My first instinct was to go straight to the data, which I think was fine, but I spent too long on the setup and not enough time actually differentiating between the three buckets.
Start by clarifying the scope and defining what 'food delivery time' means (e.g., from order placement to delivery). Then propose a structured data-driven approach: segment the data by city, restaurant, and internal process metrics to isolate the root cause, and use statistical methods to validate hypotheses.
Pro tip: Emphasize the importance of establishing a baseline and controlling for external factors like weather or traffic, and suggest A/B testing or natural experiments to validate root causes.
Clarify the metric: what exactly is 'delivery time'? Is it end-to-end or just delivery? Define the time period and cities in question. Identify stakeholders and constraints.
Break down delivery time by city, restaurant partner, and internal process stages (e.g., order acceptance, food prep, driver assignment, transit). Look for patterns and outliers.
Develop hypotheses for each potential root cause: restaurant partner (e.g., prep time), internal process (e.g., dispatch algorithm), or market-specific (e.g., traffic, regulations). Use statistical tests or experiments to validate.
Control for confounding variables (e.g., time of day, weather) and compare across similar cities or restaurants. Use techniques like regression analysis or difference-in-differences to isolate the cause.
Based on findings, propose targeted fixes (e.g., partner training, algorithm tweaks, market-specific strategies). Suggest A/B tests or pilot programs to validate the solution before scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.