--- title: Get your trade product data ready for a Shopify build description: Shopify product data cleanup for trade and wholesale businesses. What to fix before the build starts, who owns each field, and how to keep it clean. slug: trade-product-data-before-shopify-build date: 2026-08-13 updated: 2026-08-13 service: b2b faqs: - question: How long does product data cleanup take before a Shopify build? answer: It depends on the size of the catalogue and how many people have edited it. A tidy range can be ready in days. A catalogue with years of drift needs a few weeks of structured work, which is still quicker than fixing it after launch. - question: Should product data live in Shopify or our ERP? answer: Usually the ERP holds codes, stock and price, and Shopify holds the selling content. The key is deciding which system owns each field and syncing in one direction. Two systems editing the same field is where errors start. - question: Can we clean our product data in a spreadsheet? answer: Yes, and for most teams it's the right place to start. Export everything, agree the structure, fix it in one sheet and import from there. The spreadsheet becomes the map for the build. --- On most trade projects we scope, the website isn't the hard part. The product data is. Titles typed three different ways, pack sizes hidden in descriptions and codes that only the warehouse understands. So what should you do before a Shopify build? Get the catalogue into one agreed structure first. Decide how products, variants and pack sizes work. Decide which system owns each field. Clean it in one place, then build the store around it. A design can't fix data it doesn't understand. ## Why the catalogue comes before the design A trade buyer searches by code, filters by specification and orders by the box. Every one of those actions depends on the data underneath. Filters need consistent values. Search needs the right codes. Quick ordering needs clear units. If the data arrives messy, the build slows down. Templates get designed around exceptions. Filters show five versions of the same colour. The team ends up fixing records one by one after launch, when every error is visible to customers. Businesses across Birmingham and the Midlands make and distribute physical products, and their catalogues tend to have grown over decades. We don't have an office there. What we do bring is the unglamorous part of the job, and our [Birmingham page](/locations/birmingham) says the same. ## What does clean trade product data look like? Clean doesn't mean perfect. It means consistent, so Shopify and your buyers can rely on it. - One naming pattern for titles, such as brand, product type, key specification and size. - A unique SKU for every sellable item, with no reused or blank codes. - Pack sizes and units recorded as data, not buried in the description. - Attributes held as structured fields, so they can power filters and specification tables. - Weights and dimensions filled in, because shipping rates depend on them. - Images named and linked to the right product and variant. - A clear status for every item, such as active, discontinued or made to order. That list looks basic. In practice it's where most of the time goes, because the answers live in different people's heads. Where items carry several codes, our guide to [multiple barcodes per variant on Shopify](/insights/shopify-multiple-barcodes-per-variant) covers how to hold them. ## Variants, separate products or both? This is the first structural decision, and it shapes everything after it. A bolt in six lengths is probably one product with variants. A tool range sold in single units and cases might need a different approach. Our guide to [variants or separate listings](/insights/variants-or-individual-listings-how-to-organise-your-products-on-shopify) covers the trade-offs for search, stock and browsing. Large technical ranges now have more room too, since [Shopify raised the limit to 2,048 variants per product](/insights/shopify-2048-variant-limit). More room doesn't make a giant product the right answer. It just means the limit is less likely to force your hand. We saw how much this matters on [Surrey Cricket Club](/work/surrey-cricket-club). Years of different product owners had left the old catalogue inconsistent. We restructured it on the way into Shopify so the team could manage it themselves. ## Who owns each field? Most trade businesses run an ERP or stock system alongside Shopify. Before the build, write down which system is the source of truth for each field. A typical split looks like this. | Field | Usually owned by | |---|---| | SKU and product codes | ERP | | Stock levels | ERP or warehouse system | | Trade prices | ERP | | Titles and descriptions | Shopify | | Images and selling copy | Shopify | | Filters and specifications | Agreed per business | Once it's agreed, data flows one way for each field. When both systems edit the same thing, the storefront starts to disagree with the warehouse. Our note on [what a Shopify ERP integration involves](/insights/shopify-erp-integration-guide) goes deeper on the sync itself. ## Keep pricing out of the product sheet Trade pricing is where catalogues get tangled. Customer-specific prices end up as extra columns, duplicate products or notes in a description. On Shopify, prices for trade buyers belong in company accounts and catalogues, not in the product record. The product holds the standard data. The catalogue decides what each buyer pays. On [Fáilte Foods](/work/failte-foods), every trade customer needed their own terms, and we built date-based pricing on top of a Business Central integration. Keep your cleanup focused on the product itself. Then map pricing as a separate job with its own rules. ## A practical order of work This is the sequence we follow, and it works whether you have 500 products or 50,000. 1. Export everything from every system that holds product data. 2. Agree the structure, including naming, variants, units and attributes. 3. Clean it in one master spreadsheet, owned by one person. 4. Fill the gaps, starting with the best sellers. 5. Agree field ownership between Shopify and the ERP. 6. Import into a development store and test filters, search and quick ordering. 7. Set simple rules so new products follow the same structure after launch. The last step is the one that protects the work. Without it, the drift starts again the week after go live. Clean, structured data also shapes how products appear through [Shopify Agentic Storefronts in AI channels](/insights/shopify-agentic-storefronts). Clean product data is the cheapest improvement you can make to a trade store. It also makes every other part of the build faster. See how we approach [B2B commerce on Shopify](/services/b2b). If the catalogue is what's holding your project back, [send us a brief](/start) with an export attached and we'll tell you where to start.