dataclean.to

Clean Data for Facebook Product Catalog

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-13

Facebook (Meta) Product Catalogs power dynamic ads, Instagram Shopping, Facebook Shops, and Marketplace listings. But uploading raw product data from spreadsheets or databases almost always triggers feed errors — rejected items, missing required fields, format mismatches, and disapproved listings. dataclean.to helps you clean and format your product data before it reaches Facebook, catching the issues that cause catalog upload failures and ad disapprovals.

The Problem

Facebook's catalog system is strict about data formatting but vague about error explanations. A product gets rejected for "invalid price" without telling you that the dollar sign caused the issue. Images fail because URLs use HTTP instead of HTTPS. Availability values must be exactly "in stock", "out of stock", or "preorder" — not "In Stock", "available", or "yes." Condition must be "new", "refurbished", or "used" — not "brand new" or "like-new." When you have thousands of products, finding and fixing these formatting issues row by row is an enormous time sink that delays your ad campaigns. Facebook product catalog data feed specifications

How to Fix It

1
Upload your product data
Import your product spreadsheet or CSV into dataclean.to. The platform accepts data from any source — e-commerce platforms, inventory systems, ERP exports, or manually maintained spreadsheets.
2
Detect and flag format issues
dataclean.to scans every column and identifies formatting problems: prices with currency symbols instead of plain numbers, availability text that doesn't match Facebook's allowed values, image URLs using HTTP, missing required fields like brand or GTIN, and description text exceeding character limits.
3
Apply Facebook-specific cleaning rules
Normalize your data to meet Facebook's exact requirements. Prices become plain decimal numbers. Availability maps to Facebook's three allowed values. Condition text standardizes to new, refurbished, or used. Image URLs are validated and converted to HTTPS. HTML tags are stripped from descriptions.
4
Validate required fields are complete
Check that every product has the required fields populated: id, title, description, availability, condition, price, link, image_link, and brand. dataclean.to highlights rows with missing required data so you can fill gaps before export.
5
Export the cleaned catalog feed
Download the cleaned data as a CSV formatted for Facebook Catalog upload. The export uses Facebook's expected column headers and data formats, ready for direct upload to Commerce Manager or scheduled feed URL.

Frequently Asked Questions

What are the most common Facebook Catalog rejection reasons?
The most frequent causes are: prices with currency symbols or commas (Facebook expects plain numbers like 29.99), availability values that don't match the three allowed options, image URLs that return 404 errors or use HTTP instead of HTTPS, missing required fields (especially brand and GTIN), and descriptions containing HTML markup.
Can dataclean.to validate my image URLs for Facebook?
dataclean.to validates that image URLs are properly formatted, use HTTPS, and have valid URL structure. It can also detect common issues like spaces in URLs, missing file extensions, and placeholder image paths. Actual image resolution and content validation require Facebook's own review.
How is a Facebook Catalog feed different from Google Shopping?
While both require product data feeds, they differ in field names (Facebook uses 'image_link' while Google uses 'image_link' too but with different format rules), availability values (Facebook allows 3 values, Google allows 4), and category requirements (Facebook uses Google's product taxonomy but accepts it differently). dataclean.to can format the same source data for either platform.

Example: Input → Output

nameskupricebrandrating
Wireless Headphones ProWHP-001$89.99SoundTech4.5
wireless headphones proWHP00189.99soundtech4.5/5

Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Wireless Headphones Pro",
  "sku": "WHP-001",
  "offers": {"@type": "Offer", "price": "89.99", "priceCurrency": "USD"},
  "brand": {"@type": "Brand", "name": "SoundTech"},
  "aggregateRating": {"@type": "AggregateRating", "ratingValue": "4.5"}
}
💡 How it works: Product schema enables rich results with price, availability, and star ratings in Google Shopping.

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