I jumped straight to segmentation (platform, region, user cohort) which felt right, but I forgot to ask about the timeframe first and that probably hurt me.
Start by clarifying the metric definition and validating the data to rule out tracking or reporting issues. Then segment the drop across dimensions like platform, geography, and user cohorts to isolate the cause, and finally hypothesize and test potential drivers using both quantitative and qualitative methods.
Pro tip: Always validate the data first—many 'drops' are instrumentation bugs or seasonal effects. Also, consider external factors like crypto market volatility or competitor launches, which are common in the crypto space.
Confirm the exact definition of DAU and check if the drop is real by reviewing data pipelines, tracking, and recent releases. Rule out false alarms due to logging errors or seasonality.
Break down DAU by dimensions such as platform (iOS/Android/web), geography, user tenure, and acquisition channel to identify which segments are driving the decline.
Generate hypotheses for the drop, including internal factors (product changes, bugs, pricing) and external factors (market conditions, competitors, regulations).
Use quantitative analysis (cohort analysis, funnel analysis, correlation with releases) and qualitative methods (user feedback, support tickets) to test hypotheses and pinpoint root cause.
Based on findings, propose immediate fixes and long-term monitoring to prevent future drops, and outline how to measure impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.