My first instinct was to jump straight to 'check if it's a data issue' and I think that was the right call, but I fumbled the structure a bit after that.
Start by validating the spike to rule out data anomalies, then segment the data to isolate the cause (e.g., by platform, demographics, or acquisition channel). Finally, correlate with internal and external events to identify the root cause and determine if it's sustainable or a one-off.
Pro tip: Always consider both internal factors (e.g., product changes, marketing campaigns) and external factors (e.g., competitor actions, local events) — and remember that correlation doesn't imply causation, so verify with multiple data sources.
Check if the spike is real by verifying data accuracy and ruling out tracking errors, bot traffic, or logging issues.
Break down the spike by dimensions like platform (iOS/Android), user demographics, acquisition channel, and engagement metrics to narrow down the source.
Align the spike with internal events (e.g., feature launches, marketing campaigns) and external events (e.g., holidays, competitor outages, viral trends).
Analyze what new users are doing: are they engaging meaningfully or just visiting once? Check retention and activity patterns.
Decide if the spike is sustainable or a one-time event, and recommend next steps (e.g., double down on successful channels, fix data issues).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.