Recruitment Analytics

Recruitment analytics use recruiter-controlled Recruitment Status and recorded status history. They do not reinterpret AI Recommendation as a pipeline stage.

Current Funnel

The job funnel summarizes candidates in one job, while Dashboard overview cards provide selected workspace-level indicators. Job funnel metrics include:

• Total candidates.
• Active candidates.
• Candidates pending review.
• Hired candidates.
• Rejected candidates.
• Count and percentage for each Recruitment Status.

These metrics represent current candidate state.

Historical Analytics

When status history is available, Veritik can report:

• Tracked status changes.
• Changes during the last 7 days.
• Changes during the last 30 days.
• Hiring rate.
• Rejection rate.
• Candidates stagnant beyond the configured threshold.
• Average time spent in stages with sufficient history.
• Common stage transitions.

Screening Criteria Insights

The Pipeline for one job also aggregates criterion evidence from candidates screened with evidence-based assessment. It shows:

• Evidence coverage and the number of legacy candidates without criterion evidence.
• Average Skills, Experience, and Education scores, including legitimate 0% values.
• Met, Partially met, Not met, and Not found counts for every criterion.
• Exact met rate, calculated as Met ÷ all evidence records for that criterion.
• Lowest exact met-rate criteria first to surface potential screening bottlenecks.
• Up to three highest-scoring candidates marked Met for each criterion, linked to Candidate Profile.

Use the category filters to review Required Skills, Preferred Skills, Experience, or Education. These insights use evidence already stored in candidate assessments and do not make another AI request.

Reading the Results

Historical analytics begin from the time status-history recording was introduced. Earlier status decisions may not have an event record and therefore may not appear in transition-based metrics.

Small sample sizes can produce unstable percentages. Interpret analytics together with job age, candidate volume, team process, and operational context.

Evidence coverage also matters. A low met rate based on only one or two newly screened candidates should not be generalized to the full applicant pool. Legacy candidates without evidence are reported separately and are never counted as Not found.

Refresh Behavior

Analytics refresh after successful status changes. If data cannot be loaded, candidate updates can still be reviewed directly in the pipeline and activity timeline.

Responsible Use

• Use analytics to identify workflow patterns and follow-up needs.
• Do not infer candidate quality from time in stage alone.
• Investigate process context before acting on a metric.
• Keep AI evaluation and recruitment outcomes analytically distinct.

Continue to Plans and Quotas for workspace usage rules or Global Pipeline for cross-job operational review.