Business Impact Measurement and Metrics Questions

Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.

MediumTechnical

94 practiced

Design a measurement plan to evaluate a churn-prediction model that triggers retention offers. Include: primary and secondary metrics, evaluation window, randomization strategy (e.g., who receives offers), sample size reasoning tied to unit economics (cost per offer, expected retention uplift), and how you'd decide whether the feature is profitable.

MediumTechnical

85 practiced

Define a concrete operational definition of 6-month LTV for a subscription product and describe how you would compute it using cohort analysis. Explain how you would handle censored users, refunds, churn, and discounting in your calculation.

HardTechnical

88 practiced

You observe a 10% revenue uplift in an observational comparison between users exposed to a recommender and those not exposed. Assignment correlates with user intent (higher-intent users more likely to receive exposure). Propose a causal attribution strategy combining propensity score weighting, instrumental variables or encouragement designs, and sensitivity analysis to unobserved confounding. How would you communicate confidence to executives?

MediumTechnical

77 practiced

You plan to roll out a feature to a subset of geographies at different times. Explain how you would use difference-in-differences (DiD) with staggered adoption to estimate average treatment effect. Describe required diagnostics (e.g., pre-trend checks) and how you'd account for potential spillovers between geos.

EasyTechnical

81 practiced

Given the following schema:

users(user_id, signup_date)
events(user_id, event_type, event_time)

Write a SQL query (ANSI SQL) to compute 7-day retention rate for users who signed up between 2025-01-01 and 2025-01-07. Define 7-day retention as having any event in days 1-7 after signup (inclusive). Explain how you would handle timezone issues and duplicate events.

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