The Hiring Equation · Research Q2 2026

The relationship between screening inputs and production outputs.

Licensing requirements exist across regulated industries. Standard selection tools — LIMRA's Career Profile among them — have not incorporated license status as a predictive measure since 1982. A Fortune 500 insurance carrier analyzed 10,362 agents from 2022–2025 to measure which screening fields actually predict first production milestone achievement. This page is the long form of what they found.

Licensed candidates24.9%891 agents · production rate
Unlicensed candidates33.1%9,471 agents · production rate
experience_breakdown · 2022–2025APC % at 12 mo · by industry experience
Cohort2022202320242025
With insurance experience37.6%32.3%32.4%18.1%
Without insurance experience42.5%40.8%35.6%21.7%
The System · Objects + Framework

Companies screen candidates on measurable fields. Each field is testable.

The equation holds regardless of whether the screen predicts production — the right signal must be identified. If a signal predicts both P and t, then ΔP = k × Δt. Total yield scales linearly: ΔProduction = k × Δt × n.

  • Slicense (y/n)
    Binary status indicating professional licensure.
  • tdays to first milestone
    Days from hire to first production event (SNA).
  • Pannual production
    Annual production per person. APC in this dataset.
  • nhires per year
    Annual intake cohort the equation is applied over.
  • kspeed-to-production constant
    Derivable when correct signal exists. Derived below.
S tested against P · Anti-predictive

The carrier parsed 8,181 unique skills. Zero predicted production after Bonferroni correction.

Keywords tested
8,181 unique skills parsed from candidate profiles. 3,597 had sufficient sample size (n≥5).
8,181
Predictive post-correction
After Bonferroni correction for multiple comparisons, zero keywords predicted production.
0
Significantly anti-predictive
30 keywords were significantly anti-predictive. 70.2% of all keywords directionally negative (OR < 1).
30
Resume length
r = −0.090, p < 0.0001. 0–10 skills → 36.1% production rate. 51+ skills → 22.7%.
r = −0.090
Insurance experience
Candidates with industry experience produced at 28.0% vs. 33.7% for candidates without.
−5.7 pts

Requiring industry experience alone would have rejected 2,863 producing agents — eliminating $17.7M in annual production. The screen selects against producers.

Carrier internal analysis · 2022–2025
Scoring Deployed · 2025

Same carrier. Scored 679 hires in 2025. Speed and production inverted.

Speed measured as days from contract to first production milestone; production as annual output per person.

speed_vs_production · APC per personwith vs without scoring
Speed bucketWith scoring (2025)Without scoring (2022–2024)
0–30 days$13,137n = 0
31–60 days$11,219$10,033
61–90 days$10,395$11,665
91–120 days$8,030$14,246
121+ days$7,284$14,410
With scoring
r = −0.258, p < 0.001 · moderate inverse correlation with 99.9% confidence. Faster → more production.
r = −0.258
Without scoring
r = +0.045, p = 0.15 · no meaningful correlation between speed and production.
r = +0.045
Derive k · The constant

Scoring predicts both P and t. That makes k derivable.

Linear regression · 2025 deployment
APC = $13,964 − $54.35 × days
k = $54.35 / day / person

Each day faster to first production milestone correlates with $54 more annual production per person. Scales linearly with observed acceleration and annual hire volume.

Observed accel
Days faster to first production milestone after scoring deployed.
47 days
At 2,000 hires
$54 × 47 days × 2,000 = additional annual production attributable to k alone.
$5.11M
Pipeline reduction
5.5 hires/day × (109 − 62 days) = candidates removed from active pipeline.
259 people
Run this on your numbers

Your inputs. Our coefficients. Your number.

Uses the same k = $54.35/day/person derived above, applied to your hire volume and production rate. Conservative — bounded at 95% ceiling and 80% floor so it can't over-promise. Webhook logs each run so we can sanity-check against your own data later.

You hire people. Some produce. Some don't.
What would it mean if your production rate doubled?

Enter a number
%
% of hires who hit their performance target within 12 monthsEnter a percentage (1–100)
$
Not sure? Use your average deal size or annual quota attainment per rep.Enter a dollar amount
$
Recruiting, training, licensing, desk, management. Industry avg: $40K–$60K.
Estimated annual value
based on observed production data at a Fortune 500 carrier
Where this comes from
Revenue from additional producers
Failed-hire costs avoided
Speed-to-production gain

Get the full breakdown with net value, payback period, and what this means per hire.

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See this analysis on your actual data →
↻ recalculate with different numbers

Based on 1,690 producing agents at a Fortune 500 insurance carrier.
Avature ATS + HRIS production data. 2022–2025.

System output · Individual level

Five agents. Four traditional rejects produced. One clean resume did not.

Agent · 01 · rejected-by-ATS
84
No credentials. Rejected by traditional screening.
$22,710 produced
Agent · 02 · rejected-by-ATS
85
No credentials. Rejected by traditional screening.
$23,440 produced
Agent · 03 · rejected-by-ATS
85
No credentials. Rejected by traditional screening.
$22,940 produced
Agent · 04 · rejected-by-ATS
86
No credentials. Rejected by traditional screening.
$22,319 produced
Agent · 05 · passed-by-ATS
31
Perfect resume. Approved by traditional screening.
$5,280 produced

Four "rejects" combined produced $91,409. One "clean resume" produced $5,280. Across 677 candidates with zero traditional ATS keywords but a Nodes score of 75+: 33.7% production rate vs. 20–27% for the screened-in cohort.

Zero-keyword cohort · n = 677 · carrier 2025
The General Case

Every ATS has its own S. Production data always contains P.

The equation holds regardless of whether S predicts P — the right signal must be identified. Companies can only test relationships on hired candidates; rejected candidates have no production data, which limits signal discovery to applicant survivors of initial screening. Fields change. The equation doesn't.

Methodology · Data Sources

Sources + parameters

Sample
1,690 producing agents total. 679 in 2025 deployment cohort (real-time scoring). 1,011 in 2022–2024 comparison cohort (retroactive scoring).
n = 1,690
Production metric
Annual premium credit (APC), measured 12 months post-hire.
APC
Speed metric
Days from contract to first SNA (first production milestone) event.
days to SNA
Regression (2025)
APC = $13,964 − $54.35 × days_to_SNA · r = −0.258 · p < 0.001
p < 0.001
Pre-deployment slope
+$43.25/day · p = 0.15 · not significant
ns
Data sources
Avature ATS + HRIS + scoring (PI), linked via ATS Record ID. Fortune 500 insurance carrier.
—

Deployment posture

Architecture

  • SOC 2 Type II
  • Single-tenant VPC
  • Zero data egress
  • Customer-owned model weights

Monitoring + transparency

  • Bias monitoring for adverse impact
  • Explainable per-candidate decisions
  • Full audit logs (EEOC / OFCCP)
  • Model versioning + human-in-the-loop

Access + integration

  • SSO (Okta, Azure AD, OneLogin)
  • Role-based access control
  • Encrypted ATS sync
  • AWS · Azure · GCP · on-prem
14% → 28%

At 14% production rate, 59% of a year's hires never produce a single sale. The carrier improved to 28%: 160 additional producing agents, $3.19M premium credit generated, $8M avoided failed-hire costs, $630K net savings per 100 hires.

30 min · your data · no pitch deckReview the deployment