Why product data is the last step of every season

Every consumer company runs on a calendar of seasons. Flower sales peak at Mother’s Day, and this year hit an all-time high. Dorm furniture and laptops are in demand in July and August, when back-to-school spending also set records. Ski gear sells in autumn, beauty sets launch before the holidays, and swimwear starts selling in late winter. Finance sets revenue targets around these seasons, merchandising decides what goes where and when to promote it, and marketing builds the campaigns. A well-planned season gives customers a reason to come back and keeps revenue steady all year.

Most seasons are built around new products, and getting one to market takes months. Teams design and prototype, hold reviews and approvals, and in categories like beauty and baby gear, run focus groups, trials or pre-sales. Nearly all that time goes into the product. The launch date is set by the season, so the last step gets whatever time is left.

By the time a product is ready to sell, the teams who designed and planned it are often on to next season. The final step falls to a smaller team on a tight timeline: publishing complete product information to the storefront, marketplaces and retail partners, where shoppers and AI agents decide what to buy. It means describing each product in detail, in the words shoppers search with. It also means keeping every attribute accurate and consistent everywhere it appears, for every new SKU.

The work grows with the catalog. One leading home furnishings company we work with enriched more than 1,800 SKUs to get ready for the holidays. And multi-brand retailers manage tens of thousands, often from supplier data with little more than a title, brand, color, SKU and part number, and sometimes no description. Either way, teams have to decide how much time this work gets while next season is already underway.

What thin product data costs

The most common result we see is product data that is accurate but thin. Fast-moving teams would rather publish a few correct details than risk wrong ones. That protects accuracy but leaves out much of what shoppers use to decide. When that information is missing, shoppers abandon purchases and return products.

AI shopping raises the cost. Say a shopper asks ChatGPT, Gemini or a personal agent for a waterproof winter boot that fits wide feet and costs under $200. The agent can only recommend boots whose data answers every part of that request, so a page that lists only color, size and price isn’t enough. Google now asks retailers for these details so its AI can match products to shopping questions.

Consistency matters too. AI engines and agents check a product page against what marketplaces and retailers say. If the dimensions or materials don’t match, they have less reason to trust the product and more reason to recommend one whose details line up.

New products have it the hardest. In their first weeks, they have no reviews, no sales history and few mentions elsewhere online. The data the company publishes is most of what an AI agent can learn about them. That matters more each season as AI traffic to retail sites keeps growing.

How Fabric shortens product onboarding and enrichment

Onboarding and enrichment is one of the places Fabric saves teams the most time, so they can spend it on the products and campaigns that drive the business.

Fabric starts from the Fabric Shopping 360, our index of product categories and attributes. It is built from the millions of SKUs we’ve processed and organized around the questions shoppers ask AI engines and agents. For each new product, Fabric gathers what the company or supplier already knows, from spec sheets and photos to descriptions and FAQs. It then drafts complete product detail in the brand’s voice. The merchandising team approves each draft before anything goes live. Fabric then publishes it to the product page, the structured data AI engines read, and the core catalog.

The results: two companies we work with cut a full enrichment cycle from about 12 weeks to about three, roughly 70% faster. For multi-brand retailers, Fabric has taken supplier products from four attributes to 40 selling features and conversational attributes, such as use case and occasion. One clothing brand enriched part of its catalog and left the rest unchanged as a control. Over six weeks, add-to-cart from Google Shopping rose about 60% on the enriched products, while the rest of the catalog stayed flat.

To see how your new products appear on AI channels, visit fabric.inc.