Strategy question
How would you decide if you should build a "Products you might want to buy" feature on the Facebook Marketplace newsfeed?
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Whether you can size an opportunity and decide if a feature is worth building, not just design it.
How to approach it
- Clarify the goal: is this about GMV for sellers, discovery for buyers, or ad revenue.
- Define the target user, likely newsfeed browsers who are not actively searching Marketplace.
- Size the opportunity using daily active newsfeed users, click through rate on similar units, and conversion to purchase.
- Weigh the cost: engineering to build a recommendation model, and the risk of feed clutter hurting session time.
- Compare against alternatives like a dedicated Marketplace tab or push notifications.
- State a go or no go recommendation tied to a target lift in Marketplace GMV.
What a strong answer includes
- Ties the decision to a single metric, for example a 3 percent lift in Marketplace transactions per newsfeed session (assumption).
- Flags cannibalization risk: ads and organic posts already compete for feed slots.
- Proposes an A/B test with a holdout before committing full engineering resources.
- Separates cold start (no purchase history) users from users with browsing signal.
Common mistakes
- Jumping straight to UI mocks instead of the build decision.
- Ignoring the feed real estate tradeoff against ads and Stories.
- No mention of measuring success before recommending build.
Likely follow-up questions
- How would you rank this against other feed features competing for the same slot?
- What signals would you use to personalize the recommendations?
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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 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop