| name | cohort-analysis-specialist |
| description | Track customer behavior over time by grouping customers based on shared characteristics or experiences. Use when measuring retention rates, understanding how customers evolve over time, comparing new vs old user behavior, evaluating product changes by cohort, identifying at-risk cohorts early, or calculating LTV by cohort. Essential for understanding retention, lifecycle patterns, and how product changes affect different user vintages. |
Cohort Analysis Specialist
Track customer behavior over time by grouping customers into cohorts for temporal analysis. Essential for understanding retention, lifecycle patterns, and product evolution.
Core Framework: Understanding Cohorts
What is a Cohort?
A group of users who share a common characteristic or experience within a defined time period.
Most common: Acquisition cohort (when they signed up)
Why Cohorts Matter:
Overall metrics can hide important trends. Example:
- Overall retention: 60%
- But actually: 2023 cohorts 75%, 2024 cohorts 45%
- Without cohorts, you wouldn't know new users perform worse!
Cohort Types
Type 1: Acquisition Cohorts
Definition: Grouped by signup/first purchase date
Time granularity:
- Daily (high volume, short analysis)
- Weekly (most common, balances detail/manageability)
- Monthly (strategic analysis, lower volume)
- Quarterly (very long-term trends)
Best for: Retention analysis, lifecycle understanding, LTV calculation
Type 2: Behavioral Cohorts
Definition: Grouped by specific action
Examples: Activated cohort, Converters, Feature adopters, Engagement level
Best for: Feature impact analysis, engagement optimization
Type 3: Attribute Cohorts
Definition: Grouped by characteristic at acquisition
Examples: Acquisition channel, Plan tier, Geographic, Segment
Best for: Channel quality, segment performance, market analysis
Key Analysis: The Retention Table
Classic Cohort Retention Table
Structure:
M0 M1 M2 M3 M4 M5 M6
Jan 24 100% 65% 52% 45% 41% 38% 36%
Feb 24 100% 68% 55% 48% 44% 40% 38%
Mar 24 100% 70% 58% 51% 46% 42% --
Apr 24 100% 72% 60% 53% 48% -- --
May 24 100% 74% 62% 55% -- -- --
Jun 24 100% 75% 63% -- -- -- --
Jul 24 100% 76% -- -- -- -- --
How to read:
- Rows: Each cohort (signup month)
- Columns: Time since signup
- Cells: % of original cohort still active
- M0: Always 100%
- Diagonal: Most recent data
Key insights:
- Vertical: Compare same period across cohorts (M1 improving: 65%→76%)
- Horizontal: See retention curve shape (steep drop early, then gradual)
- Trends: Draw lines through columns to see improvement/decline
Building a Retention Table (Descriptive)
Data to Collect:
- User signup dates grouped into cohorts (weekly or monthly)
- Activity data for each user in each subsequent period
- Definition of "active" (logged in, made purchase, used core feature)
- Time periods to track (typically 6-12 months)
How to Analyze:
Analysis 1: Create Retention Table
- Format: Table with cohorts as rows, time periods as columns
- Calculate: For each cohort and period, % of original cohort active
- Color coding: Use heat map (red <40%, orange 40-60%, yellow 60-75%, green >75%)
- Mark: Incomplete data (recent cohorts) clearly
Analysis 2: Cohort Retention Curves
- Chart type: Line chart
- X-axis: Time periods (M0, M1, M2, etc.)
- Y-axis: Retention rate (%)
- Lines: One line per cohort
- Look for: Separation between cohorts, curve shapes
Analysis 3: Period-Specific Trends
- Chart type: Line chart showing trends over time
- X-axis: Cohort (chronological)
- Y-axis: Retention rate
- Lines: Separate line for M1, M2, M3 retention
- Look for: Improvement or decline in specific periods
What to Look For:
Good patterns:
- Recent cohorts performing better than old (product improving)
- Curves plateau after M3-M6 (stable long-term retention)
- M1 retention >40%, M3 retention >30%
Bad patterns:
- Recent cohorts worse than old (product degrading)
- No plateau, continuous decline (no loyal base forming)
- Steep early drop that never recovers
Key Metrics from Cohort Analysis
D1/D7/D30 Retention
Definition: % active on specific day after signup
Milestones:
- D1 (Next Day): Immediate value delivery (target >40%)
- D7 (Week 1): Habit formation (target >30%)
- D30 (Month 1): Product stickiness (target >20%)
Analysis:
- Compare D1/D7/D30 across cohorts
- Track trends over time
- Identify which milestone is weakening
Retention Curve Shape Analysis
Question: How does retention decay over time?
Patterns:
- Smiling curve: Dip then recovery (common in B2B, slow onboarding)
- Flat after dip: Initial drop then stable (good pattern)
- Continuous decline: No stickiness, churn risk
- Plateau: Healthy engaged user base
Cohort Lifetime Value (LTV)
Calculate: Revenue per cohort over lifetime
Analysis:
- Compare LTV across cohorts (improving or declining?)
- Calculate time to 80% of LTV (payback period)
- Segment LTV (by channel, segment, etc.)
Use for: CAC targets, pricing strategy, prioritization
Advanced Techniques
Leading Indicator Analysis
Method: Identify early behaviors predicting long-term retention
Process:
- For mature cohorts, identify who retained long-term
- Look back at their Week 1 behavior
- Find patterns predicting retention
Example findings:
- Users with 3+ sessions in Week 1: 75% retained at M6
- Users who invited teammate: 82% retained at M6
- Users with <3 sessions: 15% retained at M6
Application: Track leading indicators in new cohorts for early warning
Segment Migration Analysis
Track: Movement between segments over time
Example:
- 80% of power users stay power users (good)
- 15% of power users become casual (warning)
- 20% of casual users become power (opportunity)
Insight: Understand what causes upgrades/downgrades
Cohort Comparison
Method: Compare two cohorts directly
Use for:
- A/B test impact evaluation
- Channel quality comparison
- Feature launch impact assessment
Analysis:
- Side-by-side retention curves
- Statistical significance testing
- Quantify difference magnitude
Common Pitfalls & Solutions
Pitfall 1: Incomplete Cohorts
- Problem: Recent cohorts have limited data
- Solution: Mark incomplete data clearly, focus on same maturity points
Pitfall 2: Cohort Size Variation
- Problem: Different sized cohorts make comparison hard
- Solution: Always use percentages, not absolutes
Pitfall 3: Seasonality Confusion
- Problem: Seasonal patterns mistaken for cohort differences
- Solution: Compare to same period prior year, adjust for seasonality
Pitfall 4: Definition Changes
- Problem: Changing "active" definition mid-analysis
- Solution: Lock definitions, clearly mark any changes
Pitfall 5: Cherry-Picking Metrics
- Problem: Only showing metrics that look good
- Solution: Report full retention curve, be transparent
Analysis Templates
Template: Cohort Retention Report
Structure:
- Executive Summary (trends in 2-3 sentences)
- Cohort Retention Table (with color coding)
- Key Metrics (D1/D7/D30 trends)
- Cohort Comparison (best vs worst, why)
- Analysis (what's working, what's not, early warnings)
- Recommendations (actions with owners)
Template: Cohort LTV Analysis
Structure:
- Summary table (cohort, size, age, actual LTV, projected LTV, maturity)
- LTV trends (by vintage)
- Time to 80% LTV
- LTV components (ARPU trend, lifetime trend)
- Recommendations
Validation Checklist
Data Quality:
Analysis Quality:
Presentation:
Actionability:
Related Agents & Skills
Prerequisites:
- 📊 KPI Definition Specialist - Define retention properly
- 🌳 KPI Tree Architect - Retention in broader context
Use together:
- 👥 Segmentation Expert - Segment within cohorts
- 🎯 Mix Effect Analyzer - Separate cohort from performance
- 📋 Analysis Planner - Plan cohort analysis properly
For insights:
- 🔍 Root Cause Investigator - Explain cohort differences
- 📝 Executive Summary Writer - Communicate findings
- 📊 Chart & Visualization Advisor - Create cohort visuals
Success Metrics
Mastered cohort analysis when:
- ✅ Can build retention table in <30 minutes
- ✅ Identify trends and outliers immediately
- ✅ Predict future performance from early cohorts
- ✅ Retention analysis drives product decisions
- ✅ Early warning system catches issues proactively
Key Principles
From "The Power of Analytics":
- "Cohorts help you understand if improvements are real or just mix effects"
- "Track cohorts to see product evolution impact over time"
- "Early cohort behavior predicts long-term retention"
- "Cohort analysis is essential for comparing apples to apples"
See references/retention_table_guide.md for detailed table creation
See references/cohort_metrics.md for complete metrics catalog
