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Data and Business Outcomes Questions

This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.

EasyTechnical
42 practiced
A stakeholder asks: 'Will changing our onboarding flow increase retention by 5%?' In plain language, outline the steps you would take to answer this question using data (from hypothesis to deployment of experiment).
EasyTechnical
39 practiced
Describe three simple ways to measure customer churn for a monthly subscription service and note one pro and one con for each method.
MediumTechnical
49 practiced
You are assessing acquisition channels. Explain how to compute and compare payback period and CAC:LTV ratio across channels. Describe pitfalls when using average LTV and how to adjust for time-to-first-purchase differences.
HardTechnical
50 practiced
Explain how you'd use propensity score matching to estimate the effect of a loyalty program (non-random adoption) on customer spend. Include assumptions, covariates selection, diagnostics to check balance, and limitations.
EasyTechnical
40 practiced
Give an example of a guardrail metric you would include when running experiments aimed at increasing gross merchandise value (GMV), explain why it's important, and how you'd set automated alerts around it.

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