Remove Duplicates from Food Delivery Platform Data
✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Food delivery platforms accumulate duplicate records when the same restaurant operates virtual brands, lists on multiple delivery apps, or has separate entries for dine-in and delivery menus. A pizza restaurant might appear as 'Tony's Pizza', 'Tony's Pizza & Pasta', and 'Tony's Wings' on the same platform, sharing the same kitchen and address. Customer records similarly fragment when the same person orders from different devices or uses different payment methods. dataclean.to matches food delivery records by comparing restaurant addresses, menu item overlap, and customer contact information to produce a clean operational dataset.
The Problem
Duplicate restaurant listings on delivery platforms confuse customers and distort marketplace metrics. When the same kitchen appears under three storefronts, order volume appears spread across three weak performers rather than concentrated in one strong restaurant. Commission calculations may differ across duplicate listings, creating accounting discrepancies. Menu items duplicated between storefronts with different prices lead to customer complaints when the same pizza costs more from one listing. Delivery zone calculations that double-count the same restaurant inflate coverage metrics. Customer support teams waste time on issues that stem from orders placed on the wrong duplicate listing. Schema.org Restaurant structured data specification
How to Fix It
1
Export restaurant, menu, and customer data
Pull records from your delivery platform including restaurant name, address, phone, menu items with prices, customer name, delivery address, email, phone, and order history.
2
Upload to dataclean.to
Upload the CSV. The tool normalizes restaurant names, matches addresses, compares menu overlap between storefronts, and identifies customer records that share contact details across duplicate accounts.
3
Review duplicate clusters
Examine groups of restaurants flagged as the same kitchen. The tool shows address matches, phone number overlap, and menu item similarity percentages. Customer clusters reveal accounts with the same email or phone under different names.
4
Merge or link related records
For restaurants confirmed as the same kitchen, decide whether to merge into one listing or keep virtual brands as separate storefronts linked to a common entity. Unify customer accounts to show complete order histories.
5
Export the clean platform data
Download the deduplicated CSV and update your delivery platform database. Restaurant metrics now accurately reflect per-kitchen performance, and customer profiles show complete ordering patterns.
Frequently Asked Questions
How does the tool handle intentional virtual brands from the same kitchen?
Virtual brands sharing an address are flagged for review. You can choose to merge them into one entity for internal reporting while keeping them as separate customer-facing storefronts. The tool surfaces the relationship without forcing a merge.
Can it identify customers who order under different names?
Yes. The tool matches customer records by phone number and delivery address. A customer who ordered as 'Mike' from their phone and 'Michael' from the website is flagged when both entries share the same phone or address.
What about restaurants that moved to a new location?
Records with the same restaurant name and phone but different addresses are flagged as potential duplicates. You can review whether the address change represents a relocation, in which case the old listing should be archived, or a second location.
Example: Input → Output
name
prep_time
cook_time
servings
calories
Pasta Carbonara
15 min
20 min
4
650
pasta carbonara
PT15M
PT20M
4
650 cal
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.