms activate workers in under 9 days, while others take materially longer. That difference compounds quickly: faster feedback loops, earlier earnings, and a higher likelihood of a second shift.

The first shift is a critical moment that determines whether a worker stays or disappears.

The longer it takes to reach the first shift, the higher the drop-off risk.
In our dataset, time to first shift is one of the strongest early signals of whether a worker stays or disappears.
Employee
34%
Avg Completion Rate
Across all 2025 cohorts
<9 days
Best-in-Class Activation
Top platforms get workers to first shift in under 9 days
<9hrs
Median Onboard Time
Time from onboarding initiated to completed
Employee Onboarding Completion Rate by Month
Time to first shift varies across platforms. Faster activation leads to earlier earnings and stronger retention.
Contractor
Employee
<9hrs
Median onboarding time
Completion rate23–45%
Days to first shift19–123 days
p90 onboard time150–328 hrs
Contractor
<1 hr
Median onboarding time
Completion rate87–94%
Days to first payment3–7 days
p90 onboard time<4 hrs
34%
Employee Completion
Average across all 2025 cohorts
90%
Contractor Completion
Consistent 87–94% across all 12 months
2.6×
Contractor Advantage
Contractor completes at 2.6× the rate of Employee
Industry Context
85%
of candidates whose onboarding was automated said they’d keep working with their recruiter — vs. less than 50% where onboarding wasn’t automated
Bullhorn GRID Talent Trends Report, 2025 · 2,800 respondents
63%
of contingent workers have rejected assignments due to a poor onboarding experience — making digital onboarding a direct revenue driver
Darwinbox Workforce Study, 2025
4-5 days
how long it typically takes to fill a role in light industrial — making speed-to-first-shift a platform design problem as much as a recruiting one
American Staffing Association / Carv Staffing Benchmarks, 2024

Getting workers to their first shift is hard. What happens immediately after determines whether they come back.

Section 02 — Payments

96-115 hrs

time from shift end to funds available
Payments

Pay is where trust is won, or lost

Payment frequency varies widely across platforms — from instant payouts to weekly payrolls. Pay speed, frequency, and accuracy directly impacts worker trust and retention.

For workers, getting paid is the ultimate goal and a reliability test for your platform. A payment that arrives late or fails isn’t seen as a simple mistake—it’s treated as a broken promise. In our dataset, time to pay depends heavily on infrastructure, funding models, and payout methods.

~99%
Employee ACH Rate
Near-universal in our dataset
~44%
Workers Paid in a Given Month
In a typical month, roughly 44% of the active worker base receives a paycheck
4-5 days
Time to Pay — Dataset Range
Fastest platforms: near-instant. Slower platforms: nearly a work week
Employee Pay Speed & Frequency
Median Hours: Shift End → Funds Available
Time to pay varies significantly across platforms. The fastest deliver funds within hours, while others take multiple days depending on infrastructure and funding models.
Employee Pay Frequency Mix
% of workers by pay cadence — full year average
Contractor Pay Methods
Contractor Pay Method Mix — Disbursement Rail Breakdown
Distribution of payout methods in our dataset — ACH vs. paycard. Method choice reflects worker preference, not payment speed.
💳
Payout Rail Shapes the Experience — But Speed Is Determined Upstream
In our dataset, contractor pay method mix shows a meaningful shift toward paycard adoption — reflecting worker preference for flexibility. The underlying speed of payment is determined by platform infrastructure and funding models, not the rail alone.
Industry Context
55M+
U.S. workers had access to on-demand pay by late 2024 — with the market growing at 25.7% CAGR, reaching $6.2B globally
Market.us On-Demand Pay Market Report, Nov 2025
$15/hr
median pay for U.S. temporary W2 workers — compared to $25/hr for independent contractors — making pay speed a key differentiator
ADP Research — “A Tale of Two Labor Markets,” Nov 2025
~20 hrs/wk
average hours worked by temp and contractor workers — about half that of traditional W2 employees — directly shaping pay frequency demand
ADP Research — “A Tale of Two Labor Markets,” Nov 2025

Pay speed shapes trust. Trust shapes whether workers come back.

Section 03 — Worker Engagement

17-28%

of workers don’t return after their first shift
Worker Engagement

Retention looks strong — until you look closer

In our dataset, most workers return after their first shift. The real question is how many don’t and what that drop-off costs.

Whether a worker comes back for a second shift is a better indicator of your platform’s opportunity than getting them to their first shift.

In our dataset, 17–28% of workers do not return after their first shift. That drop-off matters more than it looks. It means more re-acquisition spend, less predictable labor supply, and weaker marketplace liquidity.

🔁
The second shift is where retention starts.
Not the first.
One-and-Done Rate by Cohort
Most workers return after their first shift, but early drop-off remains significant. The platforms that retain workers best reduce friction before and immediately after first earnings.
1 in 4
do not return after their first shift — in our dataset

Between 1 in 6 and 1 in 4 workers complete a first shift and never come back. Each one represents re-acquisition cost, lost supply, and a missed relationship.

