How to Automate Product Descriptions with EAN Codes
Every product in the global supply chain has a unique identifier. The European Article Number (EAN), also known internationally as a GTIN-13, is a 13-digit barcode that identifies a specific product from a specific manufacturer. What many e-commerce sellers do not realise is that this simple number can serve as the key to automated product content generation, unlocking a workflow where you input a barcode and receive a complete, marketplace-ready product description.
This guide walks through how EAN-based automation works, what data can be extracted from product identifiers, and how to build a workflow that transforms barcodes into compelling product descriptions with minimal manual effort.
Understanding EAN Codes as Data Keys
An EAN code is more than a barcode on a package. It is a key into multiple global product databases that contain detailed information about the product it identifies. Databases like GS1, Open Food Facts, UPCitemdb, and various manufacturer catalogs store product attributes indexed by EAN: brand name, product title, category, weight, dimensions, materials, colour variants, and often much more.
When you scan or input an EAN code, an AI content system can query these databases to retrieve the product's structured data. This data serves as the foundation for description generation. Instead of a content writer starting from scratch -- researching the product, identifying key features, and writing from a blank page -- the system starts with a comprehensive data set and focuses its effort on transforming that data into compelling, readable content.
The quality of available data varies by product category and database. Consumer electronics and FMCG products tend to have rich data sets with detailed specifications. Niche or newly launched products may have sparser records. Effective EAN-based workflows include fallback mechanisms for products with limited database coverage, prompting the user to supply missing attributes before generation.
The EAN-to-Description Pipeline
A typical EAN-based content workflow follows four steps. First, the seller inputs one or more EAN codes, either manually, via CSV upload, or through barcode scanning. Second, the system queries product databases to retrieve available attributes. Third, the retrieved data is enriched and normalised -- standardising units, mapping categories, and filling gaps where possible. Fourth, the AI generates descriptions using the enriched data and marketplace-specific templates.
The enrichment step is critical. Raw database data often needs cleaning before it is useful for content generation. Brand names may appear in different formats (SAMSUNG vs. Samsung vs. samsung). Category mappings may not align with your marketplace's taxonomy. Specifications may be in units that need conversion for your target market. The enrichment layer handles these normalisation tasks automatically.
Batch processing makes this workflow particularly powerful for catalog onboarding. A new seller with 500 products can input their EAN list and receive complete descriptions for their entire catalog within hours rather than weeks. Existing sellers adding new product lines can generate descriptions for incoming inventory before the products even arrive in their warehouse.
Handling Edge Cases and Missing Data
Not every EAN lookup returns a complete data set. New products may not yet be registered in public databases. Private-label products may have EANs that contain only basic GS1 registration data. In these cases, the system needs a graceful fallback that prompts the seller to provide the missing information rather than generating incomplete descriptions.
Smart systems learn from these gaps. When a seller manually provides data for a product with sparse EAN records, that enriched data can be stored for future use. If another seller later queries the same EAN, the system can offer the previously enriched data set, creating a collaborative improvement loop that benefits the entire user base.
EAN validation is also important. The 13-digit EAN includes a check digit that can be verified algorithmically. Numbers that fail the check digit validation are caught before any database query is attempted, preventing wasted API calls and confusing error messages. Numbers shorter than 13 digits can be padded with leading zeros, as many systems truncate leading zeros when storing EANs in spreadsheets.
Integrating EAN Automation into Your Workflow
The most efficient integration connects your EAN-based content generation directly to your product information management (PIM) system or e-commerce platform. When a new product is added to your PIM with an EAN, the system automatically triggers data lookup and description generation. The generated content flows back into the PIM, ready for review and publication.
For teams without a PIM, spreadsheet-based workflows remain effective. Export your product list with EAN codes, upload it to the content generation tool, receive a spreadsheet with generated descriptions, review and approve, then import back into your marketplace listing tool. This batch approach works well for periodic catalog updates and seasonal product launches.
API integration offers the most flexibility for custom workflows. A REST API that accepts EAN codes and returns structured descriptions can be embedded into any existing system -- from custom admin panels to automated listing tools. This allows you to build EAN-based content generation into exactly the point in your workflow where it adds the most value.
Measuring Automation ROI
Track the time savings by comparing your pre-automation and post-automation content production metrics. Most teams find that EAN-based automation reduces the average time per description from 20-30 minutes of manual writing to 2-3 minutes of review and approval. For a catalog of 1,000 products, that translates to roughly 300 hours of saved writing time.
Quality metrics matter too. Measure your marketplace listing acceptance rate, customer question rate (fewer questions indicate more complete descriptions), and return rate attributed to "not as described." Well-automated descriptions that draw from verified product data consistently outperform manually written descriptions on these metrics because they are less likely to contain errors or omissions.
The strategic value extends beyond immediate time savings. Faster content production means faster time-to-market for new products, which directly impacts revenue. Products that go live with complete, optimised descriptions on day one capture sales that would otherwise go to competitors who listed first with better content.