How would you grow Meta products?

Q: How would you grow Meta products?

This question evaluates product growth analytics and experimentation skills, including metric definition, funnel decomposition, segmentation, hypothesis generation, causal inference, and trade-off reasoning (e.g., cannibalization, creator-viewer marketplace effects, novelty effects, and selection bias).

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Q: What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

Q: What role is this question designed for?

This question is commonly asked for Product Analyst candidates at Meta during technical interviews.

Question

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You are interviewing for a Product Growth Analyst role at Meta. For each of the following cases, explain how you would: (i) define the primary metric and guardrails, (ii) break the problem into a funnel or growth equation, (iii) identify the most useful data cuts, (iv) generate hypotheses, and (v) design an experiment or causal analysis. Discuss tradeoffs such as cannibalization, creator-viewer marketplace effects, novelty effects, and selection bias.

Instagram Story Viewer: usage of the story-viewing surface has plateaued. How would you improve story viewer engagement? What data would you inspect first? Propose several ideas, then pick one and explain how you would test it.
Boosted Post MAU: Meta wants to double boosted-post monthly active users in 6 months. Define boosted-post MAU precisely, decompose the target into acquisition, activation, retention, and resurrection levers, and explain how you would prioritize interventions.
Reels Creator Growth: Meta wants to increase the number of active Reels creators. How would you diagnose supply-side bottlenecks, segment users, and recommend product or ranking changes without hurting viewer quality?
Mobile Facebook Logged-in Users: the company wants to increase logged-in users on the mobile Facebook app, especially by improving the login and password-reset flow. How would you analyze the funnel, identify friction, and evaluate proposed fixes?

Assume you have event-level product logs, user attributes, device and country dimensions, and the ability to run A/B tests, although some changes may require observational methods because of security or platform constraints.

How would you grow Meta products?

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