retention-analysis
# retention-analysis This Claude Code skill structures retention analysis and churn investigation for product teams by diagnosing why users leave and identifying what drives long-term engagement. Use it when analyzing user retention curves, investigating churn rates, measuring DAU/MAU ratios, or building retention improvement plans. It produces a retention snapshot with root cause hypotheses, aha-moment correlations identifying early predictive behaviors, and prioritized interventions based on segmented cohort analysis and inflection point identification rather than generic recommendations.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/retention-analysis && cp -r /tmp/retention-analysis/plugins/pm-analytics/skills/retention-analysis ~/.claude/skills/retention-analysisSKILL.md
# 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 usage (power users vs lurkers) ### Step 2: Find the inflection points Where does the drop happen? D1? D7? Month 3? - D1 drop → First session experience - D7 drop → Habit loop not formed - D30 drop → Value not delivered at depth - Month 3+ drop → Boredom, competition, or lifecycle event ### Step 3: Identify the "aha moment" correlation Which early behaviour predicts long-term retention? - Run correlation: users who did [X] in first 7 days vs 30-day retention - Common patterns: connected an integration, invited a teammate, completed a core action N times ### Step 4: Qualify the churn Interview churned users — never skip this. Survey data alone is insufficient. - "What was the trigger that led you to cancel/stop?" - "What were you trying to accomplish that you couldn't?" - "What would need to change for you to come back?" --- ## Output Format ### Retention Analysis — [Product/Segment] — [Date] **Question:** [Specific retention question being answered] **Period Analysed:** [Date range] **Segment:** [Which users] --- **Current Retention Snapshot:** | Metric | Current | Industry Benchmark | Status | |---|---|---|---| | D1 Retention | [X%] | 25–40% | 🔴/🟡/🟢 | | D7 Retention | [X%] | 10–25% | 🔴/🟡/🟢 | | D30 Retention | [X%] | 5–15% | 🔴/🟡/🟢 | | DAU/MAU | [X%] | 10–20% typical | 🔴/🟡/🟢 | **Retention Curve Shape:** [Flattening / Still declining / Trending to zero] **PMF Signal:** [Strong / Weak / Absent — based on curve shape] --- **Root Cause Hypotheses:** | Hypothesis | Evidence | Confidence | Test | |---|---|---|---| | [Cause] | [Data point] | H/M/L | [How to validate] | **"Aha Moment" Correlation:** Users who [specific action] in first [N] days retain at [X%] vs [Y%] for those who don't. --- **Recommended Interventions:** | Intervention | Target Drop | Expected Lift | Effort | Priority | |---|---|---|---|---| | [Specific change] | D1 / D7 / D30 | [X%] | S/M/L | 1/2/3 | **Monitoring Plan:** - Metric to track: [X] - Review cadence: [Weekly / Monthly] - Alert threshold: [If X drops below Y, investigate immediately] --- ## Required Inputs Ask the user for these if not provided: - **Product and business model** (SaaS / consumer app / marketplace / other) - **Current retention metrics** (D1, D7, D30 if available) - **Segment to analyse** (all users / paid / free / a specific cohort) - **Key question to answer** (why is retention dropping? what drives retention?) - **Available data** (analytics events, churn surveys, interview notes) ## Quality Checks - [ ] Retention curve shape is diagnosed (flattening vs trending to zero = PMF vs onboarding) - [ ] Cohorts are segmented before analysis (not all users lumped together) - [ ] "Aha moment" correlation is identified or flagged as unknown - [ ] Interventions are specific (not "improve onboarding") - [ ] Churned user interviews are recommended (not just data analysis) - [ ] Monitoring plan includes an alert threshold ## Anti-Patterns - [ ] Do not recommend "improve onboarding" without specifying what specific step to change and why - [ ] Do not analyse retention without segmenting by cohort — aggregate retention curves hide cohort-specific patterns - [ ] Do not treat DAU/MAU below 5% as a retention problem — at that level, it is a product-market fit problem - [ ] Do not skip qualitative research — churned user interviews reveal reasons that quantitative data cannot - [ ] Do not set a monitoring alert without specifying the threshold that triggers it ## Guidelines - Never recommend "improve onboarding" without specifying *what* to change and *why* - Benchmark against industry — consumer apps, SaaS, and marketplaces have very different retention norms - If DAU/MAU is below 5%, that's a PMF conversation, not a retention tactics conversation - Always recommend talking to churned users — no amount of data replaces understanding the *reason*
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