##### Scenario Product analytics case: a new feature is launched or a key metric suddenly changes on a consumer app. ##### Question Walk me through how you would understand what changed, why it changed, and why the business should care. How would you size the magnitude of the impact or opportunity? State assumptions and show calculations. Which north-star, secondary, and guard-rail metrics would you track? At what granularity and why? Formulate a hypothesis along the user journey (AARRR). What data and analyses would you use to validate it? Design an A/B test: specify randomization unit, key metrics, network-effect concerns, sanity checks, sample-size calculation, and launch-decision criteria. If an A/B test is infeasible, outline a suitable quasi-experimental approach. ##### Hints Think top-down: frame problem → pick metrics → state hypothesis → design experiment/analysis → weigh trade-offs.