This one is harder than it looks because you can't just A/B test dashers in isolation.
Start by clarifying the business objective and the causal question: does time-based pay improve dasher efficiency and satisfaction without harming marketplace balance? Then outline a randomized experiment design with careful metric selection and spillover mitigation, and discuss how you'd analyze heterogeneous effects and make a launch decision.
Pro tip: Emphasize that dashers are not independent—they interact with each other and with the platform's matching algorithm—so you must design for interference, e.g., by randomizing at the zone level and using switchback or cluster randomization. Also, mention that you'd pre-register the analysis plan to avoid p-hacking.
Clarify what 'success' means: e.g., increase dasher utilization, reduce delivery time, maintain or improve consumer experience, and keep unit economics sustainable. Specify the primary metric and guardrail metrics upfront.
Because dashers compete for orders, individual randomization can bias results. Use cluster randomization (e.g., by city or zone) or a switchback design where the entire market alternates between compensation models over time. Ensure sufficient power by simulating under different spillover scenarios.
Track dasher metrics (earnings per hour, acceptance rate, completion rate, active time), consumer metrics (delivery time, order cancellation, ratings, retention), merchant metrics (order volume, preparation time, cancellations), and platform metrics (cost per delivery, total deliveries, contribution margin).
Use causal inference methods (e.g., difference-in-differences, synthetic control, or instrumental variables) to estimate direct and indirect effects. Check for equilibrium effects: if time-based pay changes dasher supply, it may affect consumer wait times and merchant order flow.
Evaluate results against pre-registered thresholds, conduct subgroup analyses (e.g., by market density, dasher tenure), and simulate long-term impact. Recommend pilot expansion only if benefits are robust and spillovers are manageable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition and scope (e.g., time period, geography, platform) to ensure alignment. Then systematically break down the funnel to isolate the drop, segment by dimensions to find patterns, and finally quantify the business impact in terms of orders and revenue. Use a hypothesis-driven approach, prioritizing the most likely causes based on data.
Pro tip: Always tie the diagnosis back to business impact early and often—interviewers want to see that you can prioritize actions based on potential revenue loss. Also, mention that you'd validate findings with A/B tests or holdout groups when possible.
Confirm the exact definition of 'order completion rate' and the time frame of the drop. Check for data pipeline issues, logging errors, or changes in tracking that could cause a false alarm.
Break down the metric by dimensions such as platform (iOS/Android), geography, user cohort, restaurant type, and time of day. Identify which segments are driving the overall drop.
Map out the order completion funnel (e.g., app open → search → add to cart → checkout → payment → delivery). Compare conversion rates at each step pre- and post-drop to pinpoint where the drop occurs. Generate hypotheses (e.g., payment failures, app crashes, competitor promotion).
Estimate the number of lost orders and revenue by applying the drop rate to the affected segment's baseline volume. Consider downstream effects like customer churn and lifetime value.
Rank hypotheses by likelihood and impact, and propose immediate fixes (e.g., rollback a release) and further analyses (e.g., A/B test a new checkout flow).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.