Product design question
Air travel CSAT is very low. How would you improve it?
- Product design
- Hard
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What this question tests
Tests diagnostic and design thinking on a broad, multi-touchpoint experience problem: can you break a vague low-CSAT signal into specific, addressable journey stages.
How to approach it
- Clarify what 'air travel CSAT' covers: booking, check-in, security, boarding, in-flight, baggage claim, and disruptions, since 'low CSAT' could stem from any stage and the fix differs by stage.
- Pull data to localize the problem: segment CSAT scores by journey stage and by trip type (delayed vs. on-time) to find where dissatisfaction concentrates.
- Form a hypothesis on the likely biggest driver: disruptions (delays, cancellations) usually dominate low CSAT far more than routine touchpoints, based on common industry research.
- Design around the likely top driver: proactive rebooking and real-time notifications during disruptions, reducing the uncertainty and wasted time that drives the worst satisfaction scores.
- Propose a secondary improvement: streamlining the highest-friction routine touchpoint (often security or baggage claim) based on what the data shows.
- Define success as CSAT specifically for disrupted trips improving, plus overall CSAT trend, since fixing the worst-case scenario usually moves the average the most.
What a strong answer includes
- Narrows a vague, company-spanning prompt to a specific, testable hypothesis (disruptions drive most dissatisfaction) instead of trying to fix everything at once.
- Uses journey-stage segmentation as the analytical tool to localize the problem before proposing any fix.
- Proposes a concrete mechanism, proactive rebooking with real-time push notifications, which is specific rather than generic 'improve communication'.
- Ties the metric back to the segment most likely responsible (disrupted-trip CSAT) rather than only tracking an aggregate score that could mask where the real problem is.
Common mistakes
- Trying to address the entire travel journey at once instead of localizing where dissatisfaction actually concentrates.
- No data-driven hypothesis, jumping straight to a fix without diagnosing the stage first.
- Ignoring that this spans multiple companies (airlines, airports) and the PM's actual scope of control, without naming which company's product this is.
Likely follow-up questions
- Which company or product surface are you assuming ownership of here?
- How would you validate that disruptions are really the biggest CSAT driver?
- What would you do if the fix improved CSAT but increased operational cost significantly?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop