Candidate Pipeline Scoring uses AI to score applicants on likelihood of offer acceptance and long-term retention, enabling prioritised recruiter engagement, by analysing application attributes, sourcing channel, and historical hiring outcomes, across volume recruiting programmes.
Recruiters managing high-volume pipelines spend equal time on candidates with very different conversion probabilities, causing offer delays on high-intent candidates and excessive follow-up on low-intent applicants.
A predictive model scores each candidate in the active pipeline on offer-acceptance likelihood and 12-month retention probability, surfacing a ranked engagement list updated after each candidate interaction.
Improvement in offer acceptance rate and reduction in recruiter time spent per successful hire.
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