Root cause a sudden decline in retention among couriers using Uber Eats at global scale

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 Eats has observed a sudden decline in courier retention across its global delivery marketplace. As the PM investigating the issue, you need to frame the anomaly clearly, determine whether the decline is real or measurement-driven, and identify the most likely root causes affecting couriers’ willingness or ability to continue delivering.

Couriers are a critical side of the Uber Eats marketplace: their retention affects delivery reliability, consumer experience, merchant outcomes, marketplace liquidity, and unit economics. The issue may vary by geography, courier type, tenure, earnings profile, delivery mode, app version, incentive exposure, local regulations, competitive pressure, or operational changes.

Your task is not to propose a broad retention strategy upfront, but to structure a rigorous root-cause investigation. You should define what “retention” means, isolate where and when the decline occurred, generate hypotheses, identify evidence needed to validate or reject them, and outline immediate mitigations and longer-term prevention mechanisms.

The experience should consider:

- How courier retention is defined, including denominator, time window, activity threshold, and whether it measures supply availability, completed trips, or continued engagement.

- Whether the anomaly is global or concentrated by region, city, courier cohort, delivery mode, tenure, earnings band, acquisition channel, or app/platform version.

- Instrumentation and data-quality checks, including event logging, identity matching, courier status changes, deactivations, churn labeling, and reporting pipeline changes.

- Marketplace and economic drivers such as pay, incentives, demand volatility, batching, wait times, cancellations, tips, fuel costs, and courier utilization.

- Operational and product changes that may have impacted courier experience, including dispatch logic, app flows, onboarding, compliance checks, support, safety features, or account restrictions.

- External factors such as seasonality, weather, holidays, regulation, labor actions, macroeconomic shifts, or competitor campaigns from delivery and gig-work platforms.

- Evidence required to prioritize hypotheses, including cohort analysis, funnel diagnostics, courier feedback, support contacts, experimentation history, and city-level operational signals.

- Mitigation and prevention plans, including how to stabilize affected markets, monitor leading indicators, communicate with local teams, and avoid recurrence.

The goal is to demonstrate how you would lead a high-stakes RCA at Uber scale: separating signal from noise, narrowing a complex global problem into testable segments, balancing courier experience with marketplace health, and producing an evidence-based path toward containment and durable resolution.