Product design question
How would you improve search on Walmart’s ecommerce platform?
- Walmart
- Product design
- Easy
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What this question tests
Product improvement in a core, high traffic surface, identifying real search friction and proposing a measurable fix.
How to approach it
- Identify a specific search friction point, such as irrelevant results when searching generic terms across Walmart's massive catalog.
- Prioritize the highest impact issue, likely relevance ranking, since it affects nearly every shopping session.
- Propose a concrete improvement, like better query understanding for common misspellings and synonyms, plus stronger in stock and availability weighting.
- Explain why this matters for conversion, since irrelevant top results drive users to abandon or search elsewhere.
- Define a success metric, such as improved search to purchase conversion rate or reduced zero result search rate.
What a strong answer includes
- Names a specific, common issue, poor relevance ranking or handling of misspellings, rather than a vague improve search answer.
- Ties the fix to conversion impact, since search is often the first step of a purchase journey on Walmart.com.
- Names concrete metrics like zero result rate and search to purchase conversion to measure the fix.
Common mistakes
- Proposing UI changes like search bar styling instead of addressing actual relevance and ranking quality.
- Ignoring in stock availability as a factor that hurts search satisfaction even when results are technically relevant.
Likely follow-up questions
- How would you handle balancing sponsored results with organic relevance?
- How would you measure search quality beyond click through rate?
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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 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop