Define and Analyze Early Engagement

# Define and Analyze Early Engagement A product team has a `users` dataset and a `transactions` dataset. The team asks how to define early engagement, compute a chosen metric, inspect its trend and useful breakdowns, and communicate the conclusion. The source intentionally does not fix the columns, engagement window, cohort rule, denominator, or output layout, so state each decision instead of assuming it is given. ### Clarifying Questions to Ask - What user action represents meaningful value rather than incidental activity? - Is “early” measured from signup, activation, or another event, and how complete is the observation window? - Which users are eligible for the denominator, and which segments matter to the decision? ### Part 1 — Define the Metric Choose a defensible early-engagement metric and explain its event, window, cohort, numerator, denominator, and known biases. #### What This Part Should Cover - A precise, reproducible metric definition. - Treatment of immature cohorts, duplicate activity, and users with no transactions. ### Part 2 — Compute and Diagnose It Describe the relational steps a query would perform, then explain which time trends and segment cuts you would inspect. Do not invent a fixed source schema; name the fields your approach requires. #### What This Part Should Cover - Join grain, cohorting, aggregation, and validation checks. - Breakdowns chosen to test specific hypotheses rather than produce arbitrary slices. ### Part 3 — Report the Result Explain how you would distinguish observation from causal interpretation and present the result to stakeholders. #### What This Part Should Cover - Uncertainty, data caveats, practical significance, and a decision recommendation. ```hint Make every denominator auditable A metric is not reproducible until another analyst can identify exactly which users enter each side of the ratio. ``` ### What a Strong Answer Covers - A coherent chain from business concept to metric to query plan to decision. - Explicit assumptions where the source is open-ended. - Checks for cohort maturity, missing data, and misleading aggregation. ### Follow-up Questions - How would you tell whether a trend change came from user mix rather than behavior? - What experiment could test whether improving the chosen metric causes longer-term value?