Diagnose a 20 percent drop in activation for Rides

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 has observed a 20% drop in rider activation. For this exercise, activation refers to the point at which a rider successfully completes the intended early journey milestone for Rides, such as taking a first trip after signup or re-engaging into a completed ride after entering the funnel. Your task is to diagnose the issue as a product manager responsible for understanding what changed, where it changed, and what actions should follow.

Assume this is a live marketplace environment with riders, drivers, pricing, payments, app experience, local operations, and competitive dynamics all potentially influencing the outcome. The drop may be caused by a real user behavior change, a measurement issue, an operational constraint, or a combination of factors.

You should frame the investigation clearly before jumping into causes. Define the activation metric, establish the baseline and time window, identify whether the decline is sudden or gradual, and determine which rider cohorts, geographies, platforms, acquisition channels, and funnel steps are most affected.

The investigation should consider:

- Whether the activation drop is real or caused by instrumentation, logging, attribution, data pipeline, or definition changes.

- Funnel breakdown from app install/signup through location permission, destination entry, fare estimate, ride request, driver match, pickup, and completed trip.

- Segmentation by city, country, platform, app version, new versus returning riders, acquisition source, payment method, rider intent, and time of day.

- Marketplace factors such as driver supply, ETAs, surge pricing, cancellations, airport/event demand, weather, safety incidents, or local regulatory changes.

- Product or growth changes including onboarding flows, promotions, pricing displays, payment failures, identity verification, notifications, or experimentation rollouts.

- External factors such as competitor promotions, local taxi availability, public transit disruptions, macroeconomic pressure, or seasonality.

- Evidence needed to prioritize hypotheses, including metric trends, experiment logs, release timelines, support tickets, rider feedback, and operational dashboards.

- Immediate mitigations, longer-term fixes, prevention mechanisms, and monitoring to confirm recovery without harming reliability, safety, or unit economics.

The goal is to demonstrate a structured RCA approach that separates measurement noise from real marketplace or product issues, narrows the problem to the most affected segments, identifies likely root causes through evidence, and outlines how Uber should respond while protecting rider trust and marketplace health.