Product Data in Equestrian Retail: Structuring Gear for Horse and Rider

One niche, two sizing worlds: gear for the horse and gear for the rider — with many small brands, almost no standard and a lot of supplier Excel to enrich.

Jakob Feinböck, ProductbayJuly 4, 20267 min read
☝️Key takeaways
  • Equestrian retail carries a double sizing logic: gear for the horse (rug length, bridle size, bit width) and gear for the rider (clothing sizes, boot calf-width and shaft-height) — two fitting worlds in one shop.
  • There is almost no industry standard and no central data pool; the sector is full of small niche brands, so supplier data arrives as raw Excel and PDF.
  • That means a high enrichment need — attributes, descriptions and categories mostly have to be built from manufacturer files.
  • Productbay uses flexible attribute groups plus AI enrichment to hold horse and rider data side by side and turn supplier Excel into shop-ready listings.

Few retail niches pack as much fitting complexity into so few square metres as equestrian. In the same order a customer buys a turnout rug measured by back length in centimetres, a snaffle bridle sold as pony/cob/full, a bit specified by mouth width in millimetres — and then a pair of riding boots that need the right calf width and shaft height, plus breeches in a normal clothing size. Every article you list has to answer the question: does this fit the horse, or does it fit the person?

Product data in equestrian retail is defined by a double sizing logic — one for the horse, one for the rider — layered on top of a niche with almost no shared standard. That combination is what makes the sector so data-heavy, and it's the story of this article. Equestrian sits as a niche discipline under the broader sports & outdoor retail challenge, and shares plenty with the animal-side data problems of pet supplies.

Why does equestrian carry a double sizing logic?

Most product catalogs have one sizing dimension per article. Equestrian routinely has two — the horse and the human — and they don't share a scale. A single mid-size shop is maintaining several parallel fitting systems at once:

  • Horse gear: rugs by back length (cm), bridles in pony/cob/full, bits by mouth width (mm), saddle pads and girths by their own sizes, boots and bandages per leg.
  • Rider apparel: classic clothing sizes for breeches, jackets and shirts — a normal fashion variant matrix of size and colour.
  • Riding boots: a fitting run of their own — shoe size plus calf width plus shaft height, closer to footwear pain than apparel.
  • Consumables and stable: feed, care products and stable equipment with yet another attribute set entirely.

The core multi-supplier problem — consolidate, normalize, enrich and publish — is amplified because a single catalog has to model these fitting worlds side by side without collapsing them into one wrong template.

Is there an industry standard — or is it all Excel?

This is where equestrian differs sharply from something like bike or ski. There is no dominant classification and no central data pool covering the assortment. GTIN/EAN identifies articles, and FEDAS merchandise groups touch the sport-retail side, but neither carries the tack attributes, the double sizing, the sales content or the images.

The practical consequence:

  • The sector is fragmented across many small niche brands, each delivering data its own way.
  • Supplier data almost always arrives as Excel spreadsheets or PDF catalogs, rarely a clean structured feed.
  • Because no standard fills the gaps, the enrichment need is high — attributes, categories, descriptions and translations mostly have to be built from the raw file.

Here's an honest read of what does and doesn't help:

Data layerWhat standards / pools deliverWhere it stops
Article identityGTIN/EAN keys the productNo attributes, sizing or content attached
Merchandise groupingFEDAS touches the sport-retail sideNo dedicated equestrian tack depth
Central data poolNone for equestrianEvery brand is on its own
Double sizingNot modelled by any standardHorse and rider runs built by hand
Sales contentNever part of a classificationDescriptions, SEO text, images absent

In short: there's no standard riding in to rescue this niche. The data structure and the content are yours to build — which is precisely why the right tooling matters so much here.

How does Productbay help in equestrian retail?

Two capabilities do the heavy lifting: flexible attribute groups for the double sizing, and AI enrichment for the pile of supplier Excel. That's exactly what Productbay is built for:

  • Flexible attribute groups: a rug carries back-length and neck-cut, a bridle carries a pony/cob/full run, a bit carries mouth-width, and rider apparel carries its own clothing-size and boot-fitting logic — each product type gets exactly the attributes it needs, all in one catalog. Horse gear and rider gear live side by side without being flattened.
  • Consolidate: import every source once — supplier CSV, Excel, PDF catalog, feed URL, FTP, API — and match by SKU or EAN/GTIN so existing products update and new ones are created.
  • AI enrichment: AI reads attributes out of titles and PDF datasheets, writes descriptions, assigns categories, fills gaps from whitelisted sources and translates via DeepL — always with a review queue before anything publishes. This is where the small-brand longtail finally gets usable content.
  • Publish: two-way sync to Shopify and Shopware, ERP connections (Xentral, weclapp), and feed exports for Amazon, OTTO and Kaufland — each with per-channel transformations.

Because no standard does this work for you in equestrian, Productbay is built to do it directly on the raw supplier files. It's made for specialist retailers running multi-supplier, multi-channel catalogs — and a fragmented niche like riding sport is exactly the case it was designed to absorb.

Frequently Asked Questions

Let's look at your product data process

Horse and rider, rugs and riding boots, dozens of small brands and stacks of supplier Excel — equestrian is a niche of its own. See how Productbay's flexible attribute groups and AI enrichment turn it into one clean catalog in a 30-minute walkthrough.

Get started