Metrics question
A design-partner customer says adoption of Claude Tag on a newly launched surface spiked at launch and then stalled. How would you diagnose the problem, what metrics and segmentation would you examine, and how would you determine whether the root cause is onboarding, permissions friction, model behavior, or weak product-market fit for that surface?
- Anthropic
- Metrics
- Hard
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What this question tests
Tests diagnostic rigor for a spike then stall adoption pattern, distinguishing onboarding, permissions, model behavior, and product-market fit as root causes.
How to approach it
- Restate the pattern: a launch spike then a stall, pointing to a novelty effect rather than sustained value, needing segmentation to confirm why.
- Segment usage by team and role, since the stall could be concentrated in specific teams that hit a specific blocker.
- Check onboarding completion and repeat invocation separately, since a stall right after first use points to onboarding while a later stall points elsewhere.
- Check for permissions friction, such as users who tried to invoke it in a channel it lacked access to and silently gave up.
- Interview a sample of users who stopped, asking whether outputs were wrong, invocation was confusing, or the surface just was not a fit.
What a strong answer includes
- Treats the spike then stall shape itself as a diagnostic clue, pointing first toward novelty effect or a hard early blocker, not a slow organic decline.
- Separates onboarding friction from permissions friction from model quality with distinct, checkable signals for each rather than one vague usage metric.
- Interviews actual churned users directly instead of only inferring the cause from aggregate data.
- Considers product-market fit for that specific surface as a real possible conclusion, not assuming the fix is always a product tweak.
Common mistakes
- Assuming the cause is model quality without first checking simpler explanations like permissions friction or unclear invocation.
- Looking only at aggregate adoption numbers and missing that the stall is concentrated in one team or role.
Likely follow-up questions
- How would you distinguish a permissions problem from a genuine lack of fit?
- What would you do if the stall was concentrated in exactly the team that championed the launch?
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More questions from Anthropic
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