Recruitment Analytics: Metrics That Actually Predict Hiring Success

Recruitment analytics tools can surface dozens of metrics. Most hiring teams only need to act on a handful consistently.

Process-Health Metrics

• Time-to-fill, segmented by role level: averaged across all roles, this number hides which specific role types are actually struggling.
• Pipeline conversion rate by stage: where candidates drop out (application → screen → interview → offer) diagnoses whether the bottleneck is sourcing volume, screening quality, or interview-to-offer friction.
• Offer acceptance rate: a declining trend here, even with strong fill rates, usually signals a compensation or candidate-experience problem worth investigating before it gets worse.

Quality-of-Hire Metrics (Harder, More Valuable)

• 90-day and 1-year retention by source channel: reveals which sourcing channels actually produce durable hires, not just fast ones.
• New-hire performance rating correlated against assessment scores: the only real way to know whether your screening tools are predictive, versus just adding process overhead.

A Common Mistake

Optimizing purely for time-to-fill without tracking quality-of-hire alongside it. A process that fills roles fast but produces high early turnover is not actually working, even though its headline metric looks great.

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