Handling Evidence That Your Uber Driver-Incentive Change Could Reduce Supply

You have recommended a change to the incentives Uber offers its drivers. New data suggests the change could reduce driver supply. How would you proceed? ```hint Pin down "hurt supply" Ask which drivers, cities and hours the data says would be affected, by how much, and how far you trust that evidence, before deciding what happens to your proposal. ``` ### Clarifying Questions - What change did I propose, and what was it meant to achieve: lower incentive spend, better coverage at peak hours, fairer pay, or something else? - What data points to lower supply: a forecast model, a driver survey, a small pilot, or a similar change made in the past? - Does "supply" here mean drivers going online, total online hours, or hours in the places and times riders need them? - Who signs off on the change, and is a deadline such as a budget cycle driving it? ### What a Strong Answer Covers - Revisiting the proposal's goal and treating the supply signal as evidence to weigh, not an objection to defend against - Judging the data's quality and sizing the supply effect by driver segment, market and time of day, translated into rider outcomes such as pickup times and completed trips - Options beyond ship or kill, such as redesigning, targeting, phasing or testing with clear stopping rules - A marketplace-aware test design with the metrics and guardrails that decide the outcome - Communicating the trade-off and an updated recommendation to stakeholders and to drivers ### Follow-up Questions - A pilot shows total driver hours flat but fewer drivers online late at night. What do you recommend? - Finance wants the savings the change was meant to deliver even if supply dips slightly. How do you frame that trade-off for leadership? - After launch, how would you detect supply eroding before rider pickup times reveal it?