Elie Tahari, the upscale women's fashion brand and retail chain, has a pretty good idea which of its styles customers will want.
There's no wizardry, no crystal ball. The retailer relies on the science of predictive analytics, using technologies from IBM to forecast demand for its line, which it sells through Nordstrom and other high-end retail stores. The tools pull data from a continuously updated data warehouse to forecast what needs to ship to each store every week, right down to the styles, colors and sizes each location will need to meet demand.
"That protects the customer, ensuