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 a membership program used across Uber’s mobility and delivery experiences. You are investigating a recent spike in complaints from family users, such as parents or caregivers who rely on Uber One for rides, food delivery, grocery delivery, or recurring household errands.

Assume the complaint spike is visible in customer support data, app-store feedback, social channels, or in-product feedback, but the root cause is not yet known. Your task is to structure how you would debug the issue, determine whether it is isolated or systemic, and identify what evidence you would need before recommending any mitigation.

Focus on the RCA approach rather than jumping to a fix. Consider that family users may have distinct needs around reliability, safety, scheduling, affordability, shared accounts, child-related logistics, delivery accuracy, and trust in membership benefits.

The experience should consider:

- How you would define and validate the complaint spike, including baseline period, complaint rate denominator, and severity.

- Segmentation by geography, platform, trip or order type, family account behavior, membership tenure, device/app version, and acquisition channel.

- Instrumentation checks to confirm whether the spike reflects a real customer issue or a measurement, tagging, routing, or support-policy change.

- Hypotheses across product experience, pricing or benefits, availability, driver/courier reliability, safety perception, support response, and local operations.

- Evidence needed from support tickets, behavioral funnels, cancellation/refund data, ETA accuracy, benefit redemption, and repeat usage.

- How to prioritize investigation paths based on customer impact, safety risk, revenue exposure, and reversibility.

- Short-term mitigation options, communication needs, and how you would monitor whether the issue is stabilizing.

- Preventive mechanisms to detect similar issues earlier for family-heavy cohorts.

Your goal is to demonstrate a clear, structured RCA plan for an Uber product context: frame the anomaly, isolate where it is happening, test plausible causes with data, protect affected customers, and define how the team would know the issue has been resolved.