My first instinct was to just say 'run an A/B test and look at conversion' which is fine but way too shallow for Amazon.
Start by framing the problem as a causal inference question: you need to isolate the recommendation engine's impact on revenue from other factors. Propose a randomized controlled experiment (A/B test) as the gold standard, then discuss how to measure incremental revenue and validate with guardrail metrics.
Pro tip: Emphasize measuring incremental revenue, not just total revenue, and mention that you'd check for cannibalization or novelty effects to ensure the lift is real and sustainable.
Clarify what 'moved revenue' means: incremental revenue per user, conversion rate, or average order value. State a clear hypothesis, e.g., 'The new engine increases revenue per user by X% without harming customer experience.'
Propose an A/B test with random assignment: control group sees the old engine, treatment group sees the new one. Ensure sufficient sample size and duration to detect a meaningful effect.
Compare revenue metrics between groups, focusing on incremental lift (treatment minus control). Use statistical tests to determine significance and confidence intervals.
Monitor metrics like customer satisfaction, return rates, and long-term engagement to ensure no negative side effects. Segment results by user cohorts to understand heterogeneous effects.
If results are positive, consider a holdback group for long-term validation. If not, analyze why and iterate on the model or experiment design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.