Metrics question

How would you optimize Amazon’s product recommendation algorithm to increase conversions?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to define the right optimization target and guardrails for a recommendation system, avoiding a naive click maximization approach.

Clarify the current objective the algorithm likely optimizes for, clicks or add to cart, and why it may not equal purchase conversion.
Propose shifting the ranking objective closer to purchase probability, not just click through, since clicks can be gamed by clickbait thumbnails.
Add guardrails: return rate and customer satisfaction for recommended items, since maximizing conversion alone could recommend high return products.
Propose an A/B test comparing the new conversion weighted model against the current one on a held out traffic slice.
Define primary and guardrail metrics clearly: conversion rate lift as primary, return rate and repeat purchase rate as guardrails.

Identifies the objective mismatch as the actual lever, not just improve the algorithm vaguely.
Adds return rate as a guardrail, catching a realistic failure mode where conversion optimized recommendations increase buyer's remorse.
Structures the test as a proper A/B experiment with clear primary and guardrail metrics.

Optimizing purely for conversion rate without a guardrail against increased returns or dissatisfaction.
No A/B test plan, just a described model change with no validation method.

How would you handle a product category where high returns are normal, like clothing?
What would make you roll back the new model after launch?