DoorDash UberEats Data Scraping Case Study — Where Each Platform Wins in Suburban America
How a US restaurant chain investor used DoorDash and UberEats data scraping across 250 suburban markets to replace national averages with local reality and guide portfolio priorities.
Client overview
Who the client is
The client is a US restaurant chain investor with portfolio companies operating across multiple suburban US markets. The investor needed reliable platform competitive intelligence on DoorDash and UberEats to advise portfolio companies on where each platform was winning — and where to prioritize commercial relationships. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Map platform market share across 250 suburban US markets
- Identify where DoorDash dominates vs. where UberEats leads
- Quantify merchant overlap (dual-platform vs. single-platform restaurants)
- Track 18 months of platform-share evolution per market
- Replace national-share averages with market-level evidence
- Advise portfolio companies on per-market platform priorities
The challenge
National platform share does not equal suburban reality
Industry coverage of DoorDash vs. UberEats focused on national share percentages and urban headlines. But the investor's portfolio operated in suburban America — and suburban platform dynamics were systematically different from headline metros. Without market-level merchant data, portfolio companies were making platform-prioritization decisions on national averages that did not reflect their actual operating reality.
The solution
A 250-market platform comparison tracker
FoodDataScrape built a continuous DoorDash data scraping and UberEats data scraping pipeline covering 250 suburban US markets, with per-market merchant-count tracking and platform-overlap analysis. The build went live in five weeks.
Map suburban markets
We defined 250 suburban US markets at ZIP-cluster resolution, distinct from urban metros.
Cross-platform merchant matching
Same-merchant matching identified which restaurants operated on both platforms versus only one.
Per-market share computation
Per-market merchant counts, exclusivity, and overlap were computed and rolled up monthly.
The AI layer
How does AI-assisted platform comparison work?
AI-assisted platform comparison combines food delivery data scraping across multiple platforms with cross-platform merchant matching — producing per-market merchant-level data that reveals where each platform actually wins versus where headline narratives apply.
On top of the raw feed, an AI matching layer turned multi-platform data into platform competitive intelligence: it matched the same merchants across DoorDash and UberEats, computed per-market merchant counts and exclusivity, and surfaced where each platform's local share differed from national averages. Each month the investor received refreshed market-by-market platform analytics.
- Matched 142,000+ merchants across DoorDash and UberEats
- Identified DoorDash dominance in 184 of 250 suburban markets
- Surfaced UberEats leadership in 47 specific suburban markets
- Flagged 19 markets where dual-platform overlap exceeded 80% (highest competitive intensity)
Data captured
What data we captured
The pipeline captured a full platform competitive intelligence view across suburban America:
| source | method | fields |
|---|---|---|
| DoorDash | DoorDash data scraping | merchants · menu · ZIP |
| UberEats | UberEats data scraping | merchants · menu · ZIP |
| AI matching layer | Cross-platform merchant matching | single vs dual platform |
BEFORE VS AFTER
Before vs after comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Platform-share visibility | National averages only | 250 suburban markets resolved |
| Merchant overlap insight | Anecdotal | Same-merchant cross-platform matched |
| Market-level strategy | Headline-narrative-led | Per-market priorities defined |
| Time-series depth | Quarterly industry reports | 18-month monthly panel |
| Portfolio advisory | Generic platform guidance | Per-portfolio-company priorities |
| Refresh cadence | Quarterly reports | Monthly market analytics |
ROI impact
From Assumption to Measurable ROI
ZIP-cluster resolution distinct from urban metros.
Cross-platform matched across DoorDash and UberEats.
DoorDash dominates 184 markets; UberEats leads 47.
Monthly platform-share evolution per market.
The data gave the investor a market-by-market platform-strategy framework — replacing national-share assumptions with suburban reality and aligning portfolio company commercial priorities to local dynamics.
Client testimonial
In the client's words
"We had been advising portfolio companies based on national platform-share averages. The suburban data showed us how often that was the wrong answer for the specific markets they actually operated in."
— Operating Partner, US restaurant investor (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in food delivery data scraping across the US
- DoorDash & UberEats coverage out of the box
- AI-assisted cross-platform merchant matching
- ZIP-cluster market 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 UberEats data scraping with AI cross-platform merchant matching — producing per-market merchant-level data that reveals platform share, overlap, and exclusivity.
Suburban markets are defined at ZIP-cluster resolution, distinct from urban-core metros — capturing the operating reality of mid-tier and outer-ring American communities.
Merchant matching uses name similarity, address normalization, GPS proximity, and operating-hours overlap — producing high-confidence cross-platform merchant identity.
A market-by-market platform-strategy framework that replaced national-share assumptions, portfolio-company commercial priorities aligned to local dynamics, and a continuing monthly analytics dashboard.
Yes — the same cross-platform comparison approach works for Grubhub vs. UberEats, foodpanda vs. GrabFood, Talabat vs. Careem, and any multi-platform market.
Yes — we use compliance-aware sourcing across all markets and delivery platforms.
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