This one sprawled in a way I wasn't fully ready for.
Start by framing the problem as a funnel: order → prep → pickup → delivery → handoff, and identify where temperature loss occurs. Then propose a data-driven investigation using existing metrics and logs, followed by targeted experiments to validate hypotheses. Finally, recommend changes prioritized by impact and feasibility, with clear success metrics.
Pro tip: Don't jump to solutions like insulated bags; first quantify the problem and segment by restaurant, dasher, distance, and time of day to find the biggest lever. Also, consider that 'cold' is subjective—define a measurable proxy like temperature at delivery or time-out-of-oven.
Clarify what 'cold' means (e.g., temperature below threshold, customer rating) and quantify its prevalence using complaint rates, ratings, and delivery timestamps. Segment by geography, restaurant type, order size, and time.
Break down the order-to-delivery process into stages: food prep, waiting for dasher, dasher travel, and handoff. Form hypotheses about where heat loss occurs, such as long prep-to-pickup times or multiple deliveries.
Use existing data (order logs, GPS, timestamps) to correlate cold complaints with factors like total delivery time, dasher wait time, distance, and batching. Run statistical tests to identify significant drivers.
Prioritize hypotheses and run A/B tests, e.g., testing insulated bags, optimizing dispatch to reduce wait times, or adjusting batching algorithms. Measure impact on temperature proxy and customer satisfaction.
Based on experiment results, recommend scalable changes (e.g., new packaging standards, dispatch tweaks) and define ongoing metrics to track improvement, ensuring changes are cost-effective.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.