Metrics question
Facebook has 2 applications - Facebook Blue which is the normal app, and Facebook Lite which is a lighter version specially designed for geographies with lower internet bandwidths. It is seen that user retention on Facebook Lite is low compared to Facebook Blue. Investigate why.
- Meta
- Metrics
- Hard
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What this question tests
Diagnostic root-cause analysis using segmentation to avoid confounding a comparison between two very different user bases.
How to approach it
- Clarify what 'retention' means here, D7 or D30, and confirm the comparison controls for the same window and region.
- Segment by the obvious confounder: Lite users skew toward lower-bandwidth, lower-end-device markets, so compare within the same country and device tier.
- Break down the funnel: crashes or load failures, since Lite targets weak networks, feature-parity gaps versus Blue, and data-cost-driven usage throttling.
- Check device and OS-version distribution, since Lite often runs on older Android devices with different failure modes.
- Rank hypotheses: reliability first as most likely, then feature gaps, then cost.
- Propose validating by comparing crash rates and session-success rates in the same markets before concluding it's a feature gap.
What a strong answer includes
- Immediately flags the confound, different user base and geography, before jumping to feature explanations, the key insight interviewers want.
- Proposes a concrete, testable hypothesis order rather than a random list.
- Suggests a specific comparison method, same market and device tier, to isolate the real driver.
Common mistakes
- Comparing Lite versus Blue retention directly without controlling for very different user populations.
- Jumping straight to 'add more features to Lite' without diagnosing the actual cause first.
Likely follow-up questions
- How would you design an experiment to confirm your top hypothesis?
- What would you do if crash rate wasn't the cause?
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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