Strategy question

How would you increase the basket size of different products for users?

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

Growth strategy: can you connect a business goal (basket size) to specific product levers and their tradeoffs, at Amazon scale.

Clarify the metric: average items per order or average order value, since the levers differ.
Segment by shopper type, e.g. Prime vs non-Prime, or grocery vs general merchandise, since basket-building behavior differs.
List levers: cross-sell/bundle recommendations, free-shipping thresholds, subscribe-and-save, and checkout-page upsells.
Pick the highest-leverage lever, e.g. raising visibility of 'frequently bought together' at checkout, and explain why.
Flag the risk: pushing basket size too hard (dark patterns, threshold gaming) can hurt trust and long-term retention.
State the success metric: AOV lift without a drop in order frequency or NPS.

Distinguishes basket size (items) from order value (dollars), since a strong answer picks one and reasons about it explicitly.
Uses a concrete lever like adjusting the free-shipping threshold (e.g. from $25 to $35, marked as assumption) and predicts the behavior change.
Flags cannibalization risk: bundling can reduce the number of orders even as size per order grows.
Names a guardrail metric, like return rate or customer complaints, to check the tactic isn't just gaming behavior.

Chapters of the AI PM course that teach what this question tests.