How would you drive product growth?
Quick Overview
This question evaluates product growth analytics competencies including experimentation design, funnel decomposition, metric definition and guardrails, segmentation, and causal inference within the Analytics & Experimentation domain for social media and mobile product features.
Assume you are interviewing for a Product Growth Analyst role at Meta. Answer the following product growth and analytics cases. For each case, clarify the goal, define the primary metric and guardrails, break the problem into a funnel, identify what data you would inspect first, propose hypotheses, prioritize one intervention, and explain how you would validate impact.
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Improve Instagram Stories viewer engagement.
- How would you improve the Instagram Stories viewing experience?
- What data would you examine first?
- What product ideas would you generate?
- Pick one idea and explain how you would test it.
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Double Boosted Posts MAU in 6 months.
- Assume Boosted Posts MAU is defined as the number of distinct users or businesses that launch at least one boosted post in a calendar month.
- How would you determine whether doubling MAU in 6 months is realistic?
- Which levers would you target across acquisition, activation, retention, and resurrection?
- What trade-offs and guardrails would matter?
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Grow the number of active Reels creators.
- Define what an active creator means.
- How would you diagnose supply-side bottlenecks in the creator funnel?
- What interventions would you prioritize to increase durable creator growth rather than only short-term posting spikes?
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Increase logged-in users on the mobile Facebook app.
- Logged-in user count on the mobile Facebook app is declining.
- Focus your analysis on the login funnel, session persistence, password reset, and authentication friction.
- How would you determine whether the issue is a measurement problem, a product bug, a security or anti-abuse trade-off, or a real behavioral change in users?
In all cases, discuss segmentation, possible confounding factors, and when you would use an A/B test versus a quasi-experimental or diagnostic approach.