Root cause a sudden decline in retention among couriers using Reservations

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 has observed a sudden decline in retention among couriers who use Reservations, a feature that lets couriers plan ahead for scheduled earning opportunities instead of relying only on real-time demand. The issue appears after a recent period of normal performance and is specific enough that leadership wants to understand whether it reflects a product experience problem, marketplace imbalance, operational change, measurement issue, or external market shift.

Your task is to structure a root-cause analysis for this retention drop. Focus on how couriers discover, accept, prepare for, complete, and evaluate reserved opportunities, and where friction or changed expectations could cause them to stop using Reservations or churn from courier activity more broadly.

This is an RCA interview question, so the emphasis is on framing the anomaly, validating the data, segmenting the decline, forming hypotheses, identifying evidence needed, and proposing mitigation and prevention paths without jumping directly to a solution.

The experience should consider:

- How “retention among couriers using Reservations” is defined, including the denominator, return window, and whether it measures Reservation reuse or overall courier activity.

- Whether the decline is global or concentrated by city, courier tenure, vehicle type, delivery category, marketplace density, or app version.

- Instrumentation checks for reservation views, accepts, cancellations, no-shows, completed trips, earnings, notifications, and eligibility status.

- Product workflow points where couriers may face friction, such as unclear pickup timing, low availability, poor earnings visibility, cancellation penalties, or assignment changes.

- Marketplace and operational factors such as demand volatility, merchant reliability, travel distance, batching, courier supply, weather, holidays, or local policy changes.

- Recent launches, pricing changes, incentive changes, notification changes, ranking logic, eligibility rules, or support-policy updates that could affect courier trust.

- Competitive and external alternatives, including whether couriers may be shifting to other gig platforms or non-reserved earning modes.

- Mitigation, monitoring, and prevention steps that balance courier reliability, customer experience, marketplace liquidity, safety, and unit economics.

The goal is to present a clear investigation plan that would help Uber determine what changed, who was affected, why retention declined, how urgently to respond, and what evidence would justify short-term fixes versus deeper product or marketplace changes.