Metrics question
What's the north star metric for Google Calendar?
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What this question tests
Ability to identify a single north star metric that captures the core value of a utility scheduling product.
How to approach it
- Clarify the core value: the calendar helps users manage and follow through on scheduled commitments, alone or with others.
- Consider candidate metrics: events created, events attended, weekly active users, and time saved scheduling.
- Rule out events created alone, since it doesn't capture whether the calendar actually helped someone follow through.
- Propose the north star: weekly active users who both create and attend a scheduled event, capturing genuine reliance on the tool.
- Explain supporting metrics: invite acceptance rate and meeting-scheduling time, which feed into the north star.
- Note the guardrail: notification and reminder effectiveness, since a calendar people ignore isn't truly useful.
What a strong answer includes
- Explicitly rules out a superficial candidate metric and explains why it fails to capture real value.
- Proposes a north star tied to actual follow-through, not just creation, a meaningfully better bar.
- Names supporting metrics that causally feed the north star, showing a coherent metric tree.
- Adds a guardrail around reminder effectiveness, a real usability lever for a calendar product.
Common mistakes
- Picking a vanity metric without questioning if it reflects real value.
- Not connecting supporting metrics logically to the chosen north star.
- Ignoring the multiplayer aspect of calendar use.
Likely follow-up questions
- How would you weigh personal events versus shared meeting events in this metric?
- How would you detect if users were scheduling but not really using the calendar as intended?
- How would this north star differ for enterprise versus consumer users?
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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