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.

HardTechnical

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Treatment effects vary across user segments. Propose an algorithmic pipeline to detect and validate subgroups with positive treatment effects, controlling for multiple testing and avoiding overfitting. Include how you'd operationalize targeting using uplift models or causal forests and how you'd validate on holdout data.

EasyTechnical

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Write a one-paragraph, non-technical stakeholder-facing explanation for the following result: model accuracy improved by 5% on offline test sets, but the primary business KPI (net revenue per user) showed no statistically significant change in the online A/B. Include recommended next steps and how you'd quantify uncertainty.

HardSystem Design

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Concurrent experiments may interfere with each other. Propose a randomization and analysis strategy to run multiple interacting experiments safely: discuss orthogonal randomization, factorial designs, regression models including interaction terms, and how to allocate sample to preserve power for interaction detection.

MediumTechnical

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You need to attribute conversion lift to a referral model across several channels. Contrast last-touch, time-decay multi-touch, and causal attribution (incrementality via experiments). For each approach, list the main practical trade-offs and data requirements, and recommend one for a situation with cross-device users and partial telemetry loss.

HardTechnical

0 practiced

You're asked to design experiments to measure impact on a very rare event (e.g., 0.1% high-value purchase). Describe design choices—stratified sampling, oversampling high-propensity cohorts, cohort-level randomization, or using stronger proxies—to increase power within budget. Discuss bias trade-offs and how you would validate the approach.

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