retention-analysis

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Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.

AI & Automation 915 stars 165 forks Updated 3 days ago MIT

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# Retention Analysis Skill Diagnose why users leave, identify what keeps them, and recommend specific, testable interventions — not vague "improve onboarding" suggestions. ## Retention Fundamentals **The retention curve has two components:** 1. **Steepness of initial drop** (D1–D7) — onboarding problem 2. **Long-term floor level** — product-market fit indicator A product with PMF has a retention curve that flattens. If it trends to zero, you have a PMF problem, not an onboarding problem. Name this distinction explicitly. --- ## Retention Metrics Definitions | Metric | Formula | What It Tells You | |---|---|---| | D1 Retention | Users who return on day 2 ÷ new users day 1 | Quality of first experience | | D7 Retention | Users active on day 8 ÷ users who joined 7 days ago | Early habit formation | | D30 Retention | Users active on day 31 ÷ users who joined 30 days ago | Product-market fit signal | | DAU/MAU Ratio | Daily active users ÷ monthly active users | Stickiness (>20% good, >50% excellent) | | Churn Rate | Users lost in period ÷ users at start of period | Monthly or annual | | Net Revenue Retention | MRR at end of period ÷ MRR at start (same cohort) | Revenue health including expansion | --- ## Retention Investigation Framework ### Step 1: Segment the problem Don't analyse "retention" — analyse retention for specific cohorts: - New vs returning users - Paid vs free - Acquisition channel (organic vs paid vs referral) - Onboarding path completed vs not - Feature ...

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Author
mohitagw15856
Repository
mohitagw15856/pm-claude-skills
Created
4 months ago
Last Updated
3 days ago
Language
Shell
License
MIT

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