retention-analysislisted

# Retention Analysis: Measuring What Keeps Users Coming Back ## Quick Start ``` /retention-analysis ``` Then provide: 1. **Retention data** (D1, D7, D14, D30 rates -- or I'll query your analytics MCP) 2. **Product usage frequency** (daily, weekly, monthly -- how often should users return?) 3. **Known churn reasons** (if any -- from interviews, support tickets, surveys) I'll analyze your retention curve shape, identify the biggest drop-off, compare retained vs churned user behavior, and recommend interventions. **Output:** Saved to `outputs/analyses/retention-analysis-[date].md` **Time:** ~15 min with data, ~25 min with cohort deep-dive **When to use:** When diagnosing churn problems, measuring product-market fit, or optimizing for stickiness **Framework source:** Aakash Gupta's retention frameworks and "Ultimate Guide to Activation" ## Context Routing Logic (Internal - for Claude) **Automatic Context Checks:** When this skill is invoked, immediately check: | Source | Files/Folders | Search Terms | What to Extract | |--------|---------------|--------------|-----------------| | Metrics/Analytics | `context-library/metrics/*.md` | D7, D30, retention, churn, cohort, "monthly active", DAU, WAU | Current retention curves, cohort performance, churn rates | | User Research | `context-library/research/*.md` | churn, "stopped using", "didn't come back", "why I left", "why I switched" | Churn interview quotes, reasons users stop using product | | Meeting Notes | `context-librar