✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Car parts catalogs are notoriously duplicate-heavy because the same physical part is sold under multiple OEM numbers, aftermarket brand names, and cross-reference IDs. A brake pad set for a 2020 Toyota Camry might appear under the Toyota OEM number, three aftermarket brand SKUs, and two different interchange numbers, each as a separate listing in your catalog. dataclean.to matches parts records by normalizing part numbers, comparing fitment data, and clustering entries that cross-reference to the same component.
The Problem
Duplicate parts listings create cascading problems for auto parts businesses. Inventory counts become unreliable when the same physical part is tracked under five different SKUs, leading to overordering or stockouts. E-commerce sites with duplicate listings confuse customers searching for a specific part and can trigger MAP policy violations when the same part appears at different prices. Fitment accuracy suffers because one listing might specify correct vehicle applications while its duplicate has incomplete or incorrect year-make-model data, leading to returns and warranty claims. ACES and PIES automotive catalog data standards
How to Fix It
1
Export your parts catalog
Pull all parts records including part number, brand, OEM cross-reference, description, fitment (year, make, model, engine), price, and stock quantity. Combine data from distributors, warehouse systems, and your e-commerce platform.
2
Upload to dataclean.to
Upload the CSV. The tool normalizes part numbers by removing dashes, spaces, and leading zeros, then matches records by cross-reference relationships and fitment overlap.
3
Review duplicate part clusters
Examine groups of records identified as the same physical part. The tool shows the different part numbers, brands, and descriptions that refer to the same component, along with any fitment discrepancies.
4
Consolidate into master part records
Merge confirmed duplicates into a single canonical listing per part. Retain all cross-reference numbers as attributes, use the most complete fitment data, and keep the most detailed description.
5
Export the clean catalog
Download the deduplicated CSV with unified part records. Import it into your inventory and e-commerce systems so each physical part has one listing with all applicable cross-references and accurate fitment.
Frequently Asked Questions
How does the tool handle OEM versus aftermarket part numbers?
The tool treats OEM and aftermarket numbers as potential cross-references to the same part. When records share overlapping fitment data and similar descriptions but different brand-specific part numbers, they are clustered for review as potential duplicates.
What about parts that fit multiple vehicle applications?
Multi-fitment parts are compared based on their full fitment range. Two records with the same part number but different fitment lists are flagged so you can merge the fitment data into one comprehensive record rather than maintaining incomplete duplicates.
Can it catch parts with transposed digits in part numbers?
Yes. The fuzzy matching process detects common transcription errors like transposed digits, missing characters, and extra dashes. Records with part numbers that are within one or two characters of each other are flagged for review.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.