Root cause a sudden decline in retention among restaurants using Dasher App

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 decline in retention among restaurants that depend on the Dasher App-driven delivery workflow for fulfilling orders. These merchants rely on DoorDash to reliably dispatch Dashers, coordinate pickups, maintain delivery quality, and generate profitable incremental demand. A drop in restaurant retention could signal operational breakdowns, product regressions, marketplace imbalance, competitive pressure, or changes in merchant economics.

Your task is to investigate the decline as a product leader responsible for diagnosing what changed, where the issue is concentrated, and how DoorDash should respond. The focus is not to propose a full product redesign, but to structure a rigorous root-cause analysis that separates real merchant behavior changes from measurement noise and identifies the most likely drivers.

The experience should consider:

- How to define restaurant retention clearly, including the denominator, time window, cohort, and what counts as retained versus churned or inactive

- Whether the decline is broad-based or concentrated by geography, cuisine type, restaurant size, order volume, tenure, integration type, or delivery model

- Instrumentation checks to confirm whether the retention drop is real, including data pipeline changes, event definition changes, merchant status logic, and reporting delays

- Potential marketplace causes such as Dasher availability, pickup delays, cancellations, late deliveries, batching changes, or reduced delivery reliability

- Potential merchant-side causes such as lower order volume, higher fees, poor ROI, support issues, menu/pricing problems, or operational burden during pickup

- External and competitive factors, including seasonality, local regulation, promotions from Uber Eats or Grubhub, macro demand shifts, or restaurants moving to local fleets

- Evidence needed to validate or reject hypotheses, including merchant feedback, support tickets, operational metrics, order funnel data, and cohort comparisons

- Short-term mitigation and long-term prevention mechanisms, including monitoring, alerting, ownership, and learning loops

The goal is to demonstrate how you would frame the anomaly, prioritize the investigation, identify the most plausible root causes, and recommend decision-ready next steps without jumping prematurely to a solution.