## Scenario You are a Data Scientist supporting a consumer product (app or website). A PM asks you to “dive deep” on **user retention** and recommends tracking **7-day** and **28-day** retention. ## Task 1. **Define retention clearly**. Give at least **three** common retention definitions and explain how they differ: - **N-day (classic/cohort) retention** - **Rolling retention (a.k.a. unbounded)** - **Return rate / weekly active retention** (or another reasonable variant) 2. Explain how you would compute and interpret **7-day vs 28-day retention**: - What user cohorting would you use (e.g., signup/install week)? - What does each metric capture about user behavior? 3. Discuss **short-term vs long-term tradeoffs**: - Give examples of product changes that might increase **7-day** retention but harm **28-day** retention (and vice versa). - Propose a metric hierarchy: **primary**, **diagnostic**, and **guardrail** metrics. 4. Call out at least **five pitfalls/edge cases** when measuring retention and how you would address them (e.g., right-censoring, seasonality, re-installs, bots, changing definitions, missing events, timezone issues). 5. If the PM wants to run an A/B test to improve retention, outline how you would design and evaluate it (unit of randomization, experiment duration, how to handle delayed effects, and any variance reduction or sequential testing considerations).