I started with the funnel breakdown which felt right, app sessions down to completed orders, but I kept second-guessing whether to lead with supply or demand.
Start by validating the metric definition and data pipeline to rule out instrumentation issues, then segment the drop by dimensions like time, geography, user type, and order characteristics to isolate the cause. Finally, correlate with external events and internal changes, and quantify the impact to prioritize next steps.
Pro tip: Always check if the drop is real before diving deep—many 'drops' are due to logging errors, dashboard bugs, or seasonality. Also, consider the broader business context: a drop in completed orders might be intentional (e.g., reducing fraud) or offset by other metrics.
Confirm the drop is real by checking data pipelines, logging, and metric definitions. Ensure there are no instrumentation errors, missing data, or changes in how completed orders are counted.
Break down the metric by time (daily/weekly), geography (sub-markets, neighborhoods), user segments (new vs. existing, device), and order characteristics (restaurant type, delivery time) to identify where the drop is concentrated.
Form hypotheses based on segments: supply issues (fewer Dashers, restaurant closures), demand issues (fewer users, lower conversion), operational issues (longer delivery times, cancellations), or external factors (weather, holidays, competitors).
Check for recent product changes, marketing campaigns, pricing updates, or operational shifts. Also consider external events like weather, traffic, local regulations, or competitor actions.
Estimate the contribution of each factor to the drop, prioritize based on impact and feasibility, and propose next steps such as deeper analysis, A/B tests, or operational fixes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting and also where I started to feel the pressure.
Start by clarifying the metric definition and the proposed incentive, then design a randomized controlled experiment with a clear hypothesis, primary metric (online minutes per day), guardrail metrics, and power analysis. Outline the rollout plan, analysis approach, and how you'll interpret results to decide whether to scale, iterate, or stop.
Pro tip: Define the metric precisely and consider whether the incentive could cause gaming or unintended consequences; always include guardrail metrics like order completion rate and merchant satisfaction to ensure the fix doesn't harm other parts of the business.
Confirm what 'online minutes per day' means (e.g., time logged into the merchant portal or app) and the proposed incentive. State a clear hypothesis: the incentive will increase online minutes per day for treated merchants.
Choose the primary metric (average daily online minutes per merchant), secondary metrics (e.g., orders, revenue), and guardrails (e.g., cancellation rate, merchant satisfaction). Decide on randomization unit (merchant-level), sample size, and duration via power analysis.
Randomly assign merchants to treatment (incentive) and control (no incentive) groups. Ensure proper tracking and data collection, and monitor for any technical issues or novelty effects during the test period.
Compare the primary metric between groups using appropriate statistical tests (e.g., t-test or regression with covariates). Check guardrails and segment analyses (e.g., by merchant size or region) to understand heterogeneity. Decide whether to scale, iterate, or stop based on statistical and practical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.