Structured Product Data

Structured product data is product information organized in machine-readable formats that follow established standards. Instead of unstructured text on a webpage that humans can read but machines must interpret, structured data explicitly labels each piece of information -- product name, price, availability, brand, SKU, reviews -- in a format that search engines and AI agents can directly parse.

The most common standards for structured product data include:

• Schema.org Product markup: JSON-LD or microdata embedded in product pages that tells Google, Bing, and AI agents exactly what a product is, what it costs, and whether it's available. 45 million web domains use Schema.org structured data (Schema.org, 2024).
• Product feeds: Structured data files (XML, CSV, or API responses) submitted to channels like Google Merchant Center, Meta, and AI shopping platforms.
• GS1 standards: Global Trade Item Numbers (GTINs), Global Location Numbers, and other identifiers that uniquely identify products across the global supply chain.

Structured product data has always mattered for SEO and marketplace listing. With AI shopping agents, it has become the primary determinant of whether your products get recommended.

AI shopping agents cannot "browse" your website like a human. They evaluate products based on structured data in their product index. This makes structured data the single most important factor in AI visibility.

Product discovery. When a consumer asks ChatGPT "what are the best noise-cancelling headphones under $300?", the agent filters its product index by: category (headphones), feature (noise-cancelling), and price (under $300). Products missing any of these structured attributes cannot match the query.

Product comparison. AI agents compare products by specific attributes -- battery life, weight, driver size, connectivity. Products with complete structured attributes can be meaningfully compared. Products with just a title and price cannot.

Rich results. In Google search and AI Mode, structured Schema.org data enables rich product results -- star ratings, price ranges, availability badges, and review counts. These rich results increase click-through rates by 20-30% compared to plain results.

AI citations. For GEO, structured data helps AI models cite your products accurately. When Perplexity cites product specifications in its research-style responses, it pulls from structured data sources.

Schema.org Product markup. Add JSON-LD structured data to every product page. Essential properties: name, description, image, offers (price, availability, priceCurrency), brand, sku, gtin, aggregateRating, and review. Google's Rich Results Test tool validates your implementation.

Product feed optimization. Each sales channel has specific structured data requirements. Google Merchant Center needs title, description, price, availability, image_link, and GTIN at minimum. The OpenAI commerce feed spec has its own required fields. Feed optimization for AI goes beyond minimum requirements to include all attributes that help AI agents match products to consumer queries.

Data enrichment. Most product catalogs have gaps -- missing attributes, thin descriptions, incomplete categorization. AI-powered enrichment can generate missing structured data from existing product information, images, and category context.

Consistency across channels. Structured data should be consistent across your website (Schema.org), product feeds (Merchant Center, AI platforms), and marketplace listings. Inconsistent pricing or availability across channels erodes trust with both consumers and AI agents. AI catalog management maintains this consistency.