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.

EasyTechnical

0 practiced

List and explain five essential data quality checks you would run before trusting product analytics for decision-making. For each check, describe how you'd detect the issue in SQL or Python and one practical remediation or assumption you might apply (e.g., drop, impute, or flag).

MediumTechnical

0 practiced

Outline the content and talking points for a concise 3-slide executive presentation to communicate an analytic recommendation. Slide 1: problem and impact; Slide 2: approach and key evidence; Slide 3: recommendation, risks, and next steps. Provide example bullets for each slide and suggested visuals.

MediumTechnical

0 practiced

Explain uplift (treatment-effect) modeling: define the objective, how it differs from standard prediction tasks, common algorithms used (e.g., two-model, single-model transformation, causal forests), evaluation metrics (Qini, AUUC), and give a concrete business example where uplift modeling is preferable to scoring by conversion probability.

MediumTechnical

0 practiced

A production churn model's AUC dropped from 0.78 to 0.66 over two months. Outline a systematic debugging plan to find the root cause: list the specific data, model, and business checks you would run (e.g., feature distributions, label delay, upstream pipeline changes), scripts/queries to run, how you'd prioritize potential causes, and your communication plan to stakeholders.

HardTechnical

0 practiced

You recommend a product change that reduces short-term revenue by 8% but, based on experiments, improves 12-month retention by 10%, increasing long-term NPV. How would you build the evidence package to convince finance and product leadership: what datasets, experiments, sensitivity and break-even analyses, rollout and rollback plans, and KPIs would you present?

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