Specifications are readable, but not always reusable
A shopper can read “Dimensions: 30 × 20 × 12 cm” in a description. Software sees one block of rich text unless that specification is also stored in a structured field. This makes it harder to build consistent specification sections, filters, comparison experiences, feeds, or internal catalog workflows.
Product metafields let you model attributes that Shopify does not provide as standard product fields. The right type matters: a dimension or weight benefits from a measurement-aware definition, while a short care instruction may be text. Consistent definitions also make values easier to validate.
How to structure product attributes manually
A careful manual process works well when you are defining the model or updating a small collection:
- Inventory the recurring specifications in your descriptions and choose only the attributes that have a clear business or storefront use.
- Create product metafield definitions in Shopify with stable names, namespaces, keys, and data types. Decide whether dimensions will be separate fields or one formatted specification.
- Open each product, read the source specification, normalize its unit or terminology, and enter it in the matching metafield.
- Review representative products, including ones with missing, unusual, or conflicting specifications, before using the values in your theme.
Why extracting attributes manually does not scale cleanly
Catalog descriptions rarely follow one perfect template. One supplier may write “1.2 kg”, another “1200g”, and another may omit weight entirely. Colors and materials can appear in marketing sentences as well as formal key-value lists. Mechanical copy and paste does not resolve those differences.
At scale, separate detection from updating. First collect candidate attributes, then map each field to an appropriate definition, review product-level values, and only then write approved data. Do not invent a value when the description does not support one.
Before and after: specifications to metafields
Before: description
Northline Storage Box. Powder-coated steel in forest green. Dimensions: 40 × 28 × 18 cm. Capacity: 16 L. Weight: 2.1 kg.
After: structured data
- Material
- Powder-coated steel
- Color
- Forest green
- Dimensions
- 40 × 28 × 18 cm
- Capacity
- 16 L
- Weight
- 2.1 kg
Extract attributes with Enzo Field Extractor
Enzo Field Extractor scans product descriptions for common attributes and explicit key-value specifications. It brings detected values into a review workflow where you decide which Shopify metafield each field should populate.
The scan is a starting point, not a reason to skip catalog judgment. Preview the proposed changes, confirm the field mapping, and apply only the structured values you want. The product description remains available as the original source and customer-facing copy.
Install from the Shopify App StoreInstallation and plan details are handled through Shopify.
Frequently asked questions
Which product attributes can be stored in Shopify metafields?
Common examples include material, weight, length, width, height, capacity, volume, color, voltage, wattage, and care details. The useful set depends on the products and the way the store will display or use the data.
Should dimensions use one metafield or separate fields?
Use separate length, width, and height definitions when each dimension needs to be filtered, validated, or reused independently. A combined text value can be simpler when it is only displayed as one specification. Choose the model before populating the catalog.
What happens when a description does not contain an attribute?
Leave the metafield unchanged unless you have another verified source. A review-first workflow should make missing or unclear values visible rather than filling them with guesses.