Investigate LA successful orders drop

You are a Product/Data Scientist at DoorDash. A key metric **“# of successful orders per day”** in **Los Angeles (LA)** has dropped noticeably over the last few days/weeks. Assume: - A **successful order** = an order that is placed and ultimately delivered (not canceled) within the observation window. - You have access to event logs and operational tables for customers, dashers, restaurants, and orders. Answer the following: 1) **How would you investigate** the metric drop end-to-end? Outline your approach, the first checks you’d run, and how you’d narrow to root causes. 2) Give **one plausible hypothesis each** from the perspectives of: - **Dasher** (supply/fulfillment side) - **Customer** (demand/conversion side) - **Restaurant** (merchant/operations side) 3) Suppose **all three hypotheses contribute** to the drop. How would you **quantify each hypothesis’s impact** (i.e., estimate how much each contributes to the overall decrease)? Be specific about the method, required data, and assumptions. 4) If you investigate via a **funnel**, define a reasonable funnel for DoorDash orders in LA, list key drop-off points, and propose additional hypotheses. 5) After proposing potential improvements, explain **how you would design an A/B test** to validate an improvement (choose an example improvement). Include: - Unit of randomization and why - Primary metric + guardrails - Power/MDE considerations - Key pitfalls (e.g., interference/network effects, seasonality) and how you’d handle them