Capability
Hiring analytics, benchmarks and talent rediscovery
Three related things: knowing where your funnel loses people, knowing whether that is normal, and remembering the good talent you already met.
CalHire’s analytics cover three areas. Funnel metrics show where applicants enter, stall and drop out of your own hiring process. Benchmarks compare those metrics against aggregated data from other companies, published only where the cohort is large enough that no individual company or talent can be identified. Rediscovery surfaces strong talent already in your pipeline whose verified skills match a newly opened role.
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The short version
- Funnel metrics show drop-off by stage, which is usually where a hiring problem actually lives.
- Benchmarks are only published above a minimum cohort size, because a benchmark computed from three companies describes three companies.
- Rediscovery reuses assessments you already have, so strong talent for a closed role is not lost.
- Analytics never expose another tenant’s data, and never re-identify a talent.
The metric most teams track, and the one that matters
Time to hire is the number almost every hiring team reports, and on its own it is close to useless. You can improve it by lowering your bar, by hiring the first adequate applicant, or by counting from a later starting point. All three look like progress on a dashboard.
The useful version is drop-off by stage. A funnel that loses 80 percent of applicants at the assessment invitation is telling you something specific: either the invitation is unclear, the assessment looks too long, or the role attracted people who were never going to complete it. A funnel that loses applicants at offer is telling you something entirely different and considerably more expensive.
Stage-level data also makes fairness measurable. Adverse impact usually concentrates at one stage rather than spreading evenly, and you cannot find that stage from an aggregate. This is the same data the compliance console uses for selection-rate analysis, viewed for a different purpose.
What you can see
Stage-by-stage drop-off
Where applicants enter, where they stall, and where they leave. The shape of the funnel rather than a single duration.
Time in stage
How long applicants wait at each step. Waiting is the most common cause of losing good talent and the easiest to fix once it is visible.
Score distributions
How your applicant pool actually scores against your requirements. A role where everyone clears the bar is a role whose bar is not doing anything.
Selection rates by stage
The same data the adverse-impact analysis reads, so a fairness question and a performance question are answered from one source.
Benchmarks against other companies
Aggregated, anonymised, and published only above a minimum cohort size. Useful for answering whether a number is bad or simply unfamiliar.
Rediscovery
Talent already assessed, matched against a newly opened role. Their verified score is still valid, so the expensive part is already done.
How benchmarks stay safe to publish
Cross-tenant benchmarks are genuinely useful and genuinely risky. Aggregate carelessly and you can reconstruct an individual company’s hiring data, or in a small enough segment, an individual talent’s result.
The protection is a minimum cohort size. A benchmark is only computed and shown once enough companies and talent sit behind it that no single one materially moves the number. Below that floor the benchmark is suppressed rather than shown with a caveat, because a caveat next to a number does not stop anyone reading the number.
This means some segments have no benchmark, particularly narrow roles in small markets. That is the correct outcome. A comparison drawn from four companies is not a market rate, and presenting it as one would be worse than presenting nothing.
How rediscovery works
- 1
A role opens
With its required skills and weights, the same as any other.
- 2
Existing verified profiles are matched against it
Talent who applied to other roles, or who are in your pool, and whose verified scores are still within validity.
- 3
You review matches anonymously
Same anonymous surface as any other talent. Previous history with your organisation does not identify them to a reviewer.
- 4
You invite, they choose
An invitation, not an automatic re-entry into a pipeline. A talent who declined you once is entitled to decline again without explanation.
What this does not do
Analytics do not explain causes. A drop-off at one stage tells you where to look, not what is wrong. The answer usually requires talking to talent, and the report cards give you a reason they might be willing to reply.
Benchmarks are not available for every segment, by design. Narrow roles and small markets fall below the cohort floor and show nothing rather than something unreliable.
Rediscovery only reaches talent with a valid verified profile. Someone whose score has expired needs to retake before their profile carries a current result, and no amount of matching can substitute for that.
None of this measures the people who never applied. The most consequential bias in most hiring processes happens before anyone reaches your funnel, and no funnel analytic can see it. Where the job was posted and how it was written matter more than anything on this page.
Questions people actually ask
Can we see other companies’ hiring data?
Why is a benchmark missing for our role?
Does rediscovery contact talent automatically?
Can analytics identify individual talent?
Do these metrics feed the compliance reporting?
Related
Where this connects to the rest of the platform.
AI governance, bias auditing and the audit trail
Adverse-impact analysis on the four-fifths rule, a hash-chained audit trail, talent appeals, and DSAR handling. The evidence exists before anyone asks for it.
Zero ghosting and talent report cards
Every talent who does not get the job receives a report card explaining where they stood and what to work on. It is automatic, and it is not optional.
Composite scoring and blind ranking
A job score you set the weights for, defaulting to 35 percent vetted score, 40 percent the job’s own interview and 25 percent match. It orders a list. It never makes a decision.
Read the reasoning
The evidence and the argument behind what is on this page.
Time-to-hire is a throughput metric. Quality-of-hire is the one that matters
Speed metrics are easy to collect and easy to game. How to measure whether your hiring decisions were any good, and which metrics to pair with which.
ReadWhy candidates get ghosted, and how to actually stop
Ghosting is almost never malice. It is what happens when closing the loop is optional, unowned and manual. The four structural causes, and the fix for each.
ReadThe four-fifths rule: how to test your hiring for adverse impact
The four-fifths rule is a rule of thumb, not a legal safe harbour. How to calculate it, where it misleads, and how to find the stage causing the disparity.
ReadSee a verified pipeline for one of your roles
Post a role free and review anonymous, skill-ranked talent. No card, no sales call to get started.