Employee
3-4
Typical Shifts per Worker, per Month Consistent across all 2025 cohorts
16-20
Shifts / Month — “Super Workers”
The most active 10% work 4–5× more than the average
67-193
How Many Days Workers Stayed
Typical time working on platform before leaving — roughly 2 to 6 months
Industry Context
72%
of candidates stopped working with a recruiter due to slow or absent communication — speed of response is the #1 driver of contingent worker loyalty
Bullhorn GRID Talent Trends Report, 2025 · 2,800 respondents
87%
of contingent workers who get the hours they want say they’d continue working with their staffing firm — hours alignment is the single strongest retention signal
Bullhorn GRID Talent Trends Report, 2025
43%
of temporary workers are under age 30 — a mobile, digitally-native cohort with lower default loyalty and higher sensitivity to platform experience quality
ADP Research — “A Tale of Two Labor Markets,” Nov 2025

Retention is partly about experience. But it’s also about whether the pay is worth showing up for.

Section 04 — Wages / Pay Rates by State

~$9/hr

spread from lowest to highest wage state
Wages & Pay Rates

Where you operate determines what workers cost

In our dataset, median wages span nearly $15/hr from lowest to highest states. That gap shapes compliance, pay competitiveness, supply quality, and shift fill rates across every market you operate in.

Median base hourly wages range from $15–17/hr in lower-wage states to over $24/hr in markets like Washington and Colorado. That’s a nearly $10/hr spread across comparable W-2 roles. That spread has direct consequences for operators: what counts as competitive pay varies by market, and platforms operating across multiple states face asymmetric cost and pricing structures.

📍
What counts as competitive pay depends entirely on where your workers are.
A rate that attracts workers in Indiana may not move the needle in California — and vice versa.
$22-24
High-wage markets
CA, CO, WA — top of the range in our dataset
$21-22
Mid-range markets
NY, IL — near the dataset median
$15-18
Lower-wage markets
IN, TX, FL — bottom of the range
Median Base Hourly Wage by State — Full-Year 2025 Average (W2 Employees)
In our dataset, median base hourly wages span nearly $10/hr from lowest to highest states. Only states with sufficient worker volume are included. Overtime and double-time pay are not factored in.
Median Hourly Wage Trend — Top 6 States by Volume
Wage comparison across the top six states by worker volume. California and Colorado lead; Indiana and Texas remain below the national temp worker median.
🏆 Highest Wage States
WA — Washington$24.28
CO — Colorado$23.27
CA — California$22.59
NY — New York$21.74
📊 Mid-Range States
IL — Illinois$21.09
AZ — Arizona$19.94
OH — Ohio$19.62
MD — Maryland$19.08
⚡ Lower-Wage States
PA — Pennsylvania$19.41
GA — Georgia$18.79
FL — Florida$17.57
TX / IN$15–17

Wages tell you what workers cost. Seasonality tells you when you’ll need them most — and whether you’ll be ready.

Section 05 — Seasonality & Growth

34%

swing between peak and trough worker utilization
Seasonality & Growth

Utilization swings are predictable. Most platforms underplan for them.

In our dataset, shifts per active worker range from 5.6 to 7.5 across the year — nearly a third more activity at peak than trough. That variation has direct implications for staffing capacity, onboarding timing, and payment volume.

In our dataset, shifts per active worker swing meaningfully across the year — from a high of 7.5 in peak months to a low of 5.6 at the quietest point. That’s nearly a 34% difference in worker utilization between the busiest and slowest periods. The platforms that operate most efficiently treat seasonality as a planning variable.

📅
Utilization peaks don’t wait for your onboarding pipeline to catch up.
In our dataset, the gap between peak and trough months is nearly 35%. Platforms that plan around that curve fill shifts faster and waste less re-acquisition spend.
7.5
Peak Utilization
Top of the dataset range — highest shifts per active worker recorded
5.6
Trough Utilization
Bottom of the dataset range — nearly 35% below peak
6.5
Dataset Average
Median shifts per active worker — the floor most platforms plan around
Shifts per Active Worker — Monthly Benchmark
Shifts per active worker in our dataset. The gap between peak and trough months is nearly 35%, a meaningful signal for capacity and onboarding planning.
Month-over-Month Shift Volume Change — Seasonality Signal
Month-over-month shift volume changes in our dataset. Swings are significant — platforms that anticipate this variation can manage onboarding, staffing, and payment capacity more effectively.
📊
The staffing market isn’t growing — it’s stabilizing.
The next phase of growth won’t come from macro tailwinds. It will come from operational efficiency, speed, and technology. The platforms that win won’t wait for the market to lift them.
Industry Context
65%
of global company leaders plan to increase their use of contingent workers within two years — the single largest structural tailwind for workforce platform growth
Ceridian / Staffing Industry Analysts, 2025

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