UberEats Data Scraping Case Study — Korean Restaurant Growth 3x Across 25 US Metros
How a private-equity firm used UberEats data scraping and AI-assisted cuisine classification to validate 3.1x Korean merchant growth across 25 US metros and underwrite a $42M roll-up investment.
Client overview
Who the client is
The client is a private-equity firm evaluating a Korean QSR roll-up thesis in the US. The firm needed defensible, time-series merchant-count data to validate the category growth narrative before underwriting a $42M platform investment. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Validate the Korean cuisine growth narrative with hard data
- Quantify Korean restaurant merchant growth across 25 US metros
- Track 36-month merchant counts and menu evolution
- Identify which metros had platform-suitable Korean operator density
- Underwrite a defensible roll-up investment thesis
- Provide ongoing monitoring post-investment
The challenge
An investment thesis without time-series evidence
The PE firm had identified Korean QSR as a thesis-stage opportunity but lacked the time-series data to defend a multi-million-dollar underwriting decision. Public restaurant databases offered snapshot counts but no trend depth. Industry reports cited growth narratives without underlying merchant-level evidence. The investment committee was unwilling to commit capital without verifiable category-growth data.
The solution
A 25-metro Korean restaurant growth tracker
FoodDataScrape built a continuous UberEats data scraping pipeline focused exclusively on Korean cuisine across 25 priority US metros, with 36 months of historical merchant-count and menu backfill. The build went live in six weeks.
Define Korean
We built a Korean-cuisine taxonomy covering classic Korean BBQ, Korean fried chicken, bibimbap-led concepts, fusion, and Korean-American formats.
Build extractors
UberEats extractors captured merchant launches, menu evolution, pricing, and ratings across all 25 metros.
Time-series backfill
Historical merchant-count data was reconstructed for the prior 36 months so the growth curve was visible from day one.
The AI layer
How does AI-assisted cuisine growth tracking work?
AI-assisted cuisine growth tracking combines food delivery data scraping with classification models that identify Korean restaurants consistently across multi-language menus, mixed-cuisine concepts, and ambiguous brand names — producing a clean merchant-count time series.
On top of the raw feed, an AI classification layer turned UberEats data into Korean cuisine intelligence: it distinguished Korean-led concepts from Pan-Asian fusion, identified Korean fried chicken separately from American QSR fried chicken, and tracked sub-category growth (Korean BBQ vs. KFC vs. bibimbap concepts). Each month the firm received a refreshed merchant-count index per metro and sub-category.
- Classified 9,800+ Korean and Korean-leaning restaurant listings
- Identified Korean fried chicken as the fastest-growing sub-category at 4.2x
- Surfaced 8 metros with merchant density above platform threshold
- Flagged 3 metros (NYC, LA, Atlanta) as already saturated
Data captured
What data we captured
The pipeline captured a full Korean restaurant dataset across every covered US metro:
| source | method | fields |
|---|---|---|
| UberEats | UberEats data scraping | merchants · menus · price · ratings |
| Time-series backfill | Historical reconstruction | 36 months of monthly counts |
| Classification layer | AI cuisine taxonomy | Korean sub-categories & chain attribution |
BEFORE VS AFTER
Before vs after comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Merchant-count visibility | Snapshot reports only | 36-month time series, monthly |
| Sub-category resolution | 'Korean' as one bucket | BBQ, KFC, bibimbap, fusion tracked separately |
| Metro coverage | Top-5 metros only | 25 metros with ZIP-level depth |
| Investment defensibility | Narrative-led | Data-anchored underwriting |
| Saturation signals | Anecdotal | Density-vs-growth flagged per metro |
| Underwriting confidence | Thesis-stage hesitation | $42M committed with conviction |
ROI impact
From Assumption to Measurable ROI
Verified across all 25 metros with month-by-month resolution.
PE firm closed a Korean QSR platform deal anchored to the data.
Cities with platform-suitable Korean operator density.
Sub-category leading the overall Korean expansion.
The data turned a thesis-stage investment into a board-defensible decision — and the same pipeline now monitors the portfolio company's competitive landscape post-close.
Client testimonial
In the client's words
"We had heard the Korean food growth story for years, but without time-series merchant data we couldn't defend the underwriting. The 36-month panel gave our investment committee exactly the evidence they needed."
— Investment Partner, mid-market PE firm (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in food delivery data scraping across the US
- UberEats coverage with ZIP-level resolution
- AI-assisted cuisine classification for clean merchant counts
- 36-month historical backfill for defensible time-series
- Compliance-aware sourcing and dedicated analyst support
- Live in six weeks with a free proof-of-concept first
Questions
Frequently asked questions
It combines UberEats data scraping with AI classification models that identify Korean restaurants consistently — distinguishing Korean-led concepts from Pan-Asian fusion and producing a clean merchant-count time series.
Historical UberEats menu and merchant-count data was backfilled using archived sources and time-stamped capture, producing a defensible month-by-month panel covering 36 months.
Korean BBQ, Korean fried chicken, bibimbap-led concepts, Korean-American fusion, and modern Korean casual were each tracked as distinct sub-categories.
The data underwrote a $42M Korean QSR roll-up investment and continues to monitor the portfolio company's competitive landscape post-close.
Yes — the same time-series cuisine-growth pipeline can track any cuisine or sub-category on UberEats, DoorDash, or any covered platform.
Yes — we use compliance-aware sourcing across all US markets and delivery platforms.
Need defensible cuisine-growth data for your thesis?
Tell us your cuisine and target markets. We'll scope a time-series tracking pipeline and show sample output in a short demo.
