I started with clarifying questions about the timeframe and whether it was across all markets or localized, which felt right.
Start by clarifying the scope and timeframe of the 45% drop, then systematically break down the metric into its components (e.g., by geography, user segment, ride type, and funnel stage) to isolate the cause. Prioritize hypotheses based on internal vs. external factors and validate with data before proposing solutions.
Pro tip: Demonstrate a hypothesis-driven approach by ranking potential causes by likelihood and impact, and mention how you would use A/B tests or cohort analysis to confirm the root cause. Also, consider both supply and demand sides, as ride orders depend on driver availability and rider demand.
Ask clarifying questions to understand the metric definition, timeframe, and whether the drop is global or segment-specific. Confirm if it's a sudden or gradual decline and if it's consistent across platforms.
Break down the 45% drop by dimensions such as geography, time, user demographics, ride type, and acquisition channel. Use funnel analysis to see where the drop-off occurs (e.g., app opens, ride requests, completed rides).
Brainstorm potential internal and external causes: seasonality, competitor actions, pricing changes, product bugs, marketing campaigns, driver supply issues, or macroeconomic factors. Prioritize by likelihood and impact.
Use data analysis to test each hypothesis: compare cohorts, run correlation analyses, check for anomalies in logs, and review external benchmarks. Consider A/B tests if applicable.
Summarize findings, identify the most probable root cause(s), and propose next steps for mitigation or further investigation. Outline how you would monitor the metric going forward.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.