Nightshift Data

How should I structure fitment data in a Shopify parts store?

Store fitment as structured data, not text in the description: one entry per vehicle or machine, with the fields your category matches on. On Shopify that usually means metafields or metaobjects (or a year/make/model app), and then rendering the same fitment as visible text and JSON-LD on the product page so search, filters and AI assistants can all use it.

Last updated · Nightshift Data

Three places fitment needs to work on Shopify

Where fitment is used on a Shopify store
WhereWhat it needsHow it's usually done
Store search and filtersFitment values the store can filter onMetafields used as Search & Discovery filters, or a year/make/model app
The product pageA readable fitment list with qualifiers and exclusionsTheme code that renders metafields or metaobjects as a table
AI assistants and search enginesFitment in the page HTML and in Product JSON-LDThe same theme code, plus JSON-LD generated from the same data

Metafields and metaobjects in brief

Metafields extend Shopify's data model, so a product can carry fields Shopify doesn't have by default [1]. Metaobjects group several fields into one object that metafields can reference [2]. List metafields store several values in one field [3]. For fitment that means two common patterns:

Search & Discovery can use some metafield types as storefront filters, including single line text, lists of single line text and metaobject references, with limits on the number of filters and values shown [4].

How to set up fitment on Shopify, step by step

  1. 1

    Define your fitment key

    Use the fields your category matches on, such as year, make, model and trim for powersports, or engine make, model, HP and serial range for marine. The pillar guide lists them by category.

  2. 2

    Create the metafield or metaobject definitions

    Choose the simple or structured pattern above. For anything with qualifiers, the structured pattern keeps each row intact.

  3. 3

    Load clean fitment from one source

    Import from your cleaned fitment file, not by hand. The same file should feed your marketplaces, so every channel agrees.

  4. 4

    Render it on the product page

    Add a fitment table to the product template, in the HTML the server sends. Some AI crawlers don't run JavaScript.

  5. 5

    Add it to Product JSON-LD

    Extend your theme's Product structured data with identifiers and fitment, for example with isAccessoryOrSparePartFor. Keep it identical to the visible table.

  6. 6

    Turn on filters where they help

    Use Search & Discovery filters for the keys shoppers browse by, within Shopify's limits, or a year/make/model app for full vehicle lookups.

Where Nightshift fits

Nightshift produces the clean fitment file this setup depends on: fields filled from your own supplier data, gaps marked “Needs supplier data”, and an import-ready CSV plus Product JSON-LD. Try it on a few parts with the free check, or see an example report.

Sources

Checked October 25, 2026. Platform rules and vendor offerings change; check the source for the current version.

  1. Shopify Help Center: Metafields
  2. Shopify Help Center: Metaobjects
  3. Shopify Dev: Metafield data types
  4. Shopify Help Center: Search & Discovery filters

Frequently asked questions

Does Shopify have a built-in fitment field?

No. Shopify doesn't have a native year/make/model field. Stores use metafields and metaobjects to store fitment, or a year/make/model app that keeps its own fitment database.

Can I filter a Shopify store by year, make and model?

Shopify's Search & Discovery app can turn some metafield types into storefront filters, including single line text, lists of single line text and metaobject references. Filters have limits, such as how many values a filter shows, so large fitment lists usually need a year/make/model app or a dedicated lookup.

Do AI assistants read Shopify metafields?

Not directly. Assistants read the product page. Metafields only help if your theme renders them as visible text or structured data on the page.

Metafields or a year/make/model app?

Many stores use both: an app for the year/make/model lookup widget, and metafields or theme code to put each product's fitment in the page HTML and JSON-LD. Whichever you use, keep one fitment source.

Where should the fitment data come from?

From your supplier or manufacturer data, cleaned and structured. Don't infer fitment from titles or descriptions you can't verify; mark gaps and request the data.

See what AI assistants can read on your parts.

The free AI Readiness Check takes a product page URL or a CSV of up to 10 parts. You get a readiness score, fixes taken only from your own data, ready-to-paste JSON-LD, an improved CSV, a before-and-after AI answer test and an AI access check. No signup.

Whole catalog? Catalog cleanup fixes every SKU for you: $2–5 per SKU, one-time, $3,000 minimum. See pricing