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.

EasyTechnical

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Explain in your own words the difference between correlation and causation and why conflating them can lead to incorrect conclusions when measuring ML model impact. Give one practical example from a product analytics context where correlation could mislead a team.

MediumTechnical

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A personalization feature increases session time but not revenue. Design follow-up analyses and experiments to determine whether increased usage will lead to longer-term monetization. Include hypothesis generation, proxy metrics, segmentation, and suggested A/B tests or holdouts.

MediumTechnical

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Implement a Python function that computes required sample size per group for a two-arm A/B test on a binary conversion metric using the normal approximation. Function signature: required_n(p0, relative_lift, alpha=0.05, power=0.8, ratio=1.0). Explain any approximations and show the formula in comments.

EasyTechnical

0 practiced

You will brief a product manager on the top 3 dashboard KPIs to monitor for the first 30 days after launching a personalization model. Provide the KPIs, the aggregation frequency (daily/hourly), the visualization type you recommend for each, and a reason for the choice.

MediumSystem Design

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Design the architecture for an experimentation metric pipeline that supports 100M users and 10B events per day. Describe components for event ingestion, deduplication, bucketing, metric computation (streaming vs batch), storage for experiment results, monitoring and automated alerts, and how you guarantee reproducibility of metrics.

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