← Flatiron Health Interview Insights
I started with the obvious stuff: did anything ship recently, any infra changes, any external events.
Start by clarifying the metric definition and confirming the drop is real, not a data artifact. Then systematically segment the data to isolate the cause, considering internal changes, external factors, and user behavior shifts. Finally, prioritize hypotheses and validate with qualitative and quantitative evidence.
Pro tip: In healthcare, always consider regulatory or data privacy changes that could affect tool usage, and check if the drop is consistent across user segments or isolated to a specific group.
Define what 'usage' means (e.g., daily active users, sessions, actions) and verify the data pipeline for errors or tracking changes. Confirm the drop is real and not due to logging issues.
Break down usage by dimensions like user role, geography, device, time, and feature to identify if the drop is broad or concentrated. Look for patterns that suggest a specific cause.
Brainstorm potential causes: internal (product changes, bugs, releases), external (seasonality, market events, competitor actions), and user behavior (onboarding, training, workflow changes).
Use data analysis, user interviews, and logs to test hypotheses. Check release notes, support tickets, and system status for anomalies. Correlate with other metrics to confirm impact.
Summarize findings, identify root cause, and propose next steps: fix if internal, adapt if external, or further investigate if inconclusive. Communicate clearly to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.