I jumped straight to segmentation before even clarifying what 'usage' meant, which in hindsight was a mistake.
Start by clarifying what 'usage' means (e.g., DAU, sessions, key actions) and the timeframe, then segment the drop by dimensions like user type, platform, geography, and feature to isolate the cause. Use a structured root-cause framework to test hypotheses, distinguishing between internal changes (releases, pricing) and external factors (competition, seasonality).
Pro tip: Always quantify the impact and prioritize the most likely causes first—interviewers want to see you focus on the 20% of factors that could explain 80% of the drop. Also, mention that you'd check data quality and instrumentation before diving deep, as false alarms are common.
Ask clarifying questions to understand what 'usage' means (e.g., daily active users, sessions, core actions) and the exact timeframe and comparison baseline. Confirm whether the drop is sudden or gradual.
Break down the drop by key dimensions: user cohorts (new vs. existing, free vs. paid), platform (web, mobile), geography, acquisition channel, and feature usage. This helps localize the issue.
List potential causes: internal (product changes, bugs, pricing, marketing campaigns) and external (competition, seasonality, economic shifts). Prioritize based on likelihood and impact.
Use data analysis, user feedback, and engineering checks to test hypotheses. Look for correlations (e.g., release dates, error logs) and validate with qualitative insights.
Based on findings, propose immediate fixes and long-term preventive measures. Define success metrics and set up monitoring to track recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.