cohort-retention-analysis
Cohort Retention Analysis
Understand retention by isolating who joined when and tracking what they did after.
How to use
/cohort-retention-analysisApply cohort analysis constraints to this conversation./cohort-retention-analysis <context>Analyze retention for the described product and data.
Constraints
Why Cohorts Matter
- Aggregate retention numbers lie. A growing product masks terrible retention with new users.
- MUST isolate groups by when they joined and track behavior over time
- NEVER report retention as a single aggregate number without cohort breakdown
Cohort Types
- Time-based: weekly signup cohorts (fast products), monthly (most SaaS), quarterly (long sales cycles)
- Behavioral: users who completed onboarding vs. didn't, used Feature X vs. didn't
- Segment: by plan tier, company size, acquisition channel, geography
- SHOULD use at least two cohort types for any retention analysis
Retention Metrics
- User retention: did they come back? (Day 1, 7, 14, 30, 60, 90)
- Revenue retention: did they keep paying? NRR includes expansion; GRR only contraction and churn.
- Activity retention: did they do the core action? (Define what "active" actually means)
- MUST pick the metric that matches your product type. "Active" for a chat tool is different than a tax tool.
Curve Shape Interpretation
- Steep early drop then flattens: normal. Focus on getting more people past the initial drop.
- Gradual continuous decline: dangerous. Product delivers diminishing value over time.
- Flattens then drops again: something triggers later churn. Investigate the inflection point.
- Newer cohorts retain better: product or onboarding is improving.
- Newer cohorts retain worse: product-market fit may be weakening or user quality declining.
Diagnosing Drop-Off Points
- Day 0-1 drop: onboarding too complex? Aha moment not reached?
- Day 1-7 drop: no reason to return? No habit loops?
- Day 7-30 drop: novelty wore off? Hitting plan limitations?
- Day 30+ drop: product not delivering ongoing value? Competitor pulling them away?
- MUST compare cohorts to find what drives differences, not just observe the drops
Anti-Patterns
- Reporting DAU/MAU without cohort context
- Averaging retention across segments that behave completely differently
- Measuring only one retention metric when multiple views tell different stories
- Ignoring that improving one stage can sometimes hurt a later stage
More from dragoon0x/product-skills
prd-writing
Write product requirement documents that engineers want to read and can actually build from. Covers structure, scope discipline, and the balance between clarity and over-specification. Use when writing PRDs, reviewing spec quality, or when engineering keeps asking clarifying questions.
1freemium-vs-paid-gate
Decide whether a product should offer a free tier, free trial, or go straight to paid. Structured decision framework based on economics, distribution model, and competitive landscape. Use when launching a new product or reconsidering your pricing model.
1error-recovery
When things break, guide people forward instead of leaving them stranded. Error message copy, retry patterns, graceful degradation, and recovery flows. Use when building error handling or failed state UIs.
1cta-patterns
Design calls-to-action that people actually click. Covers button copy, placement logic, urgency without manipulation, and progressive commitment. Use when reviewing pages for conversion potential or when CTA copy feels generic.
1onboarding-flow
Design first-run experiences that create the aha moment fast. Reduces time-to-value by sequencing actions, progressive disclosure, and contextual guidance. Use when building signup flows, product tours, or empty states.
1user-psychology
Apply motivation, friction, and trust patterns to product decisions. Maps cognitive biases and behavioral triggers to specific UI and copy choices. Use when reviewing flows for drop-off points or when something feels right but doesn't convert.
1