Use metafields as filters in Shopify Analytics

Most Shopify stores we audit already hold the data they need to answer good questions. It sits in metafields. Fabric, product range, supplier, loyalty tier, trade account type. Until now, getting that into a report usually meant an export and an afternoon in a spreadsheet.

Since the 12th of February 2026, Shopify Analytics can group and filter by metafields on products, variants, customers and orders. It works in Reports and in Explore. You choose which metafields to use by turning on Use in Analytics for each definition, under Settings, then Metafields and metaobjects. If your custom data is well kept, you can now analyse it without leaving Shopify.

What changed?

Shopify's changelog entry on metafields in Analytics adds your custom data as dimensions and filters. Shopify's own examples are material, loyalty tier and ingredients.

It covers four kinds of metafield.

• Products.
• Variants.
• Customers.
• Orders.

Nothing is switched on by default. You turn on Use in Analytics for each definition you want to report on. Shopify's Help Center has step-by-step guidance and a list of supported types.

What questions can you answer now?

It depends on what you store. A few examples from the kinds of stores we work with.

• Which material sells best, and does that change by season?
• Do customers in one loyalty tier reorder more often than another?
• Which product range drives most revenue, when ranges don't line up with product type?
• How do trade accounts compare with retail customers on order value?
• For food and drink, which ingredients or dietary attributes are growing?

Each of these used to need an export, a lookup and a pivot table. Now it's a filter. That means the question gets asked more often, and by more people on the team.

Why does metafield hygiene matter?

A report is only as good as the data behind it. Metafields often grow up without a plan. Different people add them for different reasons. Some are filled on half the catalogue. Some store the same idea in two places.

When those become dimensions, gaps show up as blanks and duplicates split your numbers. A report where a third of products say "not set" isn't telling you much.

Before switching anything on, check these.

1. Every metafield you want to use has a definition, with a clear name and type.
2. Values are consistent. "Merino" on some products and "merino wool" on others become two rows.
3. Coverage is complete for the products or customers you want to analyse.
4. Nobody is storing the same fact in a tag and a metafield.

Where the definition allows it, a fixed list of accepted values stops the inconsistency at source.

Product structure matters too. If each colour is a separate product, you'll want a metafield that groups them for reporting. Our note on variants or separate listings covers that choice.

Which metafields should you turn on first?

Start with the ones tied to a decision. A dimension you'll never act on is noise in the report builder.

• For merchandising, product attributes such as material, range or supplier.
• For retention, customer attributes such as loyalty tier or account type.
• For operations, order attributes such as delivery method or sales rep, if you store them.

Pick two or three, clean them properly and build one report around each. Add more once those are earning their place.

Give each report an owner and a question. "Which fabrics should we reorder for spring" is a better starting point than "a report on materials". The question tells you which metafield matters, which products need filling in first and who should read the answer. It also tells you when a report has stopped being useful and can be retired.

If you sell across borders, this sits well next to the cross-border profit reports Shopify added in October 2025, which break margin down by market. Wholesale stores can go further by recording trade attributes on customers and orders from the start. Our guide to what a Shopify ERP integration involves covers where that data usually comes from.

How does this fit a monthly review?

Reports matter when someone reads them and acts. That's the gap we see most on stores without an in-house analyst.

On our Growth subscription, we review analytics and customer behaviour every month and give three to five prioritised recommendations, tracked against a KPI baseline. Metafield dimensions make those reviews sharper. We can cut results by the attributes your business cares about, not only by product type or vendor.

The same applies if you run reviews in-house. Agree the few reports that matter, check them on a rhythm, and write down what you changed as a result. Log platform changes as well, such as Shopify's change to session measurement, so a shifted baseline isn't mistaken for a real trend.

Where parallel helps

If your metafields need tidying before they're useful, that's a focused piece of work. Our Shopify support packages cover it, with two monthly improvements from the Optimise package at £1,000 a month.

Want a hand deciding which metafields belong in Analytics? Book a call and we'll look at your data with you.