Retention Cohorts

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)

1. 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"

1. 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%    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." |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [masterleopold](https://github.com/masterleopold)
- **Source:** [masterleopold/anty-framework](https://github.com/masterleopold/anty-framework)
- **License:** MIT
- **Homepage:** https://4n7y.com

Install and usage instructions live in the source repository linked above.