Metrics question
How would you assess the opportunity for showing "You may also like" block along with the existing ads block on Google Shopping?
- Metrics
- Hard
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What this question tests
Opportunity sizing for a new ad surface: can you estimate incremental value while weighing cannibalization and user experience risk.
How to approach it
- Define the opportunity: a 'You may also like' block adds discovery-driven impressions alongside existing intent-driven ads on Google Shopping.
- Estimate incremental engagement: this block could capture users who don't click the primary ad, converting otherwise-lost sessions into additional ad interactions.
- Weigh cannibalization: some clicks may shift from the existing ads block rather than being purely incremental, so net lift is smaller than gross clicks on the new block.
- Consider user experience risk: more ad surface area on a shopping results page could reduce perceived relevance or increase page clutter, hurting long-term trust.
- Propose a test: A/B test with the block live vs a holdout, measuring net revenue per session (not just new-block clicks) plus a user satisfaction guardrail.
- Define the decision rule: launch if net incremental revenue is positive and satisfaction/quality guardrails don't degrade beyond a set threshold.
What a strong answer includes
- Explicitly separates gross clicks on the new block from net incremental revenue, correctly flagging cannibalization as the key risk to size.
- Adds a user-experience guardrail (perceived relevance, page clutter) instead of treating this purely as a revenue-upside question.
- Proposes a proper A/B test with a holdout group as the way to actually measure the opportunity, not a top-down guess.
- States a clear decision rule for launch, tying the opportunity assessment to an actionable outcome.
Common mistakes
- Sizing the opportunity using raw clicks on the new block without accounting for cannibalization of the existing ads block.
- Ignoring the user-experience/quality risk of adding more ad surface to a results page.
Likely follow-up questions
- How would you isolate cannibalization from truly incremental revenue in your test design?
- What guardrail metric would stop you from launching even if revenue looked positive?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop