Analyze DoorDash marketplace product decisions

You are a product-focused data scientist at DoorDash. Discuss how you would approach the following three product analytics and experimentation problems. 1. **Top Dasher program** DoorDash is considering changes to the **Top Dasher** program, which gives certain dashers additional benefits and may affect fulfillment quality, dasher incentives, and marketplace balance. - What are the main pros and cons of the program from the perspectives of consumers, dashers, merchants, and DoorDash? - What success metrics, secondary metrics, and guardrail metrics would you define? - What should the randomization unit be for an experiment, and why? - If the experiment’s primary metric is lower in treatment than in control, how would you investigate before deciding whether to ship, iterate, or roll back? 2. **Order cancellation rate is increasing** Suppose the overall order cancellation rate has risen materially over the last several weeks. - How would you diagnose the problem? - Which parts of the organization or product funnel could be contributing to the increase? - How would you identify likely root causes rather than just correlations? - How would you test your hypotheses and prioritize actions? 3. **Merchant-created promotions vs. automatically generated promotions** DoorDash is deciding between two promotion systems for merchants: - merchants manually create and configure promotions themselves, or - DoorDash automatically recommends or launches promotions on their behalf. Compare the pros and cons of the two approaches, including trade-offs in merchant control, adoption, incremental demand, profitability, and marketplace health. Then design an experiment to evaluate the better approach: - define the key product and business metrics, - choose the right randomization unit, - discuss spillover effects and selection bias, - and explain how you would interpret the results if different stakeholders benefit in different ways.