"Which designer sells best in Germany?"
Until recently, answering that in Shopify meant an export, a spreadsheet and a free afternoon. Now it's a report.
On September 28, Shopify made metaobject fields available in Analytics. If you've structured your product data properly, you can now group and filter reports by the things that actually describe your business. Designer. Material. Collection drop. Loyalty tier.
If you haven't, this is a good reason to start.
First, what is a metaobject?
A metafield is a single extra field on a product, like "Fabric: merino wool".
A metaobject is a small record with its own fields, which many products can point to. Take a designer. Instead of typing the designer's name into every product, you create one Designer entry with a name, a country, a bio and a portrait. Then you link products to it.
Change the designer's bio once, and it updates everywhere. That's why well-built Shopify stores use metaobjects for designers, brands, materials, ingredients, care instructions and size guides.
Until now, all that structure helped the storefront. It didn't help your reports.
What changed in Shopify Analytics
Back in February, Shopify let you use custom metafields as dimensions and filters in Analytics. This update goes one level deeper.
If a metaobject is attached to a product, variant, customer or order through a metafield, the metaobject's own fields can now be used in reports. In practice:
- Group reports by metaobject fields. Open a report, choose Metaobjects, then the resource, the definition and the field. For example: Product, Designer, Country.
- Filter to a single value. Sales from designers based in Italy. Orders from customers in your top loyalty tier.
- Use them in the query editor. The same fields work when you write your own queries in ShopifyQL, Shopify's reporting language.
Your existing metaobjects and metafields don't change. Custom metafields and category metafields stay in the field picker, each in their own group.
The questions you can now answer
This is where it gets useful. A few examples of reports that used to need a spreadsheet:
A furniture or interior brand. Sales by designer, then by designer country. Are Danish designers outperforming Swedish ones in your German market?
A fashion brand. Revenue by material. Is the merino range pulling its weight against the cotton one?
A beauty brand. Sales by key ingredient. Is retinol growing or flattening this quarter?
Any brand with a loyalty program. Order value by customer tier, if the tier lives in a customer metaobject.
None of these are new questions. What's new is that your merchandising and marketing teams can answer them in Shopify, on their own, the same day they think to ask.
Two settings worth checking
Your own metaobjects are switched on automatically.*Every merchant-owned metaobject definition is already available in Analytics. If some of them are irrelevant for reporting, like a size guide or a shipping notice, you can turn them off to keep the field picker tidy. Go to Settings > Metafields and metaobjects, open the metaobject and change Filter or group data in Analytics.
Metaobjects created by apps are switched off. If a review, bundle or product-info app created the definition, it stays out of Analytics until you or the app turn it on. Worth a quick look. Some of your most useful data might be sitting behind that switch.
The catch: your reports are only as good as your data model
This update rewards stores that structured their data properly. It does very little for stores that didn't.
If your designer lives in a product tag, a vendor field used for something else, or a line of text in the product description, Analytics can't see it. Same story if the data is half filled in or spelled three different ways.
So the real question is whether your product data is built to be reported on. A few signs it isn't:
- The same attribute is stored in tags on some products and metafields on others.
- Metafields hold text that should really be a reference to a shared record.
- Nobody on the team is quite sure which fields are still in use.
Fixing that is rarely glamorous. But it's usually a few weeks of focused work, and it pays off everywhere: storefront filters, search, translations, and now your reports.
Our take
We've long pushed clients toward a proper data model: metaobjects for anything shared across products, metafields for anything specific to one. Until now, the payoff was mostly on the storefront and in day-to-day merchandising.
Now it shows up in the reports your leadership team reads. That makes a data model much easier to justify.
If you're not sure whether your product data is ready for this, send us a list of your metafields and metaobjects. We'll tell you what we'd keep, what we'd restructure, and which reports you could run next month. No pitch. Just a second pair of eyes.
