I went with session count as my metric since it felt cleaner to defend than total duration.
Start by clarifying the usage metric (e.g., daily active users, sessions, or time spent) and the definition of 'most-used' (e.g., highest average DAU over the 7 days). Then, write a SQL query that aggregates usage per app per day, filters to the 7-day window relative to the given date, computes the chosen metric, and returns the top app.
Pro tip: Always state your assumptions about the metric and time window explicitly, and consider edge cases like apps with missing data on some days. Mention that you would validate the metric with product stakeholders to ensure it aligns with business goals.
Define what 'most-used' means (e.g., highest average daily active users, total sessions, or total time spent) and confirm the 7-day window (e.g., the 7 days ending on the given date).
Assume a usage table with columns like app_id, user_id, date, and possibly session_count or duration. Also assume an apps table with app_id and app_name.
Write a subquery or CTE that groups by app_id and date, computing the daily metric (e.g., COUNT(DISTINCT user_id) for DAU).
Filter to the 7-day window, then aggregate across days (e.g., AVG(daily_metric)) and order descending to find the top app.
Join the aggregated result with the apps table to get app_name, and select the top app (e.g., using LIMIT 1 or a window function).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Session count rewards apps people open frequently but briefly, like a news app.
Start by clarifying the business objective behind 'most used app' and the decision it will inform, then systematically compare the three definitions across dimensions like user intent, engagement quality, and business impact. Conclude by recommending a definition or composite metric based on the specific context and trade-offs.
Pro tip: Acknowledge that no single metric is perfect and propose a composite or tiered approach (e.g., primary metric with guardrails) to show strategic thinking. Also, mention that the choice should align with the company's north star and be validated through A/B testing or causal analysis.
Ask what decision the 'most used app' metric will inform (e.g., resource allocation, feature prioritization) and who the stakeholders are. This ensures the definition aligns with business goals.
Clearly define number of sessions, total time spent, and unique users, including how they are measured (e.g., session timeout, active vs. passive time).
Compare the metrics on dimensions such as sensitivity to outliers, ability to capture depth vs. breadth of engagement, susceptibility to gaming, and alignment with user value.
Discuss how the choice might vary by app type (e.g., utility vs. social) and user segments, and potential pitfalls like double-counting or bot traffic.
Propose a primary metric or composite, suggest guardrail metrics, and outline how to validate the choice (e.g., correlation with retention, A/B testing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.