Debug a spike in complaints from families on Uber One

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 One is Uber’s membership product spanning rides and delivery benefits, and families may use it across multiple recurring needs: school commutes, airport trips, grocery orders, meal delivery, shared household accounts, and time-sensitive errands. Recently, complaints from family users on Uber One have spiked, and you are asked to diagnose what is happening.

In this RCA interview, assume the spike is meaningful enough to require cross-functional investigation, but the root cause is unknown. Complaints may be coming through support tickets, app feedback, social channels, refunds, cancellations, or account-management flows. Your task is to structure how you would investigate the issue, isolate whether it is a true customer-experience degradation or a measurement artifact, and determine what evidence would guide action.

You should consider the full Uber One family experience across membership enrollment, benefit discovery, ride and delivery usage, household coordination, billing, support, and renewal. The investigation should account for Uber’s marketplace constraints, including driver/courier availability, local operations, pricing, safety expectations, reliability, and the different ways families may experience failures compared with individual users.

The investigation should consider:

- How to frame the anomaly: complaint rate vs. complaint volume, baseline period, seasonality, geography, and whether the spike is specific to families on Uber One

- Segmentation by market, family size, account type, new vs. tenured members, ride vs. delivery usage, benefit type, platform, and acquisition channel

- Instrumentation checks to confirm whether complaint tagging, support routing, survey questions, app releases, or reporting pipelines changed

- Hypotheses across product experience, pricing or fees, benefit eligibility, shared account usage, delivery reliability, ride wait times, cancellations, safety concerns, billing, and support resolution

- Evidence needed from funnels, operational metrics, support transcripts, refund data, cancellation reasons, NPS/CSAT, app logs, and cohort behavior

- How to distinguish member expectation issues from actual service failures in the Uber marketplace

- Short-term mitigation options while the investigation is ongoing, including customer communication, support handling, and operational escalation

- Prevention mechanisms such as monitoring, alerting, ownership, and post-launch checks for membership or family-related changes

The goal is to demonstrate a structured RCA approach that can narrow a broad complaint spike into testable causes, identify the customer and business impact, recommend what teams should investigate next, and define how Uber should decide whether to mitigate, roll back, communicate, or make longer-term product and operational changes.