How BI is helping to predict fashion trends

By , Computerworld |  Business Intelligence, Analytics, business intelligence

"Computer-aided fashion projections are something everyone is talking about," says David Wolfe, creative director at The Doneger Group, which predicts fashion trends the old-fashioned way: using seasoned experience and insight. But it's a high-stakes decision for merchandisers and fashion designers -- and one that can be tricky to get right. Fashion retailers stake their fortunes on the experience, intuition and gut instincts of an elite cadre of buyers. For smaller retailers, the effect of a buyer who loses his mojo can be devastating to the bottom line.

"Apparel is a very fickle business. If you miss one season, you can go under," says Aytaman. Most buyers simply don't trust technology to do the job. So they turn to consultants like The Doneger Group for predictions as to what colors and styles will be in -- and what will be out. Those insights, in turn, are based on experience, intuition and regular visits to designers and fashion shows.

Adding to the pressure is the fact that the consumer market has fragmented and shoppers are less willing to embrace styles dictated from the runway or by designers and retailers. Just 19% of consumers listen to manufacturers or retailers these days, according to an IBM survey. Consumers today tend to make their own decisions about fashion, in conjunction with their peers. More than ever, the industry needs to listen to the customer.

The Elements of Style

The problem with using predictive analytics to forecast fashion trends, says Aytaman, is that the accuracy of those predictions varies in direct proportion to the amount of historical data that can be fed into the model. So while Elie Tahari uses analytics to determine, for example, demand for its business-suit line, which doesn't change much from year to year, it doesn't use the technology to pick more seasonal, fashion-oriented items, such as dresses and sportswear.

"We can't accumulate enough history to really do something like this," he says.

While it's true that a new design may have no historical analog on which to model success, merchandisers can break down the key attributes that describe a given fashion -- everything from color to collar size -- and perform a regression analysis on those. In other words, merchandisers can perform a statistical analysis on all of the variables that describe the new style, assuming historical data is available, to project whether the item will be hot or not.

"Using attributes and supplementing that with what you see as fashion trends, again as attributes, is pretty cutting edge," says Saurabh Gupta, director of retail solutions at IBM. And while there may not be enough historical data to create models for every attribute, he says some fashion elements do have predictable cycles. "A color stays popular for a year at least, and you can derive insight from that," Gupta says.


Originally published on Computerworld |  Click here to read the original story.
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