Metrics question
How will you decide lock-in time for a seat (before user makes the payment) on checkout page of an airline ticket app?
- Meta
- Metrics
- Hard
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What this question tests
Tests a precise product and analytical trade off: balancing conversion (giving users enough time to decide) against inventory efficiency (not holding seats too long) using data-driven reasoning.
How to approach it
- Clarify the trade off: too short a lock-in causes checkout abandonment from time pressure; too long ties up inventory that could sell to another buyer, especially on high-demand flights.
- Propose a data-driven starting point: analyze historical checkout completion times to find a duration covering a high percentile, such as 90 percent of completions, without excessive holding.
- Segment by context: last-minute or high-demand flights likely need shorter lock windows than off-peak flights with ample remaining inventory.
- Propose a dynamic approach: adjust lock-in time based on remaining seat inventory and demand in real time, rather than a single fixed duration for every flight.
- Add a safeguard: a visible countdown timer during checkout so users are aware of the constraint and can act with urgency rather than being silently timed out.
- Define success: checkout completion rate balanced against lost-sale rate from seats held too long, tracked together as a single trade off, not separately.
What a strong answer includes
- Frames this explicitly as a two-sided trade off, buyer conversion versus inventory efficiency, rather than treating it as a single, isolated UX decision.
- Proposes a dynamic, demand-based lock-in duration instead of one fixed number for every flight, reflecting that high-demand and low-demand flights have very different costs of holding a seat.
- Uses historical checkout completion time data as the empirical basis for the decision, rather than guessing a round number like 10 minutes.
- Adds a visible countdown as a UX safeguard, since silently expiring a hold would create a worse experience than a time-pressured but transparent one.
Common mistakes
- Picking an arbitrary fixed lock-in time with no data or demand-based reasoning behind it.
- Optimizing only for conversion (long hold times) without accounting for the real cost of lost sales on high-demand flights.
Likely follow-up questions
- How would you handle a user who is mid-checkout when their held seat's lock-in is about to expire?
- How would you measure the actual revenue impact of getting this trade off wrong in either direction?
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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