Food Delivery Data Scraping Case Study — Plant-Based Menu Growth Across 10 US Metros
How a plant-based ingredient supplier used multi-platform menu data scraping to track +143% vegan menu growth across 10 US metros and reallocate $4.8M in sales focus.
Client overview
Who the client is
The client is a plant-based ingredient supplier — a B2B brand selling alternative protein and plant-based dairy ingredients to restaurant chains across the United States. With limited sales-team capacity, the brand needed reliable restaurant data intelligence to decide which US metros to prioritize for new account development. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Quantify vegan menu adoption across 10 priority US metros
- Track 36-month menu-growth trend by city and cuisine type
- Identify which metros had genuine momentum vs. plateauing demand
- Map restaurant categories where plant-based SKUs were growing fastest
- Replace anecdote-led sales targeting with hard data
- Build a foundation for ongoing plant-based market data tracking
The challenge
Sales decisions based on assumption, not adoption data
The brand's sales team was overweight in two flagship metros — New York and Los Angeles — based on long-held assumptions about plant-based momentum. Meanwhile, secondary metros like Portland, Austin, and Denver were under-served despite anecdotal reports of fast-growing demand. Without time-series menu data, the brand could not credibly defend any reallocation. The leadership team needed evidence, not intuition.
The solution
A 10-metro vegan menu adoption tracker
FoodDataScrape built a continuous pipeline combining UberEats data scraping, DoorDash data extraction and Grubhub menu capture into a single normalized vegan-SKU dataset, with 36 months of historical backfill. The build went live in five weeks.
Map vegan SKUs
We tagged plant-based dishes across every captured menu using a curated ingredient and label taxonomy.
Build extractors
Per-platform extractors captured menu changes, new listings, and price moves across all 10 metros.
Time-series backfill
Historical data was reconstructed for the prior 36 months so growth trends were visible from day one.
The AI layer
How does AI-assisted menu-trend tracking work?
AI-assisted menu-trend tracking combines food delivery data scraping with language models that classify dish names, ingredient lists, and dietary tags — so vegan SKUs are identified consistently across thousands of menus, not just where the label is explicit.
On top of the raw feed, an AI classification layer turned menu data into vegan restaurant intelligence: it disambiguated 'vegan' from 'vegetarian,' caught implicit plant-based items (e.g., dishes without dairy or animal protein but unlabeled), and rolled up SKU-level growth into city-level adoption curves. Each month the brand received a refreshed adoption index per metro.
- Classified 380,000+ menu items into vegan / vegetarian / omnivore
- Detected 22,400 newly-added vegan SKUs over the 36-month window
- Surfaced 4 metros with accelerating growth (Portland, Austin, Denver, Seattle)
- Flagged 2 metros (Atlanta, Chicago) with slowing momentum
Data captured
What data we captured
The pipeline captured a full plant-based restaurant dataset across every covered US metro:
| source | method | fields |
|---|---|---|
| UberEats | UberEats data scraping | menu · vegan flag · price · timestamp |
| DoorDash | DoorDash data extraction | menu · ingredient · price · zone |
| Grubhub | Grubhub menu capture | menu · category · price · promo |
BEFORE VS AFTER
Before vs after comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Vegan SKU visibility | Anecdote & field reports | 143% growth quantified across 10 metros |
| Metro prioritization | NYC + LA flagships | 4 high-momentum cities added |
| Time-series depth | Snapshot reports | 36 months of monthly history |
| Sales-team allocation | Even spread | Reallocated $4.8M to momentum metros |
| Refresh cadence | Annual market report | Monthly metro-level update |
| Account targeting | Generic outreach | SKU-growth-ranked target lists |
ROI impact
From Assumption to Measurable ROI
Plant-based menu items grew sharply across the 10-metro panel.
Investment shifted from plateauing metros to genuinely accelerating ones.
Portland, Austin, Denver, and Seattle elevated to top tier.
Three full years of menu evolution available from day one.
The reallocation lifted new-account win rates by 31% over the first two quarters — directly attributable to better metro targeting.
Client testimonial
In the client's words
"We had been quietly over-investing in our flagship metros for years. The data showed us where plant-based momentum was actually accelerating — and our sales team finally had a defensible answer when leadership asked why we were entering Austin or Portland."
— Head of Foodservice Sales, plant-based ingredient supplier (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in food delivery data scraping across the US
- UberEats, DoorDash & Grubhub coverage out of the box
- AI-assisted vegan SKU classification, not just keyword matching
- 36-month historical backfill, not just forward capture
- Compliance-aware sourcing and dedicated analyst support
- Live in five weeks with a free proof-of-concept first
Questions
Frequently asked questions
It combines food delivery data scraping with language models that classify dish names, ingredient lists, and dietary tags — catching vegan items even when they are not explicitly labeled.
The pipeline used UberEats data scraping, DoorDash data extraction, and Grubhub menu capture, with 36-month historical backfill across 10 priority US metros.
New York, Los Angeles, San Francisco, Chicago, Austin, Portland, Seattle, Boston, Denver, and Atlanta — each with ZIP-level resolution and chain attribution.
A $4.8M reallocation of sales investment and a 31% lift in new-account win rates over the first two quarters after the data went live.
Yes — the same menu-classification pipeline can track any cuisine, dietary category, or ingredient theme across food delivery platforms in any covered market.
Yes — we use compliance-aware sourcing across all US markets and delivery platforms.
Want vegan adoption data for your category?
Tell us your category and target metros. We'll scope a menu-tracking pipeline and show sample output in a short demo.
