Chain-Free Menu Dataset — Independent Restaurant Menu Data Across US Restaurant Websites and Public Listings
How Food Data Scrape scoped a menu dataset that keeps local, non-chain US restaurants and filters out chain and franchise locations.
Key takeaways
Key takeaways
A US client asked Food Data Scrape for independent restaurant menu data: menus from local restaurants only, with every chain and franchise location left out. Our approach pairs public listings with restaurant websites, applies a chain-exclusion filter, and returns structured menu items, prices and categories for non-chain US restaurants.
Client overview
What did the client need?
The client wanted restaurant menus from local, independent restaurants in the US. Chains were out of scope, so the dataset had to separate a family-run diner from a national brand's outlet on the same street.
The segment is large and changing. Technomic data reported by Nation's Restaurant News counted 412,498 independent restaurants at the end of 2025, down 2.3% from the prior year. A separate summary of the same data notes full-service independents fell 2.6% in 2025, so closures must be tracked too.
The challenge
What made this hard?
- No "independent" flag. Public listings rarely say whether a restaurant belongs to a chain, so status must be inferred.
- Small and regional chains. A brand with 8 locations in one state is easy to miss if you only check national chain lists.
- Franchise lookalikes. Franchised outlets often use a local owner's business name on listings while selling a chain menu.
- Messy menu formats. Independent menus live in PDFs, images, website builders and ordering widgets.
- Closures. With the segment shrinking, closed restaurants must be dropped rather than kept with old prices.
The solution
How did Food Data Scrape solve it?
We designed the work around one rule: decide chain status first, then collect menus only for restaurants that pass.
Approach
- Build the restaurant universe. Start from public business listings, such as our restaurant POI dataset and Google Maps restaurant data for the USA.
- Normalise and quality-check. Map items to standard categories, parse prices and sizes, and log the source URL and capture date.
- Apply chain-exclusion logic. Flag a location as a chain when its brand name, domain or menu matches other locations above a set threshold (for example, the same brand name or website at 3 or more addresses), and check a reference list of chain and franchise brands.
- Stay compliant. Collect only public business data, follow each site's terms and robots.txt, use polite rate limits, and never collect personal contact details.
- Keep a review queue. Borderline cases, like small regional groups, get a manual check.
- Collect menus from public pages. Use the restaurant's own website first, then public delivery-app storefronts, via our restaurant menu data scraping and food delivery app data workflows.
Data captured
Data attributes / fields
| Field | Description |
|---|---|
| restaurant_id | Stable internal ID for each restaurant location |
| restaurant_name | Business name as shown on the public listing |
| address_city_state | Street address, city, state and ZIP code |
| cuisine_type | Primary cuisine or category from public listings |
| chain_status | independent, chain or review, from the exclusion logic |
| chain_match_reason | Why a location was excluded (brand name, domain or menu match) |
| menu_section | Menu heading, such as Starters, Mains or Drinks |
| item_name | Menu item name |
| source_url | Public page the menu was captured from, with capture date |
| item_price_usd | Listed price in US dollars, with size or variant where shown |
Client testimonial
In the client's words
“The chain-exclusion process gave us a clearer view of independent restaurant menus without mixing in national and regional brands. The structured menu data and exclusion reasons also made it easier to review coverage and support local restaurant market analysis.”
— Data Strategy Lead, US Food-Data Client
Results
What were the results?
- A chain-free menu file. The planned output is a menu dataset limited to independent US restaurants, so national and regional chains no longer skew menu and price analysis.
- Clear exclusion audit. Every removed location carries a reason, so the client can review or override the chain decision instead of trusting a black box.
- Coverage. The scope is US-wide; restaurant and menu-item counts are reported in each delivery file.
- Refresh plan. A monthly refresh re-checks each menu and marks restaurants that closed or stopped publishing a menu.
Questions
Frequently Asked Questions
We compare brand names, website domains and menus across locations and check a reference list of chain and franchise brands. Unclear cases go to manual review.
From public sources only: the restaurant's own website, public business listings and public delivery-app menu pages, collected within each site's terms.
No. Order and sales numbers are not public. We can add labelled public proxies, such as rating counts or popular-item badges, where shown.
Yes. Scope can be set by state, city, ZIP code or cuisine, and the chain filter runs the same way in each area.
Need a chain-free menu dataset?
Tell us the states, cities or cuisines you need, and we will scope a chain-free menu dataset with monthly refresh. Visit fooddatascrape.com to discuss your data requirement.
