Define success metrics for Rides serving riders

Problem Statement Description

Product context: Uber is a mobility and delivery platform; its products include rides, Uber Eats, grocery and retail delivery, freight, driver and courier tools, and marketplace pricing.

Uber Rides is a two-sided mobility marketplace where riders open the app, compare available options, request a trip, wait for pickup, complete the ride, and decide whether to use the service again. In this interview, you are asked to define what “success” means for the rider side of the Rides experience and how Uber should measure it.

Focus on the rider journey rather than driver-side optimization alone. Consider moments of friction such as app discovery, fare and ETA confidence, ride availability, matching, cancellations, pickup experience, trip quality, safety, payments, support, and repeat usage. Your metrics should help Uber understand whether riders are getting a reliable, safe, affordable, and convenient transportation experience across different markets and trip types.

This is a metrics question, so the expected scope is not to redesign the product or propose growth tactics. The emphasis is on choosing useful metrics, defining them clearly, explaining what each metric captures, identifying denominators and cohorts, and discussing how Uber would instrument and interpret them for decision-making.

The experience should consider:

- Clear definition of the rider population being measured, such as active riders, new riders, repeat riders, or riders who attempted to request a trip.

- Funnel coverage from app open/search through request, match, pickup, trip completion, payment, support, and retention.

- Metric denominators and edge cases, including failed requests, rider cancellations, driver cancellations, surge periods, unavailable supply, and multi-modal ride options.

- Cohorts and segmentation by city, time of day, airport vs non-airport trips, new vs existing riders, price-sensitive riders, and marketplace supply conditions.

- Instrumentation needed across client events, marketplace matching systems, payments, safety incidents, support contacts, and post-trip feedback.

- Guardrail metrics for safety, reliability, affordability perception, driver marketplace health, fraud/abuse, and unit economics.

- How metrics would be used operationally, such as diagnosing city-level issues, evaluating product changes, monitoring launches, and comparing rider experience over time.

The goal is to present a concise but complete metrics framework that would allow Uber to judge whether Rides is serving riders well, detect meaningful experience problems, and make informed product and marketplace decisions without over-optimizing for a single number.