Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install ingramradical235-anty-framework-skills-retention-cohortsgit clone https://github.com/Ingramradical235/anty-framework.gitcp anty-framework/SKILL.MD ~/.claude/skills/ingramradical235-anty-framework-skills-retention-cohorts/SKILL.md---
name: retention-cohorts
description: Cohort retention analysis — triangle chart construction, curve shape analysis for PMF detection (flattening/declining/rising), layer cake chart, PMF-driven priority shifting, 5 retention anti-patterns, 4 improvement levers, three definitions setup. Use when measuring PMF, analyzing user retention, or deciding whether to scale or improve product.
type: skill
---
# Retention & Cohort Analysis
## When to Apply
- Measuring product-market fit
- Deciding whether to invest in growth vs product improvement
- When the founder asks "do we have PMF?"
- When retention data is available from integrations
- Quarterly PMF reassessment
## Core Framework
### Three Definitions (Set During Onboarding)
Before any retention measurement, guide the founder to define:
1. **Cohort grouping** — How new users are grouped
- Weekly: daily-use products
- Monthly: utility products
- Quarterly: infrequent-use products (travel, tax)
2. **Active action** — What counts as "active." Must reflect genuine value delivery.
- Ask: "Imagine watching a customer use your product. What moment tells you they're genuinely getting value?"
- B2B SaaS: "completed a core workflow"
- Consumer: "engaged with 3+ pieces of content"
- Marketplace: "completed a transaction"
3. **Time granularity** — How often users should ideally use the product. Cross-check against chosen action for consistency.
### Triangle Chart (Cohort Retention Table)
```
Week 0 Week 1 Week 2 Week 3 Week 4 Week 5
Jan 100% 62% 45% 38% 35% 34% <- flattening
Feb 100% 58% 41% 33% 31% ...
Mar 100% 65% 50% 42% ...
Apr 100% 71% 55% ... <- improving
```
### Curve Shape Analysis (Most Important Insight)
Analyze SHAPE, not absolute numbers:
| Curve Shape | PMF Signal | Agent Action |
|---|---|---|
| **Flattening** (stabilizes at any level) | PMF detected | Shift to growth Drivers. "Retention flattening at ~34%. Even Google Photos flattened at 20-40%." |
| **Declining to zero** (no stabilization) | No PMF | Shift to product/activation Drivers. Trigger WHY analysis. "Users not sticking. Understand why before investing in growth." |
| **Rising** (curves go up over time) | Strong PMF + network effects | Propose aggressive scaling. "Retention increasing — extremely strong signal." |
| **Newer cohorts better** | Product improving | "Product improvements working — newer cohorts retain better." |
| **Newer cohorts worse** | Product or acquisition degrading | "Warning: newer cohorts retain worse. Investigate product quality or acquisition quality." |
### Layer Cake Chart
Cohorts stacked over absolute calendar time. Shows whether active user base grows from retained cohorts (healthy) or just cycles through new users (treadmill).
### PMF-Driven Priority Shifting
| Situation | Agent Focus |
|---|---|
| No PMF + Vitamin product | Repositioning and pain discovery before growth |
| No PMF + Painkiller product | Product improvement, onboarding, user research |
| PMF signal but no "Locally Famous" group | Deepen penetration in one specific group |
| PMF + Locally Famous confirmed | Shift to scaling: growth Drivers get higher impact_weight |
This directly feeds into the KPI tree's dynamic impact_weight system.
### 5 Retention Anti-Patterns
| Anti-Pattern | Detection | Response |
|---|---|---|
| Time period too large | Quarterly cohorts for daily-use product | "Your product is daily-use. Weekly cohorts give more honest signal." |
| Action too easy | "Opened app" or "visited site" | "Doesn't reflect real value. Consider [specific recommendation]." |
| Payment-only tracking | Only tracking subscription status | "Users stop using before they stop paying. Add usage-based action metric." |
| Cherry-picking | Citing single favorable retention number | "Which period? Show the complete triangle chart." |
| Curve misinterpretation | High initial retention that declines | "50% that declines to zero is worse than 20% that flattens. Shape > number." |
### 4 Improvement Levers
When retention curves don't flatten:
1. **Product improvement** — New use cases, speed, simpler flows
2. **Better user acquisition** — Targeting users who are a better fit. "Paid cohorts retain worse than organic. Consider shifting budget."
3. **Onboarding/activation** — Help users reach "aha moment" faster. Often cheapest lever. "What was the user doing yesterday? What should they do differently today?"
4. **Network effects** — If applicable, user-to-user value. "Each new user could make your product better for existing users."
## Decision Rules
1. **Three definitions before measurement** — no retention analysis without explicit cohort, action, and time definitions
2. **Shape over numbers** — 20% that flattens beats 50% that declines
3. **PMF drives priorities** — no scaling without retention flattening
4. **Newer vs older cohorts** — compare across time to detect product trajectory
5. **Layer cake for growth truth** — reveals treadmill vs genuine growth
6. **Cheapest lever first** — onboarding improvement often has highest ROI
## Anti-Patterns to Detect
| Anti-Pattern | Signal | Response |
|---|---|---|
| Scaling before PMF | Growing acquisition with declining retention | "Retention curve hasn't flattened. Fix retention before scaling." |
| Wrong granularity | Mismatched time period and product usage | "Adjust cohort grouping to match actual product usage frequency." |
| Vanity retention | Tracking logins instead of value delivery | "Redefine 'active' to reflect genuine value delivery." |
| Single-number fixation | "Our retention is 40%" | "40% when? Show the full curve over time." |