Metrics question
Meta is expanding into gaming. What KPIs would you track to measure success?
- Meta
- Metrics
- Medium
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Metric tree design for a new business line, connecting engagement and monetization goals to specific trackable KPIs.
How to approach it
- Clarify what gaming means here, for example cloud gaming, Instant Games, or VR gaming through Quest.
- State the north star, such as monthly active gamers or time spent in game surfaces.
- Break down a metric tree: discovery, game starts from feed or store, engagement, session length and retention day 7 and day 30, and monetization, in app purchases and ad revenue.
- Add guardrails, like complaints about performance or battery drain on mobile.
- Prioritize which metric the team is accountable for in the first two quarters, likely retention over monetization early on.
What a strong answer includes
- Builds a full funnel from discovery to retention to monetization instead of naming isolated metrics.
- Chooses day 7 and day 30 retention as an early priority over revenue, since gaming products need habit formation first.
- Adds a concrete guardrail like crash rate or negative reviews tied to performance.
Common mistakes
- Jumping straight to revenue metrics before the product has proven retention.
- Ignoring platform specific guardrails like device performance.
Likely follow-up questions
- How would these KPIs differ between mobile and VR gaming?
- What would make you recommend Meta stop investing in this?
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- You launched a new signup flow to encourage new users to add more profile information. A/B test results indicate that the % of people that added more information increased by 8%. However, 7 day retention decreased by 2%. What do you do?Google · Metrics · Hard
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More questions from Meta
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