Metrics question
For contributor engagement and retention across 500,000+ contributors in 100+ countries, what are the few core metrics you would instrument for activation, repeat participation, and churn? If weekly supply health suddenly dropped, how would you determine whether the root cause was demand mix, onboarding friction, pay, quality gating, or country-specific issues?
- Scale AI
- Metrics
- Hard
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What this question tests
Tests instrumenting a few core metrics for a massive global contributor base and diagnosing a sudden supply drop across five plausible root causes.
How to approach it
- Instrument three metrics: activation rate (percent completing a first paid task), repeat participation rate (active again within a window), and churn rate.
- Segment all three by country and task type from the start, since aggregate numbers would hide a localized problem in one region or skill category.
- If supply drops suddenly, first check demand mix: did available task volume shift in a way that reduced opportunities for a large segment.
- Next check onboarding friction, visible as a drop specifically in activation rate rather than repeat participation.
- Next check pay, visible as a drop concentrated in one skill or region rather than broadly.
- Last check quality gating and country-specific issues, like a payment disruption, both visible through segmented, not aggregate, data.
What a strong answer includes
- Picks three metrics, activation, repeat participation, and churn, that together tell a funnel story instead of one flat number.
- Segments every metric by country and task type from the start, since a global average would hide the localized problem this scenario describes.
- Orders the root-cause check from most likely aggregate cause to most localized, using segmented data to disambiguate quickly.
Common mistakes
- Using only aggregate metrics that would completely hide a localized regional or skill-category problem.
- Guessing at a root cause without checking whether the drop is broad or concentrated in a specific segment first.
- Failing to instrument activation and repeat participation separately, conflating new- and existing-contributor problems.
Likely follow-up questions
- How would you decide which segment to investigate first if multiple show a drop?
- What would you do if all segments drop proportionally, suggesting a platform-wide cause?
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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