Metrics question
What is the North Star metric for a campaign to incentivize dormant users with a $10 credit?
- Uber
- Metrics
- Medium
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What this question tests
Ability to define a single, well-chosen metric appropriate to a specific, time-boxed reactivation campaign rather than a generic growth metric.
How to approach it
- Clarify the campaign's goal: win back dormant users, those who haven't ordered in some defined period, using a credit incentive.
- Consider candidate metrics: credit redemption rate, one-time reactivation, and repeat usage after the credit is used.
- Rule out simple redemption rate alone, since it only measures people claiming the credit, not genuine reactivation.
- Propose the North Star: share of dormant users who complete a second, unincentivized order within 60 days of redeeming the credit.
- Explain why this matters: the real business goal is restoring a habit, not just generating one subsidized ride.
- Note a guardrail: cost per genuinely reactivated user, to ensure the campaign is economically worthwhile.
What a strong answer includes
- Explicitly rules out a superficial metric, redemption rate, and explains why it fails to capture the campaign's real intent.
- Proposes a North Star tied to durable behavior change, a meaningfully higher bar than simple credit usage.
- Adds a cost-efficiency guardrail, connecting the metric to the campaign's actual return on investment.
- Shows clear reasoning for why the chosen metric reflects genuine reactivation rather than a one-off subsidized transaction.
Common mistakes
- Picking redemption rate as the North Star, which only measures claiming a discount, not real reactivation.
- Not defining a time window for what counts as successful reactivation.
- Ignoring the cost side of the campaign entirely.
Likely follow-up questions
- How would you define dormant precisely for this campaign?
- How would you decide if the credit amount is the right incentive size?
- What would you do if redemption was high but repeat usage stayed low?
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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