Product Data for Dog Supplies: Food, Accessories and Sizes

Two data worlds in one aisle: labelling-regulated food with analytical constituents, and accessories with size and colour variants — where GDSN helps, and where it stops.

Jakob Feinböck, ProductbayJuly 4, 20267 min read
☝️Key takeaways
  • Dog supplies split into two data logics: labelling-regulated food (analytical constituents, feeding guide) and size-driven accessories (harnesses, collars, beds) — two worlds in one catalog.
  • GDSN delivers clean master data for the big food brands — but not the regional food, treats, supplements or the sized accessory longtail.
  • Accessory sizing is inconsistent between suppliers (girth in cm vs. XS–XL), so most of it still arrives as Excel and PDF by hand.
  • Productbay holds food attributes and size variants in one system and uses AI enrichment exactly where GDSN stops: the sized longtail.

Walk one aisle of a pet shop and you cross two completely different product worlds. On one shelf: a 12 kg bag of dog food with a printed table of analytical constituents, a composition list and a feeding guide keyed to the dog's weight. On the next: a harness that exists in five sizes and three colours, a bed in three sizes, a collar in a length run. Same category, same supplier catalog — two completely different ways product data behaves.

Product data for dog supplies is split between two logics: labelling-regulated food and size-driven accessories. Food is a flat, attribute-rich record; accessories are a variant matrix. A setup built for one always leaves the other half underserved. This is a sub-branch of the broader pet supplies challenge, and the mixed data world here is exactly what makes it hard.

What makes product data for dog supplies so difficult?

The core problem every multi-supplier retailer knows — no two suppliers deliver alike — is amplified here because you're juggling two data logics at once:

  • Food: attribute-rich and regulated. Analytical constituents (protein, fat, fibre, ash), composition, feeding recommendation, GTIN, pack size, best-before handling — a deep flat record that has to be accurate because it's labelling-relevant.
  • Accessories: variant-heavy. A single harness explodes into a matrix of sizes and colours; a bed into sizes; a collar into a length run. The size grid lives separately from the image and description.
  • Inconsistent sizing: one brand sizes harnesses by chest girth in cm, the next uses XS–XL, a third has its own breed-based chart. There is no shared size standard to map against.
  • Mixed baskets per supplier: a single distributor may ship you food and collars and supplements in one Excel — flat records and variant matrices interleaved, with columns that only apply to half the rows.

Do this by hand and it doesn't scale. The fix is the same as everywhere: consolidate, normalize, enrich and publish — but here you have to do it for both data worlds simultaneously.

Which standard applies — and where does it stop?

On the food side there is a real standard: GDSN (Global Data Synchronisation Network), the pool the big pet-food brands use to publish master data with GTIN keys, pack sizes, analytical constituents and feeding guides. GDSN is genuinely useful for the branded food core. But it's important to be honest about what it does and doesn't reach:

Data layerWhat GDSN / brands deliverWhere it stops
Branded food master dataGDSN records for the big brands (GTIN, pack, constituents)Nothing for regional / own-brand food outside the pool
Treats & supplementsPartial, brand-dependentSmall-brand snacks and supplements arrive as Excel/PDF
Accessories (harness, collar, bed)Not a GDSN use case in practiceSize/colour variants entirely manual
Size normalizationNo shared accessory size standardcm-girth vs. XS–XL vs. breed chart — you map it
Sales contentNot the job of a data poolDescriptions, SEO text, benefit copy absent

In short: GDSN covers the food core of the big brands well. What it doesn't give you is the regional and own-brand food, the accessory size matrices, a way to normalize inconsistent sizing, or any sales content. That's the gap — and in dog supplies the gap is most of the SKU count.

How does Productbay help with dog supplies?

The throughline is a three-step job, run for both data worlds at once — and that's exactly what Productbay is built for:

  • Consolidate: import every source once — GDSN export, supplier CSV, Excel, feed URL, FTP, API — and match by SKU or GTIN/EAN so existing products update and new ones are created. Food records and accessory size matrices land in one catalog.
  • Enrich: AI writes descriptions, assigns categories, normalizes inconsistent size labels into one scheme, fills missing attributes from whitelisted sources, translates via DeepL, and can read constituents and specs out of PDF datasheets — always with a review queue before anything publishes. This is where the accessory and supplement 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, and labelling-relevant food fields kept intact.

Crucially, Productbay starts where GDSN ends. If the big food brands already feed clean records via GDSN, great — Productbay complements it and takes over the regional food, the supplements, the accessory size matrices and the sales content no data pool provides. For the wider view across every animal, see the pet supplies overview. Productbay is built for specialist retailers running multi-supplier, multi-channel catalogs — from mid-sized shops to large chains — and the whole product data process lives in one place.

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Food and accessories, GDSN core and sized longtail, feeding guides and harness size runs — dog supplies packs it all into one catalog. See how Productbay consolidates, enriches and publishes both data worlds in a 30-minute walkthrough.

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