I went straight into root cause territory, which felt right, but I think I spent too long on the diagnosis side and rushed the action plan.
Start by clarifying the metric definition and scope of the 10% drop, then systematically segment the data to isolate the root cause. Once identified, prioritize solutions based on impact and effort, and propose a test-and-learn approach to validate fixes.
Pro tip: Demonstrate a hypothesis-driven approach by stating your top hypotheses upfront and how you'd validate them, rather than just listing data to pull. This shows you can think like a PM who prioritizes efficiently.
Define 'restaurant supply' precisely (e.g., active restaurants, menu items, or inventory) and confirm the 10% drop is real and not a data anomaly. Check if the drop is global or specific to certain regions, cuisines, or restaurant tiers.
Break down the metric by dimensions like geography, time, restaurant size, and platform changes to identify patterns. Form hypotheses for the root cause, such as seasonality, competitive actions, operational issues, or recent product changes.
Use data to test each hypothesis: compare with historical trends, check for correlation with external events, and interview restaurants or support teams. Prioritize the most likely causes based on evidence.
Based on root cause, brainstorm potential fixes and evaluate them on impact, effort, and speed. Consider quick wins (e.g., fixing a bug) and longer-term strategies (e.g., new restaurant incentives).
Propose a plan to implement the chosen solution, including A/B testing if applicable, and define success metrics to track recovery. Set up ongoing monitoring to prevent future drops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.