Investigate why conversion fell after a Merchant Portal launch

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 recently launched an updated Merchant Portal experience for restaurants and local merchants who use the portal to manage onboarding, store setup, menus, hours, promotions, performance reporting, and operational settings. Soon after launch, the team observes that conversion has fallen in a key merchant workflow.

Your task is to investigate the drop as a root-cause analysis problem. Treat “conversion” as a measurable funnel outcome that needs to be clarified, such as merchants completing onboarding, publishing their store, configuring key settings, or completing another critical portal action. You should frame the anomaly, determine whether the decline is real, identify where and for whom it is happening, and prioritize the most likely explanations.

This is not a request to redesign the Merchant Portal immediately. Focus on how you would diagnose the issue using product data, operational signals, merchant feedback, experimentation history, and launch context, while considering DoorDash’s marketplace constraints across merchants, consumers, Dashers, and business health.

The experience should consider:

- The exact conversion metric, numerator, denominator, time window, and expected baseline before the launch

- Funnel steps in the Merchant Portal where merchants may be dropping off, such as login, setup, menu editing, store activation, or campaign creation

- Segmentation by merchant type, size, geography, device/browser, acquisition channel, tenure, and integration status

- Instrumentation checks, including event tracking changes, data delays, duplicate events, missing events, or altered definitions after launch

- Launch-specific factors such as feature flags, rollout cohorts, UX changes, permission changes, localization, performance, or API dependencies

- Evidence sources such as analytics dashboards, session recordings, support tickets, sales/account-manager feedback, merchant surveys, and operational logs

- Short-term mitigation options, escalation paths, rollback criteria, and communication to affected merchants or internal teams

- Prevention mechanisms such as monitoring, alerting, pre-launch QA, experiment guardrails, and post-launch review processes

The goal is to demonstrate a structured investigation that separates measurement issues from real merchant behavior changes, narrows the problem to the most affected segments and funnel steps, and defines a practical path to confirm root cause and protect merchant success on DoorDash.