Three parts crammed into one question, which I did not fully clock until I was already halfway through the first part.
Start by clarifying the data schema and assumptions (e.g., session definition, timezone, app switching). Then break the problem into three parts: compute average session length per app using SQL aggregation, propose a metric like DAU or engagement rate with justification, and compute bounce rate using a self-join or window functions to track app switches and returns. Finally, discuss trade-offs and edge cases.
Pro tip: Demonstrate product sense by linking metrics to business goals (e.g., bounce rate as a proxy for user friction) and mention how you'd validate the metric (e.g., A/B testing or correlation with retention).
Ask about table structure (columns like user_id, app_id, session_start, session_end), definition of a session, timezone, and whether 'day' is calendar day or 24-hour period. Confirm if sessions can span multiple apps.
Write SQL to calculate session duration (end - start) and average per app for the given day. Use GROUP BY app_id and filter by date. Handle edge cases like null end times or overlapping sessions.
Suggest a metric like Daily Active Users (DAU) or average session length per user, and justify based on business goals (e.g., engagement vs. retention). Discuss pros and cons of alternatives.
Define bounce as a user switching to a different app and returning to the original app within the same day. Use window functions (LAG/LEAD) or self-joins to identify sequences of app usage per user per day, then calculate the proportion of users who bounce.
Mention potential issues like session boundaries, timezone effects, and whether bounce rate should be per user or per session. Suggest validation with real data or A/B tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.