Analyze why Grocery usage is growing but revenue is flat 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’s Grocery business is seeing usage grow globally, but revenue is not increasing at the same pace. You are asked to investigate this as a root-cause analysis problem for a scaled grocery marketplace serving consumers, enterprise merchants, Dashers, and DoorDash’s internal operations teams.

Assume “usage” may include signals such as orders, active users, sessions, carts, or delivery volume, while “revenue” may include consumer fees, merchant commissions, advertising, subscriptions, or other monetization streams. Your task is to clarify the anomaly, identify where the growth-revenue disconnect is happening, and build a structured investigation that can separate measurement issues from real business changes.

This is not a request to redesign the Grocery product or propose a growth strategy upfront. Focus first on framing the problem, segmenting the data, validating instrumentation, generating hypotheses, and determining what evidence would confirm or reject each path.

The experience should consider:

- How you would define “usage” and “revenue,” including numerator, denominator, time window, currency normalization, refunds, discounts, and global market differences.

- Whether the issue is isolated to certain countries, cities, merchant partners, store formats, consumer cohorts, order sizes, fulfillment types, or acquisition channels.

- Instrumentation checks for event tracking, order attribution, merchant billing, promotion accounting, subscription allocation, cancellations, and data pipeline changes.

- Marketplace hypotheses involving lower average order value, lower take rate, higher promotions, basket mix shifts, increased refunds, enterprise merchant contract changes, or reduced fee capture.

- Competitive and market factors such as price matching, Walmart/Amazon/Instacart pressure, local delivery fleets, and changing consumer grocery behavior.

- Operational factors including fulfillment reliability, substitution rates, out-of-stock issues, delivery distance, Dasher supply, batching, and service-level changes that may affect monetization.

- Evidence needed to distinguish healthy low-monetized adoption from a structural revenue problem, including cohorts, contribution margin, merchant ROI, consumer retention, and order frequency.

- Near-term mitigation, monitoring, and prevention mechanisms without jumping prematurely to a single fix.

Your goal is to demonstrate how you would lead a rigorous RCA at DoorDash scale: clearly frame the anomaly, isolate the most likely drivers, validate the data, prioritize investigation paths, and recommend what the business should monitor or act on next based on evidence.