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
Skill Metadata
category: analytics
subcategory: retention
dependencies: [pandas, plotly]
difficulty: intermediate
time_saved: 4+ hours/week
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