##### Scenario A key revenue metric on Coinbase's dashboard suddenly declines—for example, weekly trading-fee revenue drops noticeably week-over-week. You are the data scientist asked to investigate. ##### Question Describe a structured, step-by-step approach to investigate and diagnose the root cause of the revenue drop. In your answer, address each of the following: 1. **Triage and validation** — How would you confirm the drop is real (not a measurement/data-quality artifact) and pinpoint exactly when it began? 2. **Revenue decomposition** — How would you decompose revenue into its underlying drivers (e.g., volume × take rate, or traffic × conversion × price) and quantify which driver moved the most? Show the math. 3. **Priority metrics** — Which metrics and KPIs would you inspect first, and why? 4. **Data cuts / segmentation** — Which segments and dimensions would you slice by to localize the drop, and how do they help you avoid Simpson's paradox? 5. **Funnel diagnostics** — How would you use the trade and funding funnels to find the failing step? 6. **Releases & experiments audit** — How would you check whether a recent deploy, config/fee change, or running A/B experiment caused the drop? 7. **External / market factors** — How would you separate macro/market-driven effects (e.g., crypto volatility) from internal product/ops causes? 8. **Hypothesis testing & attribution** — How would you turn observations into testable hypotheses and quantify each cause's contribution (with methods like ITS, diff-in-differences, synthetic control)? 9. **Action & monitoring** — How would you communicate findings, choose remediations, and add monitoring to prevent recurrence? ##### Hints Think in terms of decomposition (volume × take rate; or sessions × conversion × AOV), segmentation (asset, geo, platform, cohort, payment rail), funnels, recent changes/experiments, seasonality, and external market factors. Remember this is a crypto exchange: trading volume is highly sensitive to price volatility.