Debug a spike in complaints from students on Logistics Platform at global scale

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 is seeing a sudden spike in complaints from student users on its Logistics Platform across multiple markets. These users may include college and university students ordering food, groceries, convenience items, or other local commerce deliveries around campuses, dorms, apartments, and late-night study locations.

You are asked to approach this as a root-cause analysis problem. The issue is not simply that complaints increased, but that the spike is concentrated in a specific user segment on a large-scale logistics marketplace where customer experience, Dasher availability, merchant operations, delivery reliability, and cost all interact.

Assume the complaint spike is recent and material enough to require immediate investigation. You should define how you would frame the anomaly, validate whether it is real, identify where in the delivery journey the issue may be occurring, and prioritize hypotheses without jumping directly to a fix.

The experience should consider:

- How to define the complaint spike, including baseline period, complaint rate denominator, severity, channels, and affected geographies

- Segmentation by student cohort, campus area, order type, time of day, merchant category, delivery distance, and subscription or promotion usage

- Instrumentation checks to rule out reporting, tagging, support workflow, survey, or data pipeline issues

- Marketplace factors such as Dasher supply, batching, ETA accuracy, merchant prep times, cancellations, substitutions, handoff failures, and refunds

- Student-specific context such as dorm access, campus drop-off constraints, late-night demand, event spikes, academic calendar shifts, and budget sensitivity

- Evidence needed to confirm or reject hypotheses across customer, Dasher, merchant, logistics, support, and payments data

- Short-term mitigation options, escalation paths, and customer communication considerations while the investigation is ongoing

- Prevention mechanisms such as monitoring, alerting, ownership, operational playbooks, and product instrumentation improvements

The goal is to demonstrate a structured RCA approach suitable for a complex, global logistics marketplace: isolate the true source of the complaint increase, assess customer and business impact, identify the most likely drivers, and outline how DoorDash should respond while protecting delivery reliability, marketplace balance, merchant ROI, Dasher earnings, and unit economics.