I went straight to segmentation which felt right in retrospect, but I jumped there too fast without first confirming what 'active user' even meant to them.
Start by clarifying the metric definition and time period, then systematically break down the 15% drop by segmenting users (new vs. existing, geography, device, content) and isolating internal vs. external factors. Use a hypothesis-driven approach to prioritize the most likely causes, and propose validation methods like A/B tests or cohort analysis.
Pro tip: Always consider data quality and metric definition first—sometimes a drop is due to a tracking change or a shift in what 'active user' means, not actual user behavior. Also, think about Disney+'s unique content release cadence and seasonality, which can cause natural fluctuations.
Define what 'active users' means (e.g., daily/weekly/monthly) and confirm the exact period of the drop. Check if there were any changes in tracking, logging, or definitions that could explain the decline.
Break down the drop by user cohorts (new vs. returning), geography, device/platform, subscription tier, and content engagement. Identify which segments are driving the decline.
List possible causes: content releases (e.g., end of a popular series), pricing changes, app performance issues, marketing campaigns, competitor launches, seasonality, or macroeconomic factors.
Use data to test the most likely hypotheses. For example, compare cohorts before/after a content release, run correlation analyses with external events, or check for technical issues via error logs.
Based on findings, propose actions such as A/B tests to improve engagement, content strategy adjustments, or product fixes. Outline how to measure success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.