The compensation data that does the analytical work for you

Peer Enhanced is the modeled layer of the Peer Data Ecosystem. It automates work compensation analysts used to do by hand — calculating geographic differentials, applying leveling logic, normalizing outliers, and delivering consistent coverage across the job list.

No. Peer Enhanced is not a gap-filler bolted onto thin data. It applies job-by-job location differentials, normalizes outliers, and uses stabilization methods so estimates stay reliable when sample sizes are small. It brings job-specific precision to scale.

Peer Enhanced uses a Bayesian machine learning approach trained on the Peer Global dataset. Unlike traditional survey aggregation, Bayesian modeling incorporates prior knowledge about pay relationships and updates estimates as new data enters — producing stable, reliable results even in cuts where raw sample sizes would otherwise be too small to report. The model learns how location, industry, company size, and job level interact to affect pay for each specific job, then produces estimates that reflect those dynamics rather than simple averages.

Coverage is the US and Canada, updated monthly, using the same location taxonomy as Peer Global. The underlying dataset holds 8.1 million incumbents across 3,600+ organizations and 5,000+ jobs — giving the model a broad, continuously refreshed foundation to learn from.

They are job-specific rather than job-agnostic. The geographic differential for a hotel general manager in New York City is materially different from the differential for an accountant in the same city. Legacy modeled approaches applied a single citywide adjustment across every job; Peer Enhanced learns the differential per job.

Peer Global is your always-on baseline. It sources directly from HRIS systems, refreshes daily, and gives you full control over your competitive scope — including contributor transparency and custom data cuts.

Peer Enhanced is the analytical lens on top of that foundation. It automates the manual work of a comp analyst — applying consistent differentials, leveling adjustments, and bias corrections across your job list so you're not rebuilding that logic from scratch for every role.

• Complementary lenses on the same underlying data: For more mature comp teams, Peer Global is where every pricing decision starts. For teams earlier in their maturity journey, Peer Enhanced can be the right entry point — delivering consistent coverage across every role quickly, without requiring scope-building or custom configuration. Either way, the two work best as complementary lenses on the same underlying data, not as competing sources.

• Should I use Peer Global and Peer Enhanced for the same job? Choose one for each pricing decision — Peer Global when you want to interrogate a specific scope or competitor set, Peer Enhanced when you want consistent analytical coverage applied across a broad job list.

• If you don't have Peer Global: Peer Enhanced can be used as a standalone source within your market pricing process, functioning like any other survey input. It's a complete and flexible solution for building competitive, market-aligned pay — and a natural on-ramp to the full Peer ecosystem when you're ready to expand.

Base salary at the 25th, 50th, and 75th percentiles plus average, total cash compensation, and Pay Impact reports that show how each compensable factor —location, industry, company size — affects the final pay range for that specific job.

Peer Enhanced applies a weighting schema so no single employer dominates. An incumbent's influence on the model scales inversely with how many employees share that job title at the same company — so a company with 500 software engineers does not outweigh one with five.

Each organization's contribution is normalized, keeping estimates representative of the broader market rather than of any single large participant.