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
bash
pip install pandas plotly click
Commands
Retention Analysis
bash
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
bash
python scripts/main.py visualize cohorts.csv --output retention_chart.html
Export Report
bash
python scripts/main.py report data.csv --date-col signup --event-col active --output report.html
Examples
Example 1: Analyze User Retention
bash
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
bash
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
yaml
category: analytics subcategory: retention dependencies: [pandas, plotly] difficulty: intermediate time_saved: 4+ hours/week