Market Sizing: Monthly Shared E-Scooter Trip Demand in San Francisco

Quick Overview

A product manager estimation question asking how many shared e-scooter rental rides San Francisco could generate each month. It tests building a sizing equation, stating round assumptions about trips, riders and local conditions, doing the arithmetic correctly and cross-checking demand against fleet supply.

Market Sizing: Monthly Shared E-Scooter Trip Demand in San Francisco

Company: Uber

Role: Product Manager

Category: Product / Decision Making

Difficulty: medium

Interview Round: Other

Estimate how many shared e-scooter rental rides San Francisco could produce in a month. Walk through your assumptions and arithmetic, and check that the result is reasonable. ```hint Two roads to one number Build the estimate from one driver, such as the trips people already take, then rebuild it from another, such as riders and how often they ride, or scooters and how often each one is used. See whether they agree. ``` ### Clarifying Questions - Does "potential" mean the demand the city could support once the market is mature, or the rides taken today with the fleet the city currently permits? - Should rides by visitors and by people who commute in from outside the city count, or only rides by residents? - Is this the total across all scooter operators, or only rides booked through Uber's app? - Is the answer for an average month, or should it reflect the wetter winter months? ### What a Strong Answer Covers - A clear equation with its unit and the scope chosen - Round, labeled assumptions, each with a reason specific to San Francisco: density, hills, weather, visitors and who can ride - Correct arithmetic and a range around the point estimate - A cross-check by an independent method, including whether fleet supply rather than demand is the binding limit - Which assumption the answer is most sensitive to, and how Uber could validate it ### Follow-up Questions - Which single assumption moves your answer the most, and what data in Uber's app could test it? - If the city capped the fleet well below what your demand estimate requires, how would that change the number and the business decision? - How would you adapt the same model for a smaller, flatter city?

Overview: A product manager estimation question asking how many shared e-scooter rental rides San Francisco could generate each month. It tests building a sizing equation, stating round assumptions about trips, riders and local conditions, doing the arithmetic correctly and cross-checking demand against fleet supply.

Uber logo
Uber
Oct 29, 2024
mediumProduct ManagerOtherProduct / Decision Making
0
0

Estimate how many shared e-scooter rental rides San Francisco could produce in a month. Walk through your assumptions and arithmetic, and check that the result is reasonable.

Clarifying Questions Guidance

  • Does "potential" mean the demand the city could support once the market is mature, or the rides taken today with the fleet the city currently permits?
  • Should rides by visitors and by people who commute in from outside the city count, or only rides by residents?
  • Is this the total across all scooter operators, or only rides booked through Uber's app?
  • Is the answer for an average month, or should it reflect the wetter winter months?

What a Strong Answer Covers Guidance

  • A clear equation with its unit and the scope chosen
  • Round, labeled assumptions, each with a reason specific to San Francisco: density, hills, weather, visitors and who can ride
  • Correct arithmetic and a range around the point estimate
  • A cross-check by an independent method, including whether fleet supply rather than demand is the binding limit
  • Which assumption the answer is most sensitive to, and how Uber could validate it

Follow-up Questions Guidance

  • Which single assumption moves your answer the most, and what data in Uber's app could test it?
  • If the city capped the fleet well below what your demand estimate requires, how would that change the number and the business decision?
  • How would you adapt the same model for a smaller, flatter city?