← Instacart Interview Insights
The maturity angle is what trips people up here and I almost glossed over it too.
Start by validating the data pipeline and metric definition to rule out measurement issues, then segment the decline by dimensions like platform, geography, and user cohort to isolate the cause. Finally, use statistical tests and compare against control groups or historical patterns to determine if the drop is real and actionable.
Pro tip: Always check if the decline coincides with a recent app release, tracking change, or seasonality event—many 'product problems' are actually instrumentation or external factors. Also, quantify the impact in absolute numbers (e.g., users affected) to prioritize investigation.
Check for pipeline failures, logging errors, or changes in how D14 retention is calculated (e.g., definition of 'day 14', user eligibility). Ensure the metric is consistent across time.
Break down the decline by dimensions such as platform (iOS/Android), acquisition channel, geography, user cohort, and experiment groups to see if it's concentrated or widespread.
Compare the current week's retention to historical trends, seasonality, and a control group (if available from A/B tests). Check if similar declines occurred in the past without product changes.
Look for recent product changes, marketing campaigns, app store updates, or external events (e.g., holidays, competitors) that could explain the drop. Correlate timing with releases.
Run statistical tests (e.g., t-test, anomaly detection) to confirm the decline is significant. Estimate the business impact and decide if further deep-dive or action is needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.