Convert E-commerce Data to Schema Markup

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema

By dataclean.to team · 2026-02-13

E-commerce businesses sit on product data that, when properly marked up with schema.org structured data, can significantly improve search visibility through product rich results. Product prices, availability indicators, ratings, review counts, brand names, and product identifiers like GTINs — all of this data already exists in your e-commerce platform but rarely in the format that schema.org requires. dataclean.to takes your e-commerce data exports, cleans the product-specific formatting issues, and generates the Product, Offer, and Review schema markup that drives rich results.

The Problem

E-commerce platforms store product data optimized for display, not for structured data markup. Shopify exports prices as "$29.99" while schema needs 29.99 with a separate currency code. WooCommerce exports sale prices and regular prices in formats that need restructuring for Offer markup. Amazon seller files use their own proprietary field conventions. Inventory status might be a numeric count ("42") instead of a schema.org ItemAvailability value. Product variants create data duplication issues where the same product appears multiple times with different sizes or colors. These platform-specific data formats all need normalization before valid schema markup can be generated. Product schema type specification

How to Fix It

Export data from your e-commerce platform

Export your product catalog from Shopify, WooCommerce, BigCommerce, Magento, or any other platform as CSV. Include all available product attributes: names, descriptions, prices, images, SKUs, GTINs, brands, inventory counts, ratings, and review data.

Import and detect e-commerce field types

Upload to dataclean.to, which identifies e-commerce-specific data patterns: regular and sale prices, product identifiers (GTIN, UPC, EAN, ISBN, MPN), inventory quantities, rating values, review counts, and product taxonomy fields.

Normalize product data for schema

Clean all fields: prices become plain decimals with ISO 4217 currency codes, GTINs validate against check digit algorithms, availability converts from inventory counts to schema.org ItemAvailability values, ratings separate into ratingValue and bestRating, and brand names standardize.

Map to Product schema with nested types

Map columns to the Product schema structure: core product info (name, description, image, sku, gtin, brand), offer details (price, priceCurrency, availability, url) as nested Offer objects, and review data (ratingValue, reviewCount, bestRating) as a nested AggregateRating.

Deploy product schema markup

Generate JSON-LD for every product in your catalog. Deploy using the auto-embed script that matches products to pages by URL, download individual JSON-LD files, or use the hosted API to serve schema dynamically as your catalog changes.

Frequently Asked Questions

Which e-commerce platforms can I export data from?

dataclean.to works with CSV exports from any e-commerce platform: Shopify, WooCommerce, BigCommerce, Magento, PrestaShop, Squarespace Commerce, Wix Stores, Amazon Seller Central, Etsy, and custom-built stores. If you can export a CSV, the platform can process it.

How does schema markup help e-commerce SEO specifically?

Product schema enables rich results that display prices, ratings, and availability directly in search listings. These enhanced listings have measurably higher click-through rates than plain listings. Google also uses product structured data for Google Shopping integration, product knowledge panels, and price comparison features.

Should I include out-of-stock products in my schema markup?

Yes. Including out-of-stock products with the correct availability value (OutOfStock or Discontinued) signals to Google that the page still has valid product information. This is better than removing the markup entirely, which could cause Google to lose context about the page's content.

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.

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "Cleaned Customer Data",
  "description": "Normalized customer records with standardized fields",
  "keywords": ["customer data", "CRM", "contact list"]
}

💡 How it works: Consistent data formatting reduces import errors and makes your dataset compatible with downstream tools.

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