✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-05
E-commerce stores managing thousands of products across multiple suppliers inevitably accumulate inconsistent category labels. Products end up miscategorized, filters return incomplete results, and analytics by department become unreliable. dataclean.to scans your product catalog CSV, identifies category variations, and normalizes them into a clean, consistent taxonomy.
The Problem
Product data enters your catalog from bulk imports, supplier feeds, manual entry, and marketplace syncs. Each source uses different category names and granularity levels. The result: "Mens Shoes", "Men's Footwear", "Shoes - Men", and "Male Shoes" all coexist in your database, fragmenting collections, breaking faceted navigation, and distorting sales reports. Google Merchant Center product data specification for category standards
How to Fix It
1
Upload your product catalog
Export your entire product catalog to CSV from Shopify, WooCommerce, Magento, or your PIM system. Include all category, department, and product type columns.
2
Select category columns to normalize
dataclean.to identifies columns with categorical data. Select product type, department, collection, and tag columns for cleaning.
3
Review category clusters
The tool groups similar entries across your entire catalog. Review clusters like "Electronics > Headphones" / "Tech > Earphones" / "Audio Equipment" to decide on standard labels.
4
Apply normalization rules
Accept suggested normalizations or customize labels to match your store's navigation structure. The tool preserves category hierarchy if your data uses parent > child notation.
5
Export and import clean data
Download the standardized CSV and import it back into your e-commerce platform. All products now share consistent category labels for reliable filtering and reporting.
Frequently Asked Questions
Does it handle multi-level category hierarchies?
Yes. If your categories use a delimiter (like ">", "/", or "|") to denote hierarchy levels, the tool normalizes each level while preserving the parent-child structure.
Can I clean Google Product Categories alongside custom ones?
You can normalize your custom categories in one pass and then separately standardize your Google Product Category mappings. Both are text-based categorical data that the tool handles.
How many categories can it process?
The tool handles catalogs with thousands of unique category values. It is designed for large product databases where manual cleanup would be impractical.
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.