My first instinct was to jump straight to dasher supply, which in hindsight was too narrow.
Start by clarifying the metric definition and scope of the drop (e.g., time period, regions, platforms) to ensure you're solving the right problem. Then systematically segment the data to isolate the cause, considering both technical and business factors. Finally, validate hypotheses with data and propose actionable next steps.
Pro tip: Demonstrate a hypothesis-driven approach by prioritizing the most likely causes first (e.g., recent deployments, external events) and using a process of elimination. Also, emphasize the importance of quantifying the impact and communicating findings clearly to stakeholders.
Confirm the exact metric definition, time frame, and scope (e.g., all regions or specific ones). Ask clarifying questions to understand if the drop is sudden or gradual, and if it's consistent across platforms.
Break down the metric by dimensions such as region, platform, delivery partner, restaurant, and time of day. Use dashboards or queries to identify which segments are most affected.
Investigate recent code deployments, infrastructure changes, or third-party service outages. Look at error logs, latency metrics, and success rates of dependent services.
Consider changes in user behavior, marketing campaigns, competitor actions, weather events, or holidays. Correlate with business metrics like order volume, cancellation rates, and customer support tickets.
Form hypotheses and test them with data (e.g., A/B tests, cohort analysis). Once root cause is identified, propose fixes, monitor impact, and communicate findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.