Debug a spike in complaints from students on DashPass

Problem Statement Description

Product context: DoorDash is a local commerce and delivery platform; its products include restaurant delivery, DashPass, grocery and retail delivery, merchant tools, and dasher tools.

DoorDash has observed a sudden spike in complaints from students using DashPass. These users may be on a student-specific subscription plan and rely on DoorDash for affordable, convenient meals, groceries, and local commerce deliveries around campus or student housing. The issue appears to be concentrated enough to require a structured root-cause investigation rather than a general customer support review.

Your task is to frame how you would debug the complaint spike as a product manager. Focus on identifying what changed, which student cohorts are affected, whether the issue is product-, operations-, pricing-, eligibility-, merchant-, dasher-, or support-related, and how you would separate true user pain from measurement or reporting artifacts.

You should assume DoorDash operates a multi-sided marketplace where student experience is influenced by subscription benefits, fees, delivery reliability, merchant availability, dasher supply, promotions, support policies, and campus-specific logistics. The investigation should balance customer experience with marketplace health, dasher earnings, merchant ROI, and unit economics.

The experience should consider:

- How you would define and scope the complaint spike, including numerator, denominator, baseline, time window, and complaint categories.

- How you would segment the issue by student status, campus, geography, merchant type, order category, subscription tenure, device/app version, and acquisition channel.

- How you would validate instrumentation, support tagging, survey changes, policy changes, or reporting shifts before assuming a real product issue.

- What hypotheses you would generate across DashPass benefits, pricing or fees, eligibility verification, promotions, checkout, delivery ETA, cancellations, refunds, and support resolution.

- What data sources you would use, such as order funnels, support tickets, refund rates, delivery performance, subscription events, merchant availability, dasher supply, and app release logs.

- How you would distinguish between localized operational issues and broader product or subscription problems.

- What immediate mitigations you would consider while the investigation is ongoing, without prematurely overcorrecting.

- How you would recommend preventing similar complaint spikes through monitoring, alerts, cohort dashboards, and launch/change management.

The goal is to demonstrate a structured RCA approach: clearly frame the anomaly, verify the data, isolate affected segments, test plausible causes with evidence, propose responsible mitigations, and define how DoorDash should monitor student DashPass experience going forward.