Strategy question
How would you decide to display ads on the Facebook Newsfeed? After every 100 posts or 25 posts? Evaluate tradeoffs.
- Meta
- Strategy
- Hard
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What this question tests
Tests structured tradeoff reasoning between ad revenue density and user experience/retention on a core surface.
How to approach it
- Clarify the goal: is this a revenue-maximization decision or a balanced one that protects long-term engagement, since that changes the answer.
- Lay out the tradeoff: ads every 25 posts raises impression frequency and near-term revenue but risks scroll fatigue and lower session length; every 100 posts protects experience but caps monetization.
- Propose testing a range (25, 50, 75, 100) rather than picking one a priori, measuring revenue per session against session length and day-7 retention.
- Segment by user type: heavy scrollers may tolerate denser ads better than light, high-value users who churn faster from clutter.
- Recommend a dynamic cadence driven by session engagement signals instead of a fixed number, then state the guardrail: retention and complaint rate can't regress beyond a set threshold.
What a strong answer includes
- Treats this as an experiment design problem, not a debate, by proposing a multi-arm test instead of arguing for one fixed number from intuition.
- Uses an illustrative tradeoff, e.g. assume 25-post cadence lifts ad revenue 15% but cuts session length 5%, to show how you'd weigh the net.
- Recognizes that ad load tolerance differs by user segment and device, so a single global cadence is a simplification worth challenging.
- Ties the decision back to a long-term metric (DAU/MAU stickiness) rather than optimizing only for quarterly ad revenue.
Common mistakes
- Picking a number without proposing how you would validate it with data.
- Ignoring the retention and complaint-rate risk and treating this purely as a revenue optimization.
Likely follow-up questions
- How would you personalize ad cadence per user instead of using one global number?
- What guardrail metric would auto-stop the rollout if it moved too far?
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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