Strategy question

How would you design surge pricing for Uber or Lyft?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to design a pricing mechanism that balances marketplace supply and demand while managing rider trust.

State the goal: use price to pull more drivers online and ration limited supply during demand spikes, not just to raise revenue.
Define the trigger: a real-time ratio of open ride requests to available drivers in a geofenced zone.
Design the multiplier curve, capping surge at a level that avoids fare shock, and decide how granular the geofence should be.
Address trust: show riders the surge multiplier before they request, and consider a fare estimate lock so it doesn't rise mid-request.
Consider driver incentives: does surge pay pull drivers from adjacent zones fast enough, or does it need extra notifications.
Define success metrics and guardrails, like wait time, completion rate, and complaint rate, alongside revenue per ride.

Ties the multiplier directly to a measurable supply-demand ratio rather than a fixed schedule, so it responds to real conditions.
Addresses the fairness backlash risk, citing caps during emergencies as a real constraint Uber and Lyft both apply.
Gives an illustrative example: a 1.5x multiplier at a 3-to-1 request-to-driver ratio, clearly marked as an assumption.
Weighs rider retention against short-term revenue, arguing surge should shrink wait time first, revenue second.

Designing surge purely as a revenue lever without addressing rider trust and fairness perception.
Ignoring driver-side incentives and assuming supply reacts instantly to price.

Chapters of the AI PM course that teach what this question tests.