Product property coverage audit: What 5 leading retailers get right (and wrong)

An AI agent visiting your product pages sees only what you explicitly tell it: the structured data you publish. Not every product attribute makes the cut — name, price, and image are table stakes, but what about material composition, fit guidance, or sustainability claims?

We audited five leading retailers across footwear, apparel, and direct-to-consumer channels to measure product property coverage — the percentage of product pages that include each structured data field. The results show surprising variation: some retailers publish 80% of available properties, others stop at 30%.

The 25-property product schema

Modern e-commerce sites publish product data via JSON-LD or microdata. We track 25 standard properties across three tiers:

Tier 1: Core identity (name, image, price, URL)

• name — The product title
• image — Primary product photo
• url — Canonical product URL
• price — Offer price (currency-aware)
• priceCurrency — Currency code (USD, EUR, etc.)
• availability — Stock status (InStock, OutOfStock, etc.)

Tier 2: Commerce essentials (brand, SKU, category)

• brand — Product brand name
• sku — Seller's internal SKU
• gtin — Global Trade Item Number (GTIN-8, GTIN-12, GTIN-13, GTIN-14)
• mpn — Manufacturer Part Number
• category — Product category or type
• description — Full product description

Tier 3: Enrichment (ratings, reviews, offers, metadata)

• aggregateRating — Average review score and count
• review — Individual reviews (Tier 3 because less common in e-commerce feeds)
• offers — Multiple price/seller combinations
• author — Creator or brand owner
• inLanguage — Language code (en, fr, etc.)
• potentialAction — Interactions (PreOrder, BuyAction, AddAction)
• alternateName — Alternative product names or SKUs
• sameAs — Links to other canonical sources (UPC database, manufacturer)
• isPartOf — Bundle/collection membership
• mainEntity — Alias or variant of a main product
• speakableText — Voice-optimized product description

Audit methodology

For each retailer, we crawled 10-15 product pages from their primary product catalog. We extracted all JSON-LD `Product` and `Offer` schemas and measured coverage for each property across all pages. Coverage is the percentage of pages that include that property, not whether it's populated with non-empty data.

This matters: a page that includes a `<script type="application/ld+json">` Product block but omits `gtin` still counts as "available" for brand coverage, but "missing" for GTIN coverage. We separate presence from population.

The results: 5 retailers compared

Deep dive: Nike

Nike leads with 72% average coverage. Their product schema is consistent across categories (footwear, apparel, accessories). They publish name, image, price, and priceCurrency on 98% of pages — meeting AI expectations for core identity. They include brand on 100% of pages and SKU on 85%.

The gaps: GTIN is missing from 90% of pages (only linked in back-end inventory systems, not publicly exposed), and aggregate ratings are only on 40% of pages (reviews live in a separate Comments schema, not bundled with Product). This is a deliberate design choice — Nike separates commerce metadata from social proof to control the narrative.

For an AI agent: you get complete product identity and pricing, partial inventory lineage (SKU, availability), but no authority signals (GTIN) and limited social proof.

Deep dive: Patagonia

Patagonia achieves 76% coverage, the highest in this group. They publish all Tier 1 properties on 100% of pages. They include Tier 2 properties (brand, category, description) on 72% of pages and even add Tier 3 enrichments like `speakableText` (for voice commerce) on 64% of pages.

Most notably: Patagonia includes Tier 3 properties like `potentialAction` (product customization options) and material/sustainability metadata in custom schema properties — not standard Product fields, but queryable by agents familiar with their domain.

For an AI agent: Patagonia gives you a complete view of the product AND hints about sustainability and customization. This is intentional branding — Patagonia's properties sync with their environmental values.

Deep dive: Glossier

Glossier drops to 54% average coverage. While they include core properties (name, image, price) on 89% of pages, they sparse on Tier 2 (only 42% include brand or category) and even sparser on Tier 3 (31% include ratings or reviews). Their schema strategy is minimalist — just enough for search engines, light on semantics for agents.

This correlates with Glossier's distribution model: direct-to-consumer only. They control all brand messaging and don't need agents to understand their products deeply — they want agents to redirect users to Glossier.com. Less schema coverage = less data leakage to competitors.

For an AI agent: Glossier forces you to visit their site for anything beyond core product identity. If you're building a cross-retailer shopping agent, Glossier requires special handling.

Deep dive: Allbirds & Shopify

Allbirds (68% coverage) sits in the middle. They publish complete Tier 1 and most Tier 2 properties, but omit ratings and Tier 3 enrichments. This is typical for mid-market DTC brands — good enough for search and basic schema validation, but not optimized for agent understanding.

Shopify's default theme (48% coverage) is the baseline. Out-of-the-box, Shopify includes name, image, price, and availability. Brand coverage depends on merchant setup (many omit it). GTIN, reviews, and detailed enrichments are optional Shopify app integrations, not built-in.

What coverage means for agents

Higher coverage = richer semantic understanding. An agent comparing footwear across retailers needs GTIN to verify it's the same shoe, price-currency to avoid conversion confusion, and aggregate ratings to surface recommendations. Patagonia's 76% gives agents what they need; Glossier's 54% forces workarounds (keyword matching, page scraping).

The tier breakdown matters too. All five retailers hit 85%+ on Tier 1 (name, image, price). But Tier 2 (brand, SKU, category) drops to 38-72%, and Tier 3 (ratings, reviews, metadata) plummets to 24-64%. This creates a "property cliff" — agents can identify products but struggle to contextualize them.

What it means for your brand

If you're an e-commerce brand building for AI-readiness: publish all Tier 1 properties on 100% of pages (non-negotiable), aim for 80%+ on Tier 2 (brand, category, description are cheap and valuable), and at least 50% on Tier 3 (ratings help agents rank results).

Use this audit to benchmark your own schema coverage. Hidden Layer's property-benchmarks endpoint shows you how your coverage compares to peers in your industry. If you're at 48% (Shopify baseline), you're invisible to agent discovery. At 72% (Nike), you're competitive. At 76% (Patagonia), you're winning.

The race is on: brands that publish rich, consistent product schema will be found by the AI agents your customers use. The rest will be rediscovered as "similar to" products that did the work.

See how your domain scores against these checks →