cohort-analyzer
Analyzes revenue cohorts, retention curves, LTV/CAC trends over time
Best use case
cohort-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyzes revenue cohorts, retention curves, LTV/CAC trends over time
Teams using cohort-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cohort-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cohort-analyzer Compares
| Feature / Agent | cohort-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyzes revenue cohorts, retention curves, LTV/CAC trends over time
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Cohort Analyzer ## Overview The Cohort Analyzer skill provides systematic analysis of customer and revenue cohorts to understand retention patterns, lifetime value trends, and business health over time. It enables deep understanding of unit economics evolution and customer quality. ## Capabilities ### Revenue Cohort Analysis - Track revenue by acquisition cohort - Analyze net revenue retention (NRR) by cohort - Measure expansion, contraction, and churn - Identify cohort quality trends over time ### Retention Curve Analysis - Build and visualize retention curves - Compare retention across cohorts - Calculate retention benchmarks by segment - Identify retention inflection points ### LTV/CAC Analysis - Calculate LTV by cohort and segment - Track CAC trends over time - Analyze LTV/CAC ratio evolution - Model payback period by cohort ### Segment Analysis - Segment cohorts by customer type - Analyze channel-specific cohort quality - Compare enterprise vs. SMB retention - Identify highest-value customer segments ## Usage ### Analyze Revenue Cohorts ``` Input: Revenue data by customer and month Process: Build cohort matrix, calculate retention Output: Cohort analysis, NRR by cohort, visualizations ``` ### Build Retention Curves ``` Input: Customer data with start dates and activity Process: Calculate retention by period since acquisition Output: Retention curves, benchmark comparisons ``` ### Calculate Unit Economics ``` Input: Revenue cohorts, CAC data, time horizon Process: Calculate LTV, LTV/CAC, payback Output: Unit economics summary, trend analysis ``` ### Identify Cohort Trends ``` Input: Multi-period cohort data Process: Analyze quality trends, flag concerns Output: Trend analysis, quality assessment ``` ## Key Metrics | Metric | Calculation | Target Range | |--------|-------------|--------------| | NRR (Net Revenue Retention) | (Start + Expansion - Churn) / Start | 100-130%+ | | GRR (Gross Revenue Retention) | (Start - Churn) / Start | 85-95%+ | | LTV/CAC | Lifetime Value / Customer Acquisition Cost | 3x+ | | Payback Period | Months to recover CAC | 12-18 months | ## Integration Points - **Financial Due Diligence**: Support revenue quality analysis - **Financial Model Validator**: Validate retention assumptions - **Quarterly Portfolio Reporting**: Track portfolio company cohorts - **Customer Reference Tracker**: Connect qualitative feedback ## Visualization Outputs - Cohort retention heatmaps - Retention curve comparisons - LTV/CAC trend charts - Cohort revenue waterfalls - Segment comparison charts ## Best Practices 1. Use monthly cohorts for SaaS, adjust for business model 2. Separate new logo vs. expansion revenue 3. Analyze both count and revenue retention 4. Look for cohort quality degradation as signal 5. Segment analysis often reveals hidden patterns
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