GEO for ecommerce: structured data first

When a shopper asks an AI assistant to compare products, the assistant has to extract attributes – price, availability, materials, sizes, ratings – from somewhere. Sites that publish that data as structured Product schema are dramatically easier to cite than sites that bury it in JS-rendered tabs.

The minimum viable Product schema

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Acme Widget Pro",
  "image": "https://acme.co/widget.jpg",
  "description": "Industrial widget rated for 10,000 cycles.",
  "sku": "AW-PRO-01",
  "brand": { "@type": "Brand", "name": "Acme" },
  "offers": {
    "@type": "Offer",
    "price": "129.00",
    "priceCurrency": "GBP",
    "availability": "https://schema.org/InStock",
    "url": "https://acme.co/products/widget-pro"
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.7",
    "reviewCount": "243"
  }
}

Beyond the basics

• Use comparable attribute names across your catalogue – 'material', 'weight_kg', 'warranty_years'.
• Mark variants with ProductGroup and individual Product entries.
• Keep availability accurate. Generative engines actively de-prioritise stale stock claims.
• Pair with FAQ schema on product pages for shipping, returns and sizing questions.