Compensation Benchmarker
Compensation analysis, band placement, equity modeling, and market benchmarking
Your best engineer just got a competing offer and you have no idea if your compensation bands are competitive. You Google salary ranges, get wildly different numbers, and make a counteroffer based on vibes instead of data.
Who it's for: HR leaders building compensation frameworks, startup founders setting salary bands for the first time, people ops teams managing equity grants, hiring managers negotiating offers, talent teams benchmarking against market rates
Example
"Benchmark compensation for our 15-person engineering team" → Percentile-based benchmarks (25th-90th) for each role, band placement analysis, equity grant modeling, retention risk flags for underpaid employees, and market adjustment recommendations
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Compensation Benchmarker
Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.
## What You Can Provide
**Option A: Single role analysis**
"What should we pay a Senior Software Engineer in SF?"
**Option B: Upload comp data**
Upload a CSV or paste your comp bands. The assistant will analyze placement, identify outliers, and compare to market.
**Option C: Equity modeling**
"Model a refresh grant of 10K shares over 4 years at a $50 stock price."
## Compensation Framework
### Components of Total Compensation
- **Base salary**: Cash compensation
- **Equity**: RSUs, stock options, or other equity
- **Bonus**: Annual target bonus, signing bonus
- **Benefits**: Health, retirement, perks (harder to quantify)
### Key Variables
- **Role**: Function and specialization
- **Level**: IC levels, management levels
- **Location**: Geographic pay adjustments
- **Company stage**: Startup vs. growth vs. public
- **Industry**: Tech vs. finance vs. healthcare
### Data Sources
- **With compensation data tools**: Pull verified benchmarks
- **Without**: Use web research, public salary data, and user-provided context
- Always note data freshness and source limitations
## Output Format
Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.
```markdown
## Compensation Analysis: [Role/Scope]
### Market Benchmarks
| Percentile | Base | Equity | Total Comp |
|------------|------|--------|------------|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |
**Sources:** [Web research, compensation data tools, or user-provided data]
### Band Analysis (if data provided)
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|----------|-------------|----------|----------|----------|----------|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |
### Recommendations
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]
```
## If Connectors Available
If **compensation data** tools are connected:
- Pull verified market benchmarks by role, level, and location
- Compare your bands against real-time market data
If **HRIS** is connected:
- Pull current employee comp data for band analysis
- Identify outliers and retention risks automatically
## Tips
1. **Location matters** — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
2. **Total comp, not just base** — Include equity, bonus, and benefits for a complete picture.
3. **Keep data confidential** — Comp data is sensitive. Results stay in your conversation.
What This Does
This playbook turns Claude into a compensation analysis assistant that benchmarks roles against market data, analyzes band placement across your team, models equity grants, and identifies outliers or retention risks. It provides percentile-based benchmarks (25th through 90th) for base salary, equity, and total compensation, with adjustments for location, company stage, and industry.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Set Up Your Project
Create a project folder and place the template inside:
mkdir -p ~/Documents/CompAnalysis
mv ~/Downloads/compensation-benchmarker.md ~/Documents/CompAnalysis/CLAUDE.md
Step 3: Start Working
cd ~/Documents/CompAnalysis
claude
Say: "What should we pay a Senior Software Engineer in San Francisco?"
Three Ways to Use It
- Single role analysis -- Ask about market rates for a specific role, level, and location
- Upload comp data -- Provide a CSV or paste your comp bands for band placement analysis and outlier detection
- Equity modeling -- Model refresh grants, new hire grants, or option valuations
Compensation Framework
The assistant considers all components of total compensation:
- Base salary -- Cash compensation
- Equity -- RSUs, stock options, or other equity instruments
- Bonus -- Annual target bonus, signing bonus
- Benefits -- Health, retirement, perks
Key variables that affect benchmarks: role, level, location, company stage (startup vs. public), and industry.
Tips
- Always specify location -- SF vs. Austin vs. London produces very different benchmarks
- Think total comp -- Base salary alone does not tell the full story; include equity and bonus
- Keep data confidential -- Compensation data is sensitive; results stay in your conversation
- Note data freshness -- The assistant will always call out the source and recency of benchmark data
Example Prompts
"What should we pay a Senior Software Engineer in San Francisco?"
"Is this offer of $180K base + 5000 RSUs competitive for a Staff PM in NYC?"
"Model a refresh grant of 10K shares over 4 years at a $50 stock price"
"Here's our comp data CSV — identify outliers and retention risks"
"Compare our engineering bands to market for Series B companies"