Benchmarking Compensation With Data
Good compensation benchmarking is a method, not a number: define the role by duties rather than title, use data no older than twelve months, and adjust for market and size. Copying a figure from a stale survey is how bands end up fiction.
Sources in order of trust
| Source | Strength | Weakness |
|---|---|---|
| Proprietary surveys | Methodology disclosed, cut by industry and size | Cost; participation lag |
| BLS occupational data | Free, census-scale | Coarse occupations, lags a year |
| Job-posting scrapes | Fresh, market-current | Asking prices, not paid rates |
| Self-report sites | Cheap and fast | No verification, selection bias |
Match duties, not titles
"Operations manager" spans roles paying half again more depending on headcount supervised and P&L owned. Benchmark by survey job code matched on responsibilities, then sanity-check against two adjacent codes. Title inflation makes most internal titles one grade above their actual duty set.
Age and geography adjustments
Survey data publishes with an effective date; wage movement since then belongs in your model, not ignored. Geographic differentials published as multipliers are starting points - remote hiring has blurred metro boundaries faster than surveys update. Pay transparency statutes accelerate this; see trends reshaping the workplace.
The output that matters
The deliverable is not a spreadsheet of medians - it is a band structure with defensible midpoints and documented sources per band. That artifact survives the questions a pay equity review will ask next; see analytics for pay equity.
Next
Frequently asked
How often should benchmarks refresh?
Annually at minimum for fast-moving functions like engineering and sales; every other year survives for stable administrative roles.
Are free salary sites usable?
For triangulation only - treat them as a signal to check against structured data, never as the primary source.
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