Diagnose rising cold-food complaints and choose metrics

## Case: Customers complain food arrives cold You are a **Data Scientist (Analytics-focused)** at a food delivery marketplace (e.g., DoorDash). Over the last few weeks, customer support tickets and app feedback indicate a **material increase in “food arrived cold” complaints**. Leadership asks you to diagnose likely causes and propose data-driven fixes. ### Business context (assume typical delivery flow) An order goes through: 1. **Order placed** (customer selects items, pays) 2. **Restaurant accepts & prepares** 3. **Dasher assigned & travels to pickup** 4. **Pickup happens** (food is handed to dasher) 5. **Travel to drop-off** 6. **Delivered** Assume you have access to event-level order logs (timestamps for each step), restaurant and dasher attributes, geography, batching/stacking flags, and customer feedback (ratings, complaint reason codes, refunds/credits). ### Tasks 1. **Hypothesize potential drivers** of colder food (generate multiple plausible root causes across restaurant operations, dispatch/assignment, dasher behavior, batching/stacking, distance/ETA accuracy, and seasonality/weather). 2. For each hypothesis, specify: - **What data/fields you would analyze** (and what joins or slices you’d do conceptually) - **What comparisons** you’d run (time series, cohorting, matched comparisons, geo/restaurant segments, etc.) - **Key confounders** to control for (e.g., order distance, cuisine type, weather, promos changing demand mix) 3. Propose **metrics** to measure: - **Customer feedback / experience** (include multiple options and tradeoffs) - **Revenue / business impact** (include multiple options and tradeoffs) - **Guardrails** (to avoid improving “warmth” at the expense of other outcomes) Clearly identify **one primary metric** and a small set of **diagnostic/guardrail metrics**. 4. Recommend **at least two interventions** (product/ops/policy) you would test, and outline an **experiment or quasi-experiment** plan: - Unit of randomization (order/restaurant/dasher/geo) - Success criteria and expected side effects - How you would handle interference/network effects (marketplace supply/demand) and delayed outcomes (refunds, repeat rate) Deliver your answer as a structured investigation plan plus an experimentation plan.