Product team wants to launch a new recommendation algorithm and asks for a rigorous A/B test plan.

Design an A/B test to measure the uplift of the new recommender on GMV. Which primary and guardrail metrics will you track and why? Compute required sample size assuming 3% baseline conversion, 7% relative lift, α=0.05, power=0.8. How would you address novelty effect and uneven seasonality across groups? Explain how you would interpret results if the primary metric is flat but secondary engagement metrics improve.

Talk randomization, CUPED, sequential testing, and post-test segmentation.

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