Metrics question
As a PM candidate, how would you measure if LinkedIn is better for iOS or android, given that the look and feel is same across both platforms?
- Metrics
- Hard
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What this question tests
Metrics thinking for cross platform parity: measuring quality difference when the surface UI looks identical on both platforms.
How to approach it
- Clarify the definition of 'better': performance, engagement, or platform-specific technical quality, since look and feel is explicitly ruled out.
- State the goal: even with identical UI, underlying performance and platform constraints can differ significantly.
- Build a metric tree covering: crash rate and app not responding rate per platform, load and page render times, and feature parity (features shipped to one platform first).
- Add engagement metrics per platform: session length, session frequency, and feature adoption rate, normalized by user demographics since iOS and Android skew differently.
- Control for confounders: iOS and Android user bases differ by region and income, so compare cohorts with similar demographics, not raw averages.
- Set a north star, such as day 30 retention by platform after controlling for these demographic differences.
What a strong answer includes
- Explicitly controls for the confound that iOS and Android users are demographically different populations, not a clean A/B comparison.
- Separates technical quality metrics (crash rate, latency) from behavioral ones (retention, engagement), since 'better' could mean either.
- Flags feature parity itself as a metric, since if Android always gets features weeks later, that alone explains a lot of any perceived gap.
Common mistakes
- Comparing raw engagement numbers across platforms without adjusting for different user demographics.
- Ignoring feature release timing differences as a confound.
Likely follow-up questions
- How would you control for the demographic differences between iOS and Android users?
- If Android showed worse retention, how would you determine if it is a platform issue or a user base difference?
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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