Brainstorm a business problem approach
Analytics & Experimentation Brainstorm (Scenario Provided)
Context
You are evaluating a feature proposal for a large consumer e-commerce site: add a "sticky Add to Cart" (ATC) button on mobile product detail pages (PDPs) that stays visible as users scroll. The goal is to increase add-to-cart conversion without harming performance, accessibility, or overall customer experience.
Assume for planning purposes:
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Baseline PDP add-to-cart rate (per eligible session) = 8%.
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Daily eligible mobile PDP sessions = 80,000.
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Significance level α = 0.05 (two-tailed), power = 0.8.
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Desired minimum detectable effect (MDE) = 5% relative uplift on ATC rate.
Task
Brainstorm and outline an approach that covers:
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Success metrics and constraints
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Define primary/secondary metrics and guardrails. State key non-functional constraints (e.g., latency, accessibility).
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Hypotheses
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List plausible hypotheses for why the feature may help or harm, and where effects might differ (segments, categories, device characteristics).
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Required data and instrumentation
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Identify what data needs to be logged (events, identifiers, attributes), experiment keys, and quality checks.
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MVP experiment or analysis plan
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Define randomization unit and eligibility.
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Specify control/variant and exposure.
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Estimate sample size and recommend test duration.
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Outline analysis steps and decision criteria.
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ML versus heuristic baselines
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If you were to gate or personalize the feature, compare a simple heuristic baseline with a potential ML approach and how you would evaluate them.
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Risks and mitigations
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Enumerate major product, data, and statistical risks and how you would detect and mitigate them.
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
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State assumptions about instrumentation, randomization, sample size, and data quality.
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Separate descriptive analysis from causal claims.
What a Strong Answer Covers
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A metric framework with primary, guardrail, and diagnostic metrics.
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A credible analysis or experiment design with clear assumptions and bias checks.
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SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
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An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions
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What sanity checks would you run before trusting the result?
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How would you handle novelty effects, seasonality, or selection bias?
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What decision would you make if metrics disagree?
