Ecommerce size guide
structured data schema

Why size data matters for AI shopping agents

Apparel is one of the highest-intent ecommerce categories for AI shopping agents. Users ask very specific size-qualified queries: "women's running shorts size S with 3-inch inseam", "men's dress shirt 15.5 neck 34 sleeve", "kids snow boots toddler size 7". Without structured size data, AI agents cannot match these queries to your specific variants — they either skip your products or recommend them incorrectly, generating returns and refund requests.

Schema.org's SizeSpecification type, introduced in version 14.0, gives merchants a standard vocabulary to express:

• Which sizing system you use (US, EU, UK, JP, etc.)
• Which gender group the size applies to (Womens, Mens, Kids, etc.)
• The actual body measurements the size is intended to fit
• Age ranges for children's sizing

AI agent size query coverage by structured data tier

Schema.org SizeSpecification — complete property reference

sizeSystem country codes

Complete JSON-LD example: women's dress with US + EU sizing

This example uses ProductGroup (for the main product) with individual Product variants, each having a SizeSpecification. This is the structure Google recommends for size-variable apparel.

{
  "@context": "https://schema.org",
  "@type": "ProductGroup",
  "name": "Linen Wrap Dress",
  "description": "Relaxed-fit wrap dress in 100% Belgian linen. Available in three colors and five sizes.",
  "brand": { "@type": "Brand", "name": "Maison Laurent" },
  "url": "https://example.com/products/linen-wrap-dress",
  "productGroupID": "WD-2026-LINEN",
  "hasVariant": [
    {
      "@type": "Product",
      "name": "Linen Wrap Dress — Size S",
      "sku": "WD-LINEN-S",
      "color": "Ivory",
      "size": {
        "@type": "SizeSpecification",
        "name": "S",
        "sizeSystem": "US",
        "sizeGroup": "https://schema.org/WomensClothes",
        "hasMeasurement": [
          {
            "@type": "QuantitativeValue",
            "name": "chest",
            "minValue": 84,
            "maxValue": 89,
            "unitCode": "CMT"
          },
          {
            "@type": "QuantitativeValue",
            "name": "waist",
            "minValue": 65,
            "maxValue": 70,
            "unitCode": "CMT"
          },
          {
            "@type": "QuantitativeValue",
            "name": "hip",
            "minValue": 91,
            "maxValue": 96,
            "unitCode": "CMT"
          }
        ]
      },
      "offers": {
        "@type": "Offer",
        "price": "195.00",
        "priceCurrency": "USD",
        "availability": "https://schema.org/InStock"
      }
    },
    {
      "@type": "Product",
      "name": "Linen Wrap Dress — Size M",
      "sku": "WD-LINEN-M",
      "color": "Ivory",
      "size": {
        "@type": "SizeSpecification",
        "name": "M",
        "sizeSystem": "US",
        "sizeGroup": "https://schema.org/WomensClothes",
        "hasMeasurement": [
          { "@type": "QuantitativeValue", "name": "chest", "minValue": 91, "maxValue": 97, "unitCode": "CMT" },
          { "@type": "QuantitativeValue", "name": "waist", "minValue": 72, "maxValue": 78, "unitCode": "CMT" },
          { "@type": "QuantitativeValue", "name": "hip", "minValue": 99, "maxValue": 105, "unitCode": "CMT" }
        ]
      },
      "offers": {
        "@type": "Offer",
        "price": "195.00",
        "priceCurrency": "USD",
        "availability": "https://schema.org/InStock"
      }
    }
  ]
}

Shopify Liquid implementation with metaobject size specs

Store your size measurement data in a size_spec metaobject definition. Each entry represents one size in one system (e.g. "US M" and "EU 40" are separate entries that both link to the size M variant). This allows you to maintain size data in Admin without editing Liquid code.

Metaobject definition fields for size_spec

type: size_spec
fields:
  - key: size_label      type: single_line_text_field  (e.g. "M", "40", "12")
  - key: size_system     type: single_line_text_field  (e.g. "US", "EU", "UK")
  - key: size_group      type: single_line_text_field  (e.g. "Womens", "Mens")
  - key: chest_min_cm    type: number_decimal
  - key: chest_max_cm    type: number_decimal
  - key: waist_min_cm    type: number_decimal
  - key: waist_max_cm    type: number_decimal
  - key: hip_min_cm      type: number_decimal
  - key: hip_max_cm      type: number_decimal
  - key: inseam_min_cm   type: number_decimal  (bottoms only)
  - key: inseam_max_cm   type: number_decimal  (bottoms only)
  - key: foot_length_min type: number_decimal  (footwear only)
  - key: foot_length_max type: number_decimal  (footwear only)

Liquid JSON-LD output snippet

{% assign size_specs = product.metafields.custom.size_specs.value %}
{% if size_specs.size > 0 %}
  "size": [
    {% for spec in size_specs %}
    {
      "@type": "SizeSpecification",
      "name": {{ spec.size_label.value | json }},
      "sizeSystem": {{ spec.size_system.value | json }},
      "sizeGroup": {{ spec.size_group.value | json }},
      "hasMeasurement": [
        {% if spec.chest_min_cm.value != blank %}
        { "@type": "QuantitativeValue", "name": "chest",
          "minValue": {{ spec.chest_min_cm.value }},
          "maxValue": {{ spec.chest_max_cm.value }}, "unitCode": "CMT" },
        {% endif %}
        {% if spec.waist_min_cm.value != blank %}
        { "@type": "QuantitativeValue", "name": "waist",
          "minValue": {{ spec.waist_min_cm.value }},
          "maxValue": {{ spec.waist_max_cm.value }}, "unitCode": "CMT" },
        {% endif %}
        {% if spec.hip_min_cm.value != blank %}
        { "@type": "QuantitativeValue", "name": "hip",
          "minValue": {{ spec.hip_min_cm.value }},
          "maxValue": {{ spec.hip_max_cm.value }}, "unitCode": "CMT" }
        {% endif %}
      ]
    }{% unless forloop.last %},{% endunless %}
    {% endfor %}
  ]
{% endif %}

Children's sizing: suggestedAge

For children's apparel and footwear, the suggestedAge property on SizeSpecification lets AI agents answer queries like "rain jacket for 4 year old" or "toddler shoes 18 months". Combine it with sizeGroup (Boy, Girl, or Unisex) for maximum coverage.

{
  "@type": "SizeSpecification",
  "name": "2T",
  "sizeSystem": "US",
  "sizeGroup": "https://schema.org/KidsClothes",
  "suggestedAge": {
    "@type": "QuantitativeValue",
    "minValue": 2,
    "maxValue": 2.5,
    "unitText": "years"
  },
  "hasMeasurement": [
    { "@type": "QuantitativeValue", "name": "height", "minValue": 85, "maxValue": 90, "unitCode": "CMT" },
    { "@type": "QuantitativeValue", "name": "weight", "minValue": 12, "maxValue": 14, "unitCode": "KGM" }
  ]
}

Common size structured data errors

Scan for size data completeness

CatalogScan checks apparel product pages for SizeSpecification, sizeSystem, and hasMeasurement structured data. Get a report showing which of your products are invisible to size-qualified AI shopping queries.

Scan your store free

Related: Schema markup overview · ProductGroup JSON-LD guide · Metaobjects for structured data