Walk into almost any fashion company’s head office and you’ll find data everywhere. Resource planning systems track every stock unit. Platforms log every customer interaction, down to each email opened. Ecommerce dashboards update by the minute. Warehouse systems know exactly what sits on which shelf, down to the last item.
Ask the same business a simpler question, though: what do your customers actually wear once they’ve bought it? Most people don’t have a clear answer. That uncertainty is the real problem in fashion right now (and a costly one).
The blind spot
Brands have become highly skilled at measuring the moment of purchase. What sold, what didn’t, what got marked down, what moved faster this week than last. What almost nobody measures is what happens after the sale. Whether a garment gets worn once and forgotten at the back of a drawer. Whether it turns into a genuine wardrobe staple, worn every month for two years. Whether it comes straight back because it never fit the way the product photos promised. None of that appears in a point-of-sale system. Yet all of it shapes what customers buy next.
Industry analysis from the forecasting firm WGSN and the consultancy OC&C has shown that more accurate demand data can shift margin by £1 million to £1.5 million on a single womenswear denim line, at a single retailer. Multiply that across a full range and a full year and the scale of what’s being lost through poor visibility into how clothes actually get used becomes hard to ignore.
Why this matters now
Overproduction and returns tend to be discussed as two separate issues, when in reality, they’re the same problem showing up twice.
Brands overproduce partly because demand forecasting still relies on sales history and trend cycles rather than any real sense of how clothing items perform once they leave the shop floor. Returns tell a similar story. UK online fashion returns are now estimated to cost the industry around £27 billion a year, with clothing return rates sitting near 23.6 percent and roughly a third of shoppers returning items on a regular basis. When a brand doesn’t know how something actually fits or wears, the customer ordering it online doesn’t know either. So they’ll often order two sizes, guess, and send one back. Repeat that across every order each week and a £27 billion bill stops being a surprise and starts looking inevitable.
Much of the industry’s response has been to push that cost back onto shoppers through return fees and closer monitoring of frequent returners. Given the pressure on margins, that outlook is understandable. However, this approach only deals with the bill, not the reason the bill keeps arriving.
The missing piece
As it stands, even a brand determined to close this gap has nowhere in its technology stack built to hold the answer. Enterprise resource planning systems manage operations. Customer relationship management platforms manage relationships. Ecommerce systems manage transactions. Marketing tools manage campaigns. Each does its own job well. None of these components connect in a way that translates real customer behaviour into a product or design decision
The problem, in other words, isn’t a shortage of data; fashion has plenty of that already. What is missing is a layer that sits between all those systems and the person actually wearing the clothes, turning genuine consumer behaviour into decisions about what to design, buy, or stock next.
What brands should do instead
Closing this gap is unlikely to come from buying another dashboard. It calls for something closer to a decision layer: a system that pulls together the signals already scattered across finance and banking tools, design software, marketing platforms, ecommerce providers and email, then does the harder work of judging what actually matters and what can be safely ignored.
The real value lies in that judgement, not the data collection itself. Picture a chief executive reviewing the quarter: what she needs is a handful of numbers that reveal whether the business is healthy, not a wall of spreadsheets. Now picture a merchandiser preparing for Monday’s trading meeting: what he needs is completely different, just the handful of product lines he’s responsible for and which ones are falling short. Same underlying information, but each person needs it filtered and shaped for the decision in front of them.
The same principle holds for customers. Most shoppers order two or three sizes of the same item online, planning to return whatever doesn’t fit, simply because they have no reliable way of knowing in advance. If fit and wardrobe information were captured once and carried across every brand a person shops with, rather than starting from zero every time, much of that guesswork, and the returns it creates, would disappear.
The bigger picture
Fashion has spent the last decade becoming extremely good at measuring transactions: what sells, when, and to whom. Almost no time has gone into measuring the thing those transactions are meant to serve, which is what people actually wear and for how long.
Until that changes, brands aren’t really designing from data. They’re designing in the dark, with excellent lighting pointing at all the parts that don’t matter.
