Metrics question
Facebook is launching a new headset with smart capabilities. How do you measure its success?
- Meta
- Metrics
- Hard
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
Metrics for a new hardware-software product: choosing meaningful early indicators of adoption and satisfaction.
How to approach it
- Clarify the goal: is success adoption, engagement, or long-term retention for this new headset, and pick primary focus.
- Define the north star, such as weekly active hours spent in smart-capable experiences.
- Break into a funnel: units sold, activated, used weekly, and retained after 90 days.
- Add quality guardrails, like return rate, comfort complaints, and motion-sickness reports.
- Note leading indicators, such as app or content installs per device, as early signal before retention data exists.
What a strong answer includes
- Distinguishes hardware metrics (units sold, return rate) from software engagement metrics (weekly usage hours).
- Adds a guardrail specific to headsets, like reported discomfort or motion sickness, showing category awareness.
- Uses assumed benchmarks, e.g. targeting under 10 percent 90-day return rate, to ground the answer.
- Proposes a leading indicator, like content downloads in week one, since long-term retention data takes months to gather.
Common mistakes
- Only tracking units sold, ignoring actual usage after purchase.
- Ignoring comfort and health-related guardrail metrics unique to headsets.
- Not distinguishing early leading indicators from long-term lagging metrics.
Likely follow-up questions
- What would you do if units sold is high but weekly usage is low?
- How would you measure comfort or health complaints systematically?
- What's a good early signal before you have 90-day retention data?
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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