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Get Started ≫Salary benchmarking is comparing an organisation's pay against the market for comparable roles, using survey data, published ranges and offer intelligence, to set bands and price offers that attract without overspending.
Where benchmark data comes from
Four tiers, in descending reliability: participating compensation surveys (matched role definitions, employer-reported, the professional standard), published government and industry data, the growing pool of posted ranges that transparency laws have created, and self-reported salary sites (directional at best, skewed by who reports). Good benchmarking triangulates at least two tiers and records which market it is pricing against: industry, company size and location change the answer materially.
Matching roles, not titles
The craft is in the match: a "product manager" spans a junior coordinator to a mini-GM depending on the company, and benchmarking titles instead of role content produces confident nonsense. Match on scope, level and accountabilities, benchmark the band midpoint rather than chasing every data point, and refresh annually, or faster in running-hot markets, because benchmark decay is how organisations end up shocked by their own offer-decline rate.
The signals your own funnel sends
Your recruiting data is a free benchmark: offer-decline reasons, the gap between advertised range and accepted offers, and counter-offer sizes all price the market in real time. A rising decline rate on pay is the market repricing your bands for you; benchmarking is just the cheaper way to hear the same message earlier.
Declined offers are market data you already paid for. Read them.
See how it worksCommon questions
How often should roles be re-benchmarked?
Annually as a floor, with hot families (engineering, sales) checked more often. The trigger-based version: re-benchmark any role where offers start declining on pay or exits cite it.
Are posted salary ranges reliable benchmark data?
Useful but partial: they show intent, not settlement, and wide compliance ranges dilute the signal. Treat them as one tier alongside survey data, not a replacement for it.
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