Metrics question
Recurring support issues show up across integrations with tools like ServiceNow, Zendesk, and GitHub: OAuth misconfigurations, confusing 403/429 errors, and webhook retry failures. How would you use support tickets, forum threads, logs, and telemetry to distinguish documentation gaps from product design defects, and how would you turn that analysis into a roadmap?
- Glean
- Metrics
- Hard
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What this question tests
Tests using support and telemetry data to separate documentation gaps from real product design defects, and turning that into a prioritized roadmap.
How to approach it
- Categorize each recurring issue by type, OAuth misconfiguration, confusing error codes, webhook retry failures, since each has a different likely root cause.
- Cross-reference support tickets with telemetry: if many users hit the same error code but few file tickets, it is likely a documentation gap users are self-solving badly, not a product break.
- If telemetry shows the error rate itself is high regardless of documentation quality, for example a genuinely ambiguous 403 that should be a 401, treat it as a product design defect.
- For webhook retry failures, check whether the retry logic itself is undocumented versus actually insufficient, for example too few retry attempts on 5xx errors.
- Rank fixes by combined ticket volume and telemetry error rate, prioritizing product fixes first since they compound across every developer, docs fixes second.
- Ship the two or three highest-impact fixes and monitor whether ticket volume for that category actually drops post-fix.
What a strong answer includes
- Uses the ticket-versus-telemetry cross-reference specifically to tell documentation gaps apart from real defects, not just reading ticket text.
- Gives a concrete example, a 403 that should really be a 401, showing genuine product-defect diagnosis.
- Prioritizes product fixes over documentation fixes since they compound for every developer hitting the same code path.
Common mistakes
- Treating all recurring issues as documentation problems solvable by writing more docs.
- Prioritizing fixes by ticket volume alone without checking underlying telemetry error rates.
Likely follow-up questions
- How would you validate that a fix actually worked after shipping it?
- What would you do if a fix for one integration surfaced a new issue in another?
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More questions from Glean
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