Source-of-hire benchmarks across LinkedIn, Naukri, and Indeed for engineering, non-tech, and frontline roles. Drawn from 8,400 hires placed through pPULSE ATS.
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We aggregate anonymised funnel metrics across 8,400 hires placed through pPULSE ATS in the trailing twelve months. The benchmark surfaces median cost-per-applicant, time-to-shortlist, and offer-accept rate, broken down by role family and source channel.
The data is anonymised at the point of aggregation; no individual customer or candidate is identifiable in the output.
Look at three numbers: cost-per-applicant (channel efficiency), time-to-shortlist (recruiter throughput), and offer-accept rate (offer quality). If you are below median on cost but above on time-to-shortlist, the recruiter team may be the constraint, not the channel.
Most teams over-index on cost-per-applicant and under-index on offer-accept rate. The latter is the longer-cycle signal that matters more for senior roles.
For engineering hiring, LinkedIn dominates on quality; pair with Naukri for inbound. For non-tech, Naukri leads on cost; Indeed is the useful third channel. For frontline, Naukri-only is often optimal; LinkedIn produces few applicants in this segment.
See the channel-by-channel breakdown post for the full analysis.
Anonymised funnel metrics from customers using pPULSE ATS, aggregated across 8,400 hires in the trailing twelve months. Individual customers cannot be identified in the output.
Quarterly. The current numbers are based on the trailing twelve months ending Q1 2026.
Yes. Engineering, non-tech, and frontline have meaningfully different funnel profiles, so the benchmark splits accordingly. Sub-roles within engineering (DevOps, frontend, ML) follow similar patterns.
Identify which metric is the gap. Cost-per-applicant typically responds to channel mix changes; time-to-shortlist to recruiter throughput and screening discipline; offer-accept rate to compensation and offer-letter quality. See our offer letter post for the offer-quality angle.
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