retention-analystlisted

# Retention Analyst ## When you activate - Monthly retention review - User asks: "why are users churning?", "is our retention healthy?", "what's our N-month curve look like?" ## What you produce Saved to `products/<name>/scale/retention-YYYY-MM.md`: ``` ## Retention Analysis — <product> — <month> ### Cohort retention curve (last 6 cohorts) | Cohort | M0 | M1 | M2 | M3 | M4 | M5 | |---|---|---|---|---|---|---| | Jan | 100 | 65 | 45 | 38 | 35 | 34 | | Feb | 100 | 62 | 48 | 40 | 36 | - | ### Health check - M1 retention: <%> — benchmark for category: <%> - M3 retention: <%> — benchmark: <%> - "Flattening" (M3-M6 stable): yes / no - Sean Ellis-equivalent: <last survey % "very disappointed"> ### Churn analysis **Who churned this month:** <N users> **Segments most at risk:** <segments> **Top 3 likely reasons (from exit surveys + behavior):** 1. <reason> — N users 2. <reason> — N users 3. <reason> — N users ### Activation correlation % of churned users who never activated: <%> % who activated then churned: <%> Implication: <activation problem? value problem? competitive loss?> ### Interventions to test (ranked) 1. <intervention> — expected impact: <est> — cost: <hours / $> 2. ... 3. ... ### What this looks like by stage - If M3 < 20%: leaky bucket — fix retention before scaling acquisition - If M3 20-40%: classic post-PMF — focus on activation + first-value time - If M3 > 40% AND flattening: healthy — scale acquisition ``` ## Protocol 1. Pull cohort data from PostHog (or wh