✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Restaurant directories, food delivery platforms, and review aggregators pull dining data from Google Business, Yelp, OpenTable, DoorDash, UberEats, and direct restaurant submissions. The same restaurant often appears under slightly different names across platforms: 'Bangkok Thai Kitchen', 'Bangkok Thai', and 'Bangkok Kitchen Thai Restaurant' all refer to one establishment. Delivery-only ghost kitchen brands operating from the same physical kitchen create additional confusion. dataclean.to matches restaurant records by name, address, phone number, and cuisine type to build a clean, authoritative dining database.
The Problem
Duplicate restaurant listings directly impact diners, restaurant owners, and platform operators. A diner searching for Thai food sees the same restaurant three times with different star ratings because reviews are fragmented across duplicate profiles. Restaurant owners cannot manage their online reputation when it is scattered across listings they may not know about. Delivery platforms with duplicate entries for the same kitchen may route orders to the wrong listing, causing delays. Directory operators who report inflated restaurant counts lose credibility when advertisers or investors audit the data. Aggregate review scores become unreliable when the same diner's experience is averaged differently across split profiles. W3C Data on the Web Best Practices
How to Fix It
1
Export restaurant listing data
Pull restaurant records from your directory, delivery platform, or aggregated data sources into CSV format. Include restaurant name, street address, city, phone, cuisine type, price range, and source platform.
2
Upload to dataclean.to
Upload the CSV. The tool compares restaurant names, addresses, and phone numbers to identify establishments listed multiple times across different platforms or with inconsistent naming.
3
Review duplicate restaurant clusters
Examine flagged groups. Common duplicates include the same restaurant under abbreviated and full names, establishments that moved but retain both old and new address listings, and ghost kitchen brands operating from an existing restaurant's address.
4
Merge into single restaurant listings
Consolidate confirmed duplicates into one entry per restaurant. Keep the most complete business details, the current address and phone, the broadest cuisine classification, and the highest review count for social proof.
5
Export the clean restaurant database
Download the deduplicated data for import into your directory or platform. Clean listings help diners find restaurants without confusion, give owners control of their online presence, and provide accurate market coverage data.
Frequently Asked Questions
How does the tool handle ghost kitchens operating from the same address as a dine-in restaurant?
Ghost kitchen brands with different names but the same physical address are flagged for review. You decide whether they should be merged with the parent restaurant or kept as separate delivery-only listings, depending on whether they represent distinct concepts.
Can it match restaurants across different delivery platforms?
Yes. The same restaurant listed on DoorDash, UberEats, and Grubhub often has slight name and address variations. The tool matches on phone number and address proximity to identify cross-platform duplicates.
What about restaurant chains with similar names in the same city?
Chain locations sharing a brand name but at different addresses are treated as separate restaurants. The tool requires address match or phone number match in addition to name similarity to flag a duplicate, so two Chipotle locations across town are kept distinct.
Example: Input → Output
name
cuisine
address
phone
hours
La Bella Italia
Italian
456 Oak Ave Chicago
(312) 555-0100
Mon-Sun 11am-10pm
la bella italia
italian
456 oak ave
chicago il
(312)555-0100
daily 11-22
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.