Product design question
How would you improve Amazon Prime?
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Product-improvement reasoning for a loyalty/subscription bundle, identifying which benefit lever to pull and how to measure impact.
How to approach it
- Identify Prime's core value pillars: fast shipping, video/entertainment content, and increasingly grocery and other perks, and pick one pillar with a clear gap.
- Name a concrete pain point, e.g. Prime members in lower-density areas not reliably getting the promised delivery speed, undermining the core value proposition.
- Propose a focused improvement: more accurate, hyper-local delivery-speed promises shown before checkout, rather than a blanket one or two-day claim.
- Explain the business tie-in: delivery-speed trust is the anchor benefit driving renewal, so improving it directly protects retention.
- Define success as delivery-promise accuracy rate and Prime renewal rate in the affected regions, as a guardrail against overpromising elsewhere.
What a strong answer includes
- Picks one pillar and one concrete gap rather than listing generic ideas across shipping, video, and grocery all at once.
- Ties the improvement directly to renewal, the core business metric Prime actually depends on.
- Defines a measurable proxy, promise accuracy, tightly linked to the trust problem identified.
Common mistakes
- Proposing unrelated new perks (more video content) instead of strengthening the core value proposition, fast and reliable shipping.
- No tie to renewal or retention, missing why Prime's business model depends on trust in the core promise.
Likely follow-up questions
- How would you prioritize this against expanding Prime Video content?
- How would you measure the trade-off between promising slower, more accurate delivery windows and losing the 'two-day' brand promise?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop