Design A/B Test for New Amazon Recommendation Module
Overview
This question evaluates proficiency in online experimentation, statistical inference, metric design, and causal reasoning for a new home-page recommendation feature, covering hypothesis formulation, metric hierarchy and guardrails, sample-size/duration calculation, variance reduction, sequential testing, bias sources, and alternative causal-inference approaches. It is in the Analytics & Experimentation domain and is commonly asked because product and data teams must quantify the causal impact of UI/algorithm changes and balance engagement versus revenue trade-offs; the prompt tests both conceptual understanding of experimental principles and practical application of statistical and causal techniques.
A/B Test Design: Home Page Recommendation Module
Scenario
Amazon plans to introduce a new product recommendation module on the home page and wants to evaluate its impact via online experimentation.
Task
Design an A/B test that covers:
- Hypotheses and experiment design (test vs. control, randomization unit, targeting, and triggering).
- Metric hierarchy: primary outcome, secondary metrics, and guardrails (with business justification).
- Sample size and duration: how to compute, with a small numeric example; include variance-reduction options.
- Statistical testing plan: define the p-value, how it informs decisions, and how to handle sequential looks.
- Biases: potential sources and how you would mitigate them.
- Limited exposure: if only 5% of users can be exposed, how to ensure adequate power.
- Trade-off decision: treatment raises click-through-rate (CTR) but lowers average order value (AOV); how to decide whether to launch.
- If randomization is not possible, outline a causal inference approach (e.g., difference-in-differences or propensity matching).
Include hypothesis formulation, metric hierarchy, variance reduction, sequential testing, and guardrail metrics.
Solution
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