Strategy question
How would you decide between showing more ads on the Facebook Newsfeed vs showing a "People you may know" recommendation widget?
- Meta
- Strategy
- Hard
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What this question tests
Tests trade-off decision-making between two revenue/engagement levers competing for the same feed real estate, requiring a framework, not just a gut preference.
How to approach it
- Clarify the constraint: both features compete for limited feed inventory (screen real estate and user attention), so this is a prioritization, not an additive decision.
- Define what each option optimizes: more ads drives short-term ad revenue directly; 'People You May Know' drives network growth and long-term engagement, which indirectly drives more ad inventory later.
- Propose a framework: model each option's expected value using a simple formula, e.g., (revenue or engagement lift per impression) x (impressions allocated), and compare short-term revenue against long-term LTV impact.
- Segment by user lifecycle: prioritize 'People You May Know' for newer users with small networks (where it drives retention-critical connections), and prioritize ads for established, highly-engaged users whose network is already mature.
- Propose testing rather than a single global choice: A/B test the allocation ratio by user segment instead of a blanket policy, since the optimal mix likely differs across user types.
- Define success: track both immediate ad revenue and downstream network growth/retention to ensure the chosen mix doesn't sacrifice long-term health for short-term revenue.
What a strong answer includes
- Frames the decision as an allocation/portfolio problem (how much of each, for whom) rather than a binary either-or choice.
- Segments the decision by user lifecycle stage (new vs mature users), recognizing that the right trade-off differs by user type.
- Explicitly connects network growth to long-term ad inventory, showing the two options aren't purely opposed, one feeds the other.
- Proposes a testable framework (expected value formula, A/B by segment) instead of a purely qualitative judgment call.
Common mistakes
- Treating it as a simple binary choice (ads vs recommendations) without considering a blended, segmented allocation.
- Ignoring that user growth widgets indirectly support long-term ad revenue by growing the network.
- No proposed way to test or measure the trade-off, just an opinion.
Likely follow-up questions
- How would you decide the exact ratio of feed slots between the two?
- How would this differ for a user with very few connections versus a power user?
- What guardrail would you set to avoid over-indexing on short-term ad revenue?
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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