What This Template Is For
Compensation benchmarking products help organizations set fair, competitive pay by comparing internal salaries against external market data. For HR tech PMs, the challenge is building a product that handles messy real-world data (inconsistent job titles, regional variation, equity vs. cash tradeoffs) while delivering actionable recommendations to compensation teams. A benchmarking tool that shows data without context creates more confusion than clarity.
This template provides a specification framework for building compensation benchmarking features, whether as a standalone product or as part of an HRIS. It covers data source evaluation, pay band design, equity modeling, and the analytics layer that ties everything together. If you are also building performance review features, pair this with the Performance Review System Template to ensure the rating-to-compensation pipeline works end-to-end. Explore the PM Salary Guide for a working example of how benchmarking data can be structured and presented.
How to Use This Template
- Identify your data sources. Evaluate at least 3 market data providers for coverage, recency, and methodology.
- Define your job architecture. Map internal titles to a leveling framework before benchmarking.
- Design pay bands for each level and function using the framework below.
- Specify the equity component if your company uses stock options, RSUs, or profit sharing.
- Build the analytics layer that surfaces actionable insights (compa-ratios, pay equity gaps, budget impact).
- Review with your compensation team, finance, and legal before deploying.
- Use the RICE Calculator to prioritize which benchmarking features to build first.
The Template
Data Source Evaluation
- ☐ Identify 3-5 market data providers to evaluate (e.g., Radford, Mercer, Pave, Levels.fyi, Glassdoor)
- ☐ Assess each source on: sample size, data recency, geographic coverage, role granularity, methodology transparency
- ☐ Define data refresh cadence (annual, semi-annual, quarterly, real-time)
- ☐ Specify data ingestion format (API, CSV upload, manual entry)
- ☐ Document data quality checks (outlier detection, sample size thresholds, staleness alerts)
| Data Source | Sample Size | Recency | Geography | Granularity | Cost |
|---|---|---|---|---|---|
| [Source 1] | [n] | [Quarterly/Annual] | [US/Global] | [Title/Level] | [$/yr] |
| [Source 2] | [n] | [Quarterly/Annual] | [US/Global] | [Title/Level] | [$/yr] |
| [Source 3] | [n] | [Quarterly/Annual] | [US/Global] | [Title/Level] | [$/yr] |
Job Architecture and Leveling
- ☐ Define leveling framework (e.g., IC1-IC6, M1-M4, E1-E3)
- ☐ Map all internal titles to levels
- ☐ Map internal levels to market data job codes
- ☐ Document mapping rationale for ambiguous roles
- ☐ Create a job family structure (Engineering, Product, Sales, etc.)
- ☐ Define career tracks within each family (IC vs. management)
Pay Band Design
| Parameter | Value |
|---|---|
| Band width | [e.g., +/- 20% from midpoint, or Min/Mid/Max with specific spread] |
| Percentile target | [e.g., 50th percentile for base, 75th for total comp] |
| Geographic adjustment | [Flat / Tiered by metro / Continuous by cost-of-living index] |
| Band overlap | [Percentage overlap between adjacent levels] |
| Review frequency | [Annual / Semi-annual market data update] |
- ☐ Define base salary bands for each level and job family
- ☐ Define total compensation bands (base + bonus + equity)
- ☐ Set geographic adjustment methodology and tier definitions
- ☐ Document band progression rules (promotion increases, within-band movement)
- ☐ Create exception approval workflow for out-of-band offers
Equity Framework
- ☐ Define equity vehicle (stock options, RSUs, phantom equity, profit sharing)
- ☐ Set equity bands by level (grant size at hire, annual refresh, promotion grants)
- ☐ Define vesting schedule (4-year with 1-year cliff is standard)
- ☐ Specify equity valuation method for benchmarking (409A, last funding round, public price)
- ☐ Document equity-to-cash tradeoff guidance for offer negotiations
- ☐ Create equity refresh policy (annual grants based on performance rating)
Analytics and Reporting
- ☐ Compa-ratio dashboard (individual salary / band midpoint, by department and level)
- ☐ Pay equity analysis (salary distribution by gender, ethnicity, tenure, controlling for level and performance)
- ☐ Budget impact modeling (cost of bringing all employees to band midpoint)
- ☐ Market movement alerts (notify when market data shifts band midpoints by >5%)
- ☐ Offer competitiveness scoring (proposed offer vs. market data for the role/location)
- ☐ Range penetration visualization (where each employee sits within their band)
Filled Example: Annual Compensation Review for a 300-Person SaaS Company
Context. Series C SaaS company, 300 employees across US (5 metros) and UK. Preparing for annual compensation review cycle. Targeting 60th percentile for base salary, 75th percentile for total compensation.
Data Sources Selected
| Source | Why Selected |
|---|---|
| Pave | Real-time data, strong SaaS coverage, 300K+ data points |
| Radford | Gold standard for tech leveling, used by board for comp committee reports |
| Levels.fyi (verified) | Candidate-facing data that informs offer negotiations |
Pay Band Example: Product Management
| Level | Title | Base Range | Total Comp Range | Equity (RSUs) |
|---|---|---|---|---|
| IC3 | Product Manager | $130K-$165K | $155K-$210K | $25K-$45K/yr |
| IC4 | Senior PM | $155K-$195K | $190K-$260K | $40K-$70K/yr |
| IC5 | Staff PM | $185K-$230K | $235K-$320K | $60K-$100K/yr |
| M1 | PM Manager | $170K-$215K | $210K-$290K | $50K-$85K/yr |
| M2 | Director of Product | $200K-$250K | $260K-$370K | $80K-$130K/yr |
Key Analytics Outputs
| Metric | Current State | Action |
|---|---|---|
| Average compa-ratio (company-wide) | 0.94 | Below midpoint. Budget $420K for market adjustments. |
| Gender pay gap (controlling for level) | 3.2% gap | Flagged. Target 0-1% gap by end of cycle. |
| Employees below band minimum | 14 (4.7%) | Priority corrections needed before cycle ends. |
| Employees above band maximum | 6 (2%) | Review for promotion eligibility or role reclassification. |
Key Takeaways
- Evaluate at least 3 market data sources. No single source covers every role and geography
- Map internal titles to a leveling framework before benchmarking against market data
- Define band widths, percentile targets, and geographic adjustments as explicit policy decisions
- Build compa-ratio dashboards and pay equity analysis into the product, not as ad hoc reports
- Update benchmarks at least annually, with mid-year refreshes for high-demand roles
About This Template
Created by: Tim Adair
Last Updated: 3/4/2026
Version: 1.0.0
License: Free for personal and commercial use