USA Plant-Based Menu Data Scraping Case Study — Vegan Adoption Across 50 Restaurant Chains
How a US plant-based ingredient supplier used DoorDash, Uber Eats, Grubhub and chain-direct data scraping to track 18,400 plant-based menu additions across 50 major chains and win 11 new accounts with evidence-led pitches.
Client overview
Who the client is
The client is a US-based plant-based ingredient supplier selling B2B foodservice ingredients to major US restaurant chains. The supplier needed reliable USA plant-based menu data intelligence to identify which chains were actively adding plant-based menu items, which categories they were entering, and which suppliers were currently winning that business — so the supplier's sales team could prioritize accounts and tailor pitches with merchant-level evidence. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Track plant-based menu additions across 50 major US restaurant chains
- Identify which chains were actively expanding plant-based menus
- Quantify category-by-category and chain-by-chain growth rates
- Surface incumbent ingredient suppliers per chain (where signals allowed)
- Replace generic 'plant-based is growing' narratives with chain-level data
- Win new chain accounts with a data-led, account-tailored pitch
The challenge
'Plant-based is growing' — but in which chains, in which categories?
Every industry report told the supplier that plant-based was growing in the US. But the supplier's sales team could not pitch to 'the industry' — they had to pitch to specific chains, in specific categories, with specific evidence. Which chains were actually adding plant-based items? Which were just talking about it? In which menu categories? And how fast? Without merchant-level data, sales briefings were powered by intuition and industry headlines rather than chain-level facts.
The solution
A 50-chain US plant-based menu tracker
FoodDataScrape built a continuous USA plant-based menu data scraping pipeline across DoorDash, Uber Eats, Grubhub and chain-direct ordering, focused on 50 major US restaurant chains with 24-month menu history and plant-based item classification. The build went live in six weeks.
Build plant-based taxonomy
We built a US-market plant-based classification taxonomy covering vegan, vegetarian-plant-forward, dairy-alternative, and meat-alternative item categories.
Multi-platform chain extractors
Per-chain extractors captured menu items, descriptions, ingredient signals, and pricing across multiple platforms plus chain-direct sites.
Reconstruct 24-month history
Historical menus were backfilled so plant-based additions were visible from January 2024 forward.
The AI layer
How does AI-assisted plant-based menu classification work?
AI-assisted plant-based menu classification combines USA plant-based menu data scraping with NLP models that read menu item descriptions and ingredient signals to classify items into plant-based categories — producing defensible chain-by-chain adoption data.
On top of the raw feed, an AI menu-classification layer turned chain menu data into USA plant-based market intelligence: it classified items into plant-based categories, computed per-chain adoption velocity, identified the categories where adoption was concentrated, and surfaced incumbent supplier signals where available. Each month the supplier received refreshed chain-by-chain adoption analytics.
- Classified 18,400 plant-based items added across 50 US chains over 24 months
- Identified top 12 chains accounting for ~64% of all plant-based additions
- Surfaced burger and bowl categories as fastest-growing plant-based segments
- Flagged 17 chains with measurable plant-based menu retraction (items removed)
Data captured
What data we captured
The pipeline captured a full USA plant-based menu data intelligence view:
| source | method | fields |
|---|---|---|
| Multi-platform | USA plant-based menu data scraping | menus · descriptions · pricing |
| 24-month history | Menu reconstruction | additions · removals · retention |
| AI NLP layer | Plant-based classification | category & sub-category tagging |
BEFORE VS AFTER
Before vs after comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Plant-based growth visibility | Industry-aggregate | Per-chain adoption quantified |
| Category-level detail | Aggregate 'plant-based' | Sub-category sub-segments resolved |
| Time-series depth | Annual industry studies | 24-month monthly menu panel |
| Account targeting | Generic ICP lists | Adoption-velocity prioritized lists |
| Sales pitch quality | Industry-headline-led | Chain-specific evidence-led |
| Account wins | Baseline | 11 new chain accounts won |
ROI impact
From Assumption to Measurable ROI
Across 50 major US restaurant chains over 24 months.
Driven by data-led, account-tailored sales pitches.
Comprehensive coverage of the major US restaurant chain footprint.
Plant-based adoption velocity visible from January 2024 forward.
The data transformed the supplier's sales motion from generic plant-based pitches into chain-specific, evidence-led conversations — and directly contributed to 11 new chain accounts within the first year of using the data.
Client testimonial
In the client's words
"Every plant-based supplier talks about industry growth. We needed to walk into a meeting with one specific chain's actual menu data and say 'you added 38 plant-based items last year, here are the categories where you're most exposed.' That changed our win rate completely."
— VP of Sales, US plant-based ingredient supplier (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in USA plant-based menu data scraping
- Multi-platform coverage including chain-direct ordering
- AI-assisted NLP plant-based classification
- 24-month historical menu reconstruction
- Compliance-aware sourcing and dedicated US analyst support
- Live in six weeks with a free proof-of-concept first
Questions
Frequently asked questions
It combines USA food delivery data scraping with NLP models that read menu item names, descriptions, and ingredient signals to classify items into plant-based categories — producing defensible chain-by-chain adoption data.
The 50 largest US restaurant chains by unit count across QSR, fast-casual, casual-dining, and pizza segments — covering the chains most likely to drive plant-based ingredient demand.
Plant-based classification uses positive signals (explicit vegan/plant-based labels, dairy-alternative ingredients, meat-alternative naming) and negative signals (absence of animal proteins, exclusion of dairy) — producing high-confidence classification.
11 new chain accounts won within the first year, account-tailored sales pitches anchored to chain-specific evidence, and a continuing monthly chain-by-chain adoption dashboard powering the sales team.
Yes — the same NLP classification approach works for organic, gluten-free, keto-friendly, allergen-aware, and any ingredient-driven menu category with merchant-level visibility.
Yes — we use compliance-aware sourcing across all US markets and delivery platforms.
Need US chain-level plant-based menu data for your sales team?
Tell us your target chain set and ingredient category. We'll scope a chain-tracking pipeline and show sample output in a short demo.
