cohort-analysis
About
This skill enables cohort-based user retention analysis for measuring customer lifecycle patterns and engagement trends. It helps developers track retention, compare acquisition cohorts, and identify churn risks over time. Use it when analyzing product impact, estimating LTV, or understanding behavioral patterns across different user groups.
Quick Install
Claude Code
Recommendednpx skills add guia-matthieu/clawfu-skills -a claude-code/plugin add https://github.com/guia-matthieu/clawfu-skillsgit clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/cohort-analysisCopy and paste this command in Claude Code to install this skill
Documentation
Cohort Analysis
Analyze retention and behavior patterns by grouping users into cohorts - understand how different customer groups behave over time.
When to Use This Skill
- Retention tracking - Measure how users stick around over time
- Acquisition analysis - Compare cohorts from different channels
- Product changes - Measure impact on user behavior
- Churn prediction - Identify at-risk cohorts
- LTV estimation - Project customer lifetime value
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
Dependencies
pip install pandas plotly click
Commands
Retention Analysis
python scripts/main.py retention data.csv --date-col signup --event-col purchase
python scripts/main.py retention data.csv --date-col signup --periods week
Visualize Cohorts
python scripts/main.py visualize cohorts.csv --output retention_chart.html
Export Report
python scripts/main.py report data.csv --date-col signup --event-col active --output report.html
Examples
Example 1: Analyze User Retention
python scripts/main.py retention users.csv --date-col signup_date --event-col last_active
# Output:
# Cohort Retention Analysis
# ──────────────────────────────────
# Cohort Users M1 M2 M3 M4
# Jan 2024 1,234 65% 48% 42% 38%
# Feb 2024 1,456 62% 45% 41% --
# Mar 2024 1,321 68% 52% -- --
# Apr 2024 1,567 64% -- -- --
#
# Avg Retention: 65% → 48% → 42% → 38%
# Best Cohort: Mar 2024 (68% M1)
Example 2: Generate Visual Report
python scripts/main.py report transactions.csv \
--date-col signup \
--event-col purchase_date \
--output retention_report.html
# Generates interactive HTML with:
# - Retention heatmap
# - Cohort size chart
# - Trend analysis
Cohort Table Format
| Cohort | Size | Period 0 | Period 1 | Period 2 | Period 3 |
|---|---|---|---|---|---|
| 2024-01 | 1234 | 100% | 65% | 48% | 42% |
| 2024-02 | 1456 | 100% | 62% | 45% | - |
| 2024-03 | 1321 | 100% | 68% | - | - |
Skill Boundaries
What This Skill Does Well
- Structuring data analysis
- Identifying patterns and trends
- Creating visualization frameworks
- Calculating statistical measures
What This Skill Cannot Do
- Access your actual data
- Replace statistical expertise
- Make business decisions
- Guarantee prediction accuracy
Related Skills
- ab-test-stats - Test retention experiments
- funnel-analyzer - Analyze conversion funnels
Skill Metadata
- Mode: centaur
category: analytics
subcategory: retention
dependencies: [pandas, plotly]
difficulty: intermediate
time_saved: 4+ hours/week
GitHub Repository
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