Practice — Case Study: Dynamic Pricing for a Marketplace (5 questions)

A ride-hailing company wants to know whether a new surge policy raises profit per session by at least 2.5 percent. The city has 8 pricing zones, about 95,000 sessions per day, and drivers who take roughly an hour to respond to a price change. The product manager proposes randomizing 50 percent of riders to the new policy for a week.

1. Explain why the rider-level test is the wrong design here, in terms of what it would measure.
2. Specify a switchback: the randomization unit, block length, burn-in, assignment scheme, analysis estimator and standard error, and what you would run beforehand to check the design.
3. Estimate the duration, and explain why a simple per-arm sample-size calculation would be too optimistic.

Your team proposes to train a surge-pricing policy that maximizes platform margin per session. You are asked to define the objective and the constraints before any model is built.

1. Why is margin alone a dangerous reward for a marketplace?
2. Write a reward that includes liquidity, say how you would choose its weight, and say which requirements belong in the reward and which are constraints.
3. Name three constraints and how each is enforced in production.