USA Food Delivery Data Scraping Case Study — DoorDash vs Uber Eats Market Share Across 25 Metros
How a US brand analytics agency used DoorDash and Uber Eats data scraping to map 184K+ merchants across 25 metros and deliver metro-specific platform-prioritization frameworks to Fortune 500 clients.
Client overview
Who the client is
The client is a US-based brand analytics agency serving Fortune 500 restaurant brands and QSR chains. The agency advises clients on platform-prioritization strategy and needed reliable USA food delivery data intelligence at the metro level — because their clients operate metro-by-metro, not by national averages. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Measure DoorDash vs Uber Eats market share across the top 25 US metros
- Quantify metro-level merchant counts, exclusivity, and overlap
- Identify metros where each platform dominates versus where they split
- Track 24 months of market share evolution per metro
- Replace national-share narratives with metro-level evidence
- Build a per-metro platform-prioritization framework for client briefs
The challenge
National averages hide brutal metro-level variance
The food delivery industry talks in national share percentages — DoorDash has roughly X%, Uber Eats has Y%. But restaurant brands operate metro-by-metro: a Houston QSR cares about Houston, not the national average. Without merchant-level metro data, the agency's clients were making platform prioritization decisions on aggregate numbers that systematically misrepresented their actual operating reality in specific cities.
The solution
A 25-metro DoorDash vs Uber Eats tracker
FoodDataScrape built a continuous DoorDash data scraping and Uber Eats data scraping pipeline covering 184,000+ merchants across the top 25 US metros, with cross-platform same-merchant matching and 24-month time-series. The build went live in five weeks.
Define 25 metro boundaries
We mapped each of the top 25 US metros to delivery-zone polygons covering urban-core through outer-ring suburbs — producing comparable footprints across all 25.
Cross-platform extractors
Per-platform extractors captured merchant lists, menus, ratings, and operating hours across DoorDash and Uber Eats with same-merchant matching.
Reconstruct 24-month history
Historical merchant presence was backfilled for every metro so share evolution was visible from January 2024 forward.
The AI layer
How does AI-assisted metro market share decoding work?
AI-assisted metro market share decoding combines USA food delivery data scraping with cross-platform merchant matching that aligns the same restaurant on DoorDash and Uber Eats — producing defensible metro-level share, overlap, and exclusivity data.
On top of the raw feed, an AI matching layer turned multi-platform data into USA food delivery market intelligence: it matched 184,000+ merchants across both platforms, computed metro-level merchant counts and exclusivity, and surfaced where each platform's dominance differed sharply from national headlines. Each month the agency received refreshed metro-level analytics.
- Matched 184,000+ merchants across DoorDash and Uber Eats in 25 metros
- Identified DoorDash dominance in 17 of 25 metros (avg 62% share)
- Surfaced Uber Eats leadership in 6 specific metros (Miami, SF, LA among them)
- Flagged 2 metros (NYC, Chicago) as genuine 50/50 split markets
Data captured
What data we captured
The pipeline captured a complete USA food delivery data intelligence view:
| source | method | fields |
|---|---|---|
| DoorDash | DoorDash data scraping | merchants · menu · metros · USD |
| Uber Eats | Uber Eats data scraping | merchants · menu · metros · USD |
| AI matching layer | Cross-platform merchant matching | single vs dual platform |
BEFORE VS AFTER
Before vs after comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Market share visibility | National averages | 25-metro merchant-level resolution |
| Cross-platform overlap | Anecdotal estimates | Same-merchant matched across platforms |
| Metro variance insight | Hidden by aggregates | 32-78% DoorDash share variance |
| Time-series depth | Quarterly analyst reports | 24-month monthly panel |
| Client advisory quality | Generic national guidance | Metro-specific platform strategy |
| Refresh cadence | Annual industry reports | Monthly metro analytics |
ROI impact
From Assumption to Measurable ROI
Comprehensive footprint across the top 25 US metropolitan markets.
Cross-platform matched across DoorDash and Uber Eats.
DoorDash dominates 17 metros; Uber Eats leads 6; 2 evenly split.
DoorDash share ranges from 32% to 78% across the 25 metros.
The metro-level data gave the agency a defensible platform-prioritization framework — replacing national-average advice with city-specific recommendations that aligned to clients' actual operating footprints.
Client testimonial
In the client's words
"Our clients open restaurants in metros, not in 'America.' The national share numbers were quietly leading us in the wrong direction for half the metros our clients actually operated in. The metro-level data ended that."
— Managing Partner, US brand analytics agency (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in USA food delivery data scraping
- DoorDash & Uber Eats coverage out of the box
- AI-assisted cross-platform merchant matching
- Metro-level delivery-zone polygon resolution
- Compliance-aware sourcing and dedicated US analyst support
- Live in five weeks with a free proof-of-concept first
Questions
Frequently asked questions
It combines DoorDash data scraping and Uber Eats data scraping with delivery-zone polygon mapping and AI cross-platform merchant matching — producing per-metro merchant-level share, overlap, and exclusivity data.
New York, Los Angeles, Chicago, Dallas, Houston, Washington DC, Miami, Philadelphia, Atlanta, Boston, Phoenix, San Francisco, Riverside, Detroit, Seattle, Minneapolis, San Diego, Tampa, Denver, Baltimore, St. Louis, Charlotte, Orlando, Portland, and San Antonio.
Merchant matching uses name similarity, address normalization, GPS proximity, and operating-hours overlap — producing high-confidence cross-platform merchant identity across DoorDash and Uber Eats.
Different metros have different fleet density, restaurant-supplier relationships, demographic patterns, and platform-launch histories — producing structural differences that national averages erase.
A metro-by-metro platform-prioritization framework that replaced generic national advice with city-specific recommendations, plus a continuing monthly metro analytics dashboard for client briefs.
Yes — we use compliance-aware sourcing across all US markets and delivery platforms.
Need metro-level US food delivery data for your clients?
Tell us your target metros and brand portfolio. We'll scope a metro-tracking pipeline and show sample output in a short demo.
