People Analytics Toolbox · Niche Benchmark Surveys

A pay benchmark for the roles the big surveys miss.

Robotics. Photonics. CRO/CDMO. Space. Semiconductors. Biomanufacturing. The broad survey vendors cover these niches poorly — or not at all. We convene a of peers, auto-match every roster to a canonical benchmark, and give everyone the anonymized cuts. Cheaply.

Why this matters

Your niche isn’t in Mercer or Radford — so you’re guessing.

When you price a novel role at a niche company, you have to judge in the moment: is this role unique because these people did something idiosyncratic, or is it a real market pattern with a benchmark waiting to exist? The broad surveys flatten specialized roles into generic ladders, so they misprice them in both directions. The reason a focused survey never got built is labor: job-matching, data-cleaning, and cut-production made niche surveys uneconomic. That cost is what we collapsed.

How the consortium works

Low-touch by design. Upload a roster — get a benchmark.

1

Upload a roster

Drop in your job titles and pay — messy HRIS export is fine. Our maps every role to a canonical benchmark automatically. No manual job-matching homework.

2

We pool the cohort

Your auto-matched roster joins the other participants’. Every cut is and anonymized, and released only when at least two companies contribute — so no single company’s data is ever the benchmark.

3

Everyone gets the cuts

Each participant receives the for their niche — pay percentiles by role and level, with honest provenance. Sparse roll up to the broader function, so even a brand-new role gets a defensible number.

Network effect: every participant improves the benchmark for all, and the data also expands our coverage of the niche — so the survey gets better and cheaper to run each cycle. HR-metric benchmarks (voluntary turnover, time-to-fill, span of control) ride the same infrastructure at no extra collection cost.

Straight about the data

Anonymized, min-N protected, honest about provenance.

You see the cuts, never another participant’s data. A cut is published only when it clears the and has at least two contributors. Where a number is modeled rather than observed, we say so. Your own mapped data stays yours.

Build the benchmark your niche deserves.

Founding participants set the cuts and get them first. People teams in underserved verticals: a 20-minute call is all it takes to see if a consortium fits.