Design metrics and geo A/B for new feature

You propose a new Marketplace feature, Verified Seller Badges, intended to increase buyer trust and monetization without harming user experience. 1) Mission and hypotheses: State your mission precisely and write primary and secondary hypotheses that are falsifiable. 2) Metrics: Define a North Star Metric (e.g., weekly GMV per active buyer) and 4–6 supporting metrics, including at least two counter/guardrail metrics (e.g., fraud reports per 1,000 transactions, session crash rate, ad revenue per session). Explain why each is diagnostic and how to compute it at user- and geo-level granularity. 3) Experiment design: Propose a geo-level clustered A/B test. Specify: cluster unit (city/metro), stratification variables (e.g., active buyers, baseline GMV, seasonality, device mix), matching strategy, number of clusters per arm, traffic ramp plan, duration, and how you will handle contamination, spillovers, and staggered rollouts. 4) Sample size and power: Show how you would estimate the minimum detectable effect for the NSM, including variance assumptions and any design effects due to clustering. 5) Instrumentation: List the exact events/attributes you need in logs to compute all metrics and diagnose mechanism (e.g., badge impressions, seller profile views, message initiations, purchase confirmations). 6) Decision framework: Suppose the test shows +2.0% (p<0.05) on NSM, −0.3% (ns) on sessions per user, and +0.8 bps in fraud reports (p=0.06). Explain the launch decision, including how you would incorporate engineering cost, staffing, and operational feasibility. Show a back-of-envelope estimate of potential incremental revenue assuming $0.50 revenue per incremental purchase and the observed lift. 7) External data: Name one third-party signal you might use to improve targeting, and discuss privacy/compliance considerations and how you would validate its incremental value without bias.