Artisan Goods Co: 250% AI Visibility Increase via Product Schema

Initial Diagnosis

Artisan Goods Co exhibited severe AI recommendation failures. Analysis of AI system responses to queries like "Where can I buy [product type]?" and "Best [product category] online" showed:

• ChatGPT recommendation rate: 18% (9 mentions in 50 relevant queries)
• Claude recommendation rate: 12% (6 mentions in 50 relevant queries)
• Perplexity recommendation rate: 24% (12 mentions, but often with incorrect pricing or availability)
• Competitor hallucination: AI systems recommended 34 non-existent competitors or products that did not exist
• Google AI Overviews: Artisan Goods Co products appeared in only 8% of relevant shopping queries

Root cause analysis identified three critical gaps:

1. Incomplete Product schema: Product pages had basic Product schema but lacked Offer, AggregateRating, and Brand entities. AI systems could not understand pricing, availability, or quality signals.
2. Missing product relationships: No category taxonomies or hierarchical relationships. AI systems could not map products to categories or understand product families.
3. No real-time validation: Product schema was static. Out-of-stock items still showed availability: "InStock", causing AI systems to recommend unavailable products.

Technical Implementation

Phase 1: Complete Product Schema

Deployed comprehensive Product schema on all 8,500 product pages with complete metadata:

{
  "@type": "Product",
  "@id": "https://artisangoods.com/products/{sku}#product",
  "name": "{Product Name}",
  "description": "{Product Description}",
  "brand": {
    "@type": "Brand",
    "name": "{Brand Name}",
    "@id": "https://artisangoods.com/brands/{brand-slug}#brand"
  },
  "offers": {
    "@type": "Offer",
    "price": "{Current Price}",
    "priceCurrency": "CAD",
    "availability": "https://schema.org/{InStock|OutOfStock|PreOrder}",
    "url": "https://artisangoods.com/products/{sku}",
    "seller": {
      "@type": "Organization",
      "name": "Artisan Goods Co"
    },
    "priceValidUntil": "{Expiry Date}"
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "{Average Rating}",
    "reviewCount": "{Total Reviews}",
    "bestRating": "5",
    "worstRating": "1"
  },
  "category": "{Product Category}",
  "productID": "{SKU}"
}

Real-time validation: Implemented dynamic schema generation that updates availability based on inventory levels. Out-of-stock products automatically emit "availability": "https://schema.org/OutOfStock".

Phase 2: Category Taxonomies

Created hierarchical category relationships using ProductCollection schema:

• /categories/handmade-jewelry: Added ProductCollection with "hasProduct" array linking to all jewelry products
• /categories/artisan-home-decor: Added parent-child category relationships using "isPartOf"
• /categories/: Added ItemList schema with all top-level categories

Result: AI systems can now understand product hierarchies and recommend products within correct categories.

Phase 3: Brand Entity Mapping

Created authoritative Brand entities for all 127 brands:

• Each brand page emits Brand schema with "@id"
• All products link to brand via "brand": {"@id": "https://artisangoods.com/brands/{slug}#brand"}
• Brand pages include "hasProduct" array listing all products from that brand

Total schema changes: 8,500 product pages modified, 127 brand pages created, 23 category pages enhanced, 8,650 JSON-LD blocks updated, 0 schema validation errors.

Results

Week 4 (post-deployment): ChatGPT recommendation rate increased to 32%. Competitor hallucination decreased by 45%.

Week 8: Recommendation rates stabilized. ChatGPT: 58%, Claude: 52%, Perplexity: 68%.

Week 11 (final measurement):

• AI recommendation accuracy: 63% average across ChatGPT, Claude, Perplexity (up from 18% baseline, 250% increase)
• ChatGPT recommendation rate: 61% (up from 18%)
• Claude recommendation rate: 58% (up from 12%)
• Perplexity recommendation rate: 70% (up from 24%, with correct pricing and availability)
• Competitor hallucination: Decreased by 90% (from 34 to 3 non-existent recommendations)
• Google AI Overviews: Artisan Goods Co products now appear in 52% of relevant shopping queries
• Product mention accuracy: 94% of mentions include correct pricing, availability, and ratings
• Schema validation: 100% valid JSON-LD, 0 errors in Google Rich Results Test

Technical note: E-commerce conversion rate increased by 8% as a side effect, but this was not the primary goal. The intervention targeted AI recommendation systems specifically.

Pattern Recognition

This failure mode occurs when:

1. E-commerce platforms have incomplete Product schema (missing Offer, AggregateRating, Brand)
2. Product relationships are not mapped (no category taxonomies, no brand hierarchies)
3. Product schema is static and does not reflect real-time inventory (out-of-stock items still show InStock)
4. AI systems cannot understand product quality signals (missing ratings, reviews, brand authority)

Fix requires: Complete Product schema with Offer, AggregateRating, and Brand entities. Category taxonomies with hierarchical relationships. Real-time schema validation for inventory. Brand entity mapping. AI systems need complete product metadata to recommend accurately and avoid hallucinating competitors.

Self-aware note: If your e-commerce platform is not being recommended by AI systems when users ask "Where can I buy [product]?" or AI systems are recommending non-existent competitors, this case study demonstrates the exact technical implementation required. The problem is not product quality—it's product schema completeness and entity visibility.