Metrics question
How would you measure customer satisfaction with an AI support agent?
- Sierra
- Metrics
- Medium
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Tests metrics design for a support product where a resolved ticket is not the same as a satisfied customer.
How to approach it
- Start from the goal: customers get their issue resolved quickly and correctly without feeling like they talked to a wall.
- Build a metric tree: top line is CSAT or a post interaction survey score, driven by resolution rate, resolution accuracy, and time to resolution.
- Add guardrail metrics that catch a bot gaming satisfaction, like escalation rate to a human and repeat contact rate on the same issue within 48 hours.
- Consider measurement methods beyond a survey, like sentiment analysis on the conversation transcript itself, since survey response rates are often low.
- Segment by channel and issue type, since satisfaction with voice resolution differs from satisfaction with a chat deflection.
- Confirm with the interviewer whether they want a single north star or a full dashboard, since that changes how much to build out.
What a strong answer includes
- Explicitly separates resolution rate, a bot side metric, from CSAT, a customer perception metric, since a bot can close tickets without customers feeling helped.
- Proposes repeat contact rate within a short window as a guardrail against false resolutions the bot marks closed prematurely.
- Suggests transcript based sentiment as a denser signal than survey response, which usually has low completion rates.
- Ties the metric to Sierra's outcome based pricing, noting that satisfaction should factor into which resolutions the client actually pays for.
Common mistakes
- Treating ticket closure or deflection rate as the same thing as customer satisfaction.
- Relying only on a post chat survey without accounting for low response rates skewing the number.
- No guardrail metric, so a bot could optimize for closing tickets fast while frustrating customers.
Likely follow-up questions
- How would you catch a bot that closes tickets without actually resolving the issue?
- How would you weight voice satisfaction versus chat satisfaction differently?
- What would you do if CSAT scores dropped after a model upgrade?
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More questions from Sierra
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