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

0 practiced

You observe self-selection into using a personalization feature (users opt in). Propose an instrumental variable (IV) strategy to estimate the causal effect of personalization on spend. Describe a plausible instrument, testable IV assumptions (relevance and exclusion), estimation approach (two-stage least squares), and potential threats to identification.

MediumTechnical

0 practiced

Your company's new model deployment is followed by a 2% drop in conversion in production. Outline a prioritized, step-by-step measurement and diagnostic plan you would execute to identify whether the model caused the drop, including quick checks you would run in the first hour, first day, and first week. Mention what data you would collect and what analyses you would run (segmentation, pre/post, backfills, A/A tests, rollout checks).

HardTechnical

0 practiced

Long-window attribution: Users may be exposed to assistant recommendations multiple times and convert weeks later. Design a measurement plan to attribute cross-sell conversions to the assistant's recommendations over a 90-day window. Include randomized holdout design, decay-weighted attribution models, survival-analysis considerations, contamination handling, and power calculations for long windows. Which approach would you recommend and why?

HardTechnical

0 practiced

You have a human-review process that triggers when model confidence ≥ 0.9; human review affects downstream conversion. Propose a regression discontinuity design (RDD) to estimate the causal effect of human review on conversion quality. Explain the difference between sharp and fuzzy RDD and how you'd test for manipulation at the cutoff and choose bandwidth.

HardTechnical

0 practiced

Explain Double Machine Learning (DML) / orthogonalization for estimating causal treatment effects with high-dimensional controls. Describe the cross-fitting procedure, why orthogonalization reduces bias from flexible nuisance estimators, and provide simplified pseudo-code for implementing DML using arbitrary ML models for nuisance functions. Mention how you would get standard errors.

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