I went straight to 'engagement per user is dropping' which is the obvious read, but I should've slowed down and questioned the metric definition first.
Start by clarifying what 'engagement' means (e.g., DAU/MAU, session duration, actions per user) and the time frame. Then systematically break down the user journey to identify where engagement drops, using data to segment by cohort, acquisition channel, and user behavior. Finally, propose hypotheses and suggest experiments to validate root causes.
Pro tip: Show that you think like an engineer by considering data pipeline integrity and metric definitions first—flat engagement might be a measurement artifact. Also, emphasize the importance of cohort analysis to separate new user behavior from existing user trends.
Ask what 'engagement' specifically refers to (e.g., DAU/MAU, sessions per user, time spent) and what 'flat' means (no growth, slight decline). Confirm the time period and whether the goal is to increase engagement or understand the cause.
Break down engagement by user cohorts (new vs. existing), acquisition channels, geography, device, and feature usage. Look for disparities that might indicate where the problem lies.
Map out key engagement milestones (e.g., first session, first action, return visit) and measure conversion rates at each step. Identify drop-off points and compare across cohorts.
Based on data, propose possible causes (e.g., new users are low-quality, onboarding is broken, feature changes hurt engagement). Suggest A/B tests or further data pulls to validate.
Summarize findings and propose actionable experiments or product changes. Emphasize iterative investigation and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.