Product design question
Build a product to buy and sell antiques.
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What this question tests
Tests product design on a niche marketplace where trust, authentication, and condition disputes matter more than typical e-commerce.
How to approach it
- Clarify the target user: casual sellers clearing out inherited items versus serious collectors and dealers, since their needs diverge sharply.
- Identify the core pain point: buyers can't verify authenticity or condition remotely, which kills trust more than in typical retail.
- Design around trust: require detailed condition photos, optional third-party authentication for high-value items, and an escrow-style payment hold until delivery confirmation.
- Add discovery features suited to collectors: search by era, maker, or provenance, and saved searches for rare finds.
- Define success: transaction completion rate on listed items and post-purchase dispute rate, since low disputes prove the trust layer works.
What a strong answer includes
- Centers the design on authentication and condition trust, the real differentiator for antiques versus generic e-commerce.
- Segments sellers into casual and professional/dealer tiers, since a dealer needs bulk listing tools a casual seller doesn't.
- Proposes an escrow or buyer-protection payment flow, which is standard in high-value, low-frequency marketplaces like this one.
- Picks dispute rate as a key success metric alongside GMV, since trust failures are the biggest churn risk here.
Common mistakes
- Designing a generic e-commerce app without addressing the unique authenticity problem of antiques.
- Ignoring payment protection, which buyers of expensive, unverifiable items will demand.
Likely follow-up questions
- How would you price or monetize the authentication service?
- How would you handle a dispute where the buyer claims the item is fake?
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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