Diagnose cold-food spike and design experiments

A food-delivery company sees a recent spike in customers complaining their food arrived cold. Using real-world rigor: 1) Define the core outcome metric(s) and a metric tree that isolates where heat loss likely occurs (e.g., prep_time, wait_at_restaurant, transit_time, distance, batching_count, outside_temp, packaging_type, courier_bag_type). How will you measure each and set guardrails (e.g., ETA accuracy, cancellations, CSAT/NPS, refund_rate)? 2) Propose a structured diagnostic plan (within 72 hours) that prioritizes the highest-variance contributors: what slices, cohorting, and negative controls will you use (e.g., wrong_item complaints as a negative control, weather-matched day-over-day, restaurant fixed effects)? 3) Design one decisive A/B test to reduce cold deliveries (e.g., mandate insulated bags for a subset of couriers or disable batching beyond 2 orders for long distances). Specify: experimental unit and randomization (e.g., courier-day, restaurant-day), sample-size assumptions (baseline cold_rate and minimal detectable effect), primary/secondary endpoints, guardrails, power, duration, ramp plan, and spillover mitigation. 4) Explain how you’d attribute improvements to the change vs. concurrent factors like weather or promotions (e.g., difference-in-differences city pairs, CUPED, or stratified randomization). 5) If the test backfires by increasing delivery time by 6% but lowers cold_rate by 2 percentage points, outline a decision framework to trade off CX vs. speed, and what follow-up tests you’d run.