Business Problem Solving and Recommendations Questions

Frameworks and skills for taking ambiguous business questions through analysis to clear, actionable recommendations. Includes decomposing complex problems into analyzable components, identifying key drivers, selecting focused analyses, synthesizing data backed findings, and articulating specific next steps and implementation considerations. Emphasizes communicating recommendations in business terms, estimating potential impact when possible, acknowledging trade offs and limitations, prioritizing among multiple actions, and tailoring communication to different stakeholders. Covers translating research or analytic results into feasible product or operational changes and defending choices with evidence.

HardTechnical

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Describe how you would use difference-in-differences or synthetic control methods to evaluate a product feature launched only in City X when national advertising also ramped up. Explain assumptions, data needs, limitations, and what robustness checks you would run.

EasyTechnical

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A product team plans to launch a 'saved items' feature to increase engagement. As a data analyst, list 6 KPIs (primary and leading indicators) you would define to measure success, explain how you'd instrument them in analytics events or backend logs, and recommend a minimum observation period and rollout scope for initial measurement.

HardTechnical

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A stakeholder claims that increasing free-trial length will increase paid conversion by 20%. Design an experiment and an observational fallback plan to test this claim, including metrics, sample allocation, guardrails, and how you'd estimate long-term effects beyond the test window.

EasyTechnical

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Explain the ICE and RICE prioritization frameworks and give a concrete example of how you would use one to prioritize three competing analytics projects given limited time and a mid-level expected impact.

MediumTechnical

0 practiced

Provide a concrete plan to measure the impact of a new checkout flow rolled out to 20% of users. Include metric selection, guardrail metrics, experiment duration, sample-size or power considerations, and analysis approach (including how you'd handle novelty effects and multiple-testing corrections).

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