Strategy question
Should Google build a shopping recommendation?
- Strategy
- Medium
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What this question tests
Strategic judgment on build-versus-stay-out decisions, weighing Google's existing assets like Shopping Graph and Search against a new standalone recommendation product.
How to approach it
- Clarify scope: a shopping recommendation feature inside Search and Shopping tabs, not a separate app, since Google already has shopping ad infrastructure.
- State Google's existing advantage: massive query and shopping ad data already power Google Shopping, so this may be an extension, not a new bet.
- Identify the user problem: shoppers see ranked ads but not personalized, intent-aware recommendations the way Amazon's homepage offers.
- Weigh the risk: recommendations blur the line between organic results and paid placement, which could hurt user trust in Search.
- Recommend building it as an opt-in personalized shopping feed within Google Shopping, kept clearly separate from ad-ranked search results.
- Define success as increased click-through and purchase-intent clicks from the feed without a drop in Search trust metrics.
What a strong answer includes
- Frames this as an extension of an existing asset, the Shopping Graph, rather than a net-new build, showing awareness of Google's real capabilities.
- Names the trust risk explicitly, since Search's core value is neutral results, and proposes a design that separates ads from recommendations.
- Ties success to a dual metric, engagement and trust, rather than engagement alone.
Common mistakes
- Ignoring that Google already has shopping ad infrastructure and treating this as a from-scratch build.
- Failing to address the trust risk of mixing recommendations with Google's neutral search reputation.
Likely follow-up questions
- How would you keep this from feeling like more ads to users?
- What data would you need to personalize recommendations without being invasive?
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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