A Rebuilt Match Score in Pool

Match score in Pool is rebuilt around competencies, with the evidence behind each one visible on the talent profile.

Match score ranked how well someone already in your talent database fit a job, but it arrived as a single number. Recruiters could not see what a score was based on, and a thin record could still produce a confident looking low score.

What's new

• Competencies from the record — the engine builds a competency list for each talent from what is on their record, including application answers, AI recruiter conversations, work history and uploaded documents. Each competency carries a confidence level, the O*NET occupations it anchors to, and the quoted evidence it came from.

• Competencies for jobs and openings — the same extraction runs on job descriptions, so you can see what the engine understands a role to require. A fuller job description produces sharper matches.

• Four scoring categories — Competencies, Proximity, Work availability and Job Interest, which reflects whether the talent has expressed interest in this kind of job before.

• A breakdown by category — opening matches show how each category contributed to a score, rather than a single blended number.

• AI-generated talent descriptions — every talent profile opens with a written summary of who the person is, generated from their prospect data, Hire application history, workforce record and parsed resume.

• Weights and a threshold you control — set how much each category counts in Match Score settings, and set a minimum match score so lower matches aren't shown.

• Not enough signal — where a record carries too little to assess, the profile says so instead of showing a score.

Why it matters

A recruiter can explain a shortlist to a hiring manager using the evidence the engine found, and can tell a score worth acting on from a record that simply lacks the data. The engine was built to read what frontline applicants actually leave behind, not only résumés.

What to expect

Scores come from a different engine, so a person may score higher or lower than before against the same job. Audiences built on a match score threshold are recalculated, so some may get smaller: talent with no extracted competencies drops out of a competency scored audience rather than scoring low in it. No talent records are deleted, and your weight settings are kept. Coverage grows as new applicants arrive and records are updated, and records with little data, such as imported prospect lists, will see less from the competency category. The talent description is a reading aid: a sparse record produces a sparse summary, and it does not affect match score or audience membership.

Availability

Available to all Pool customers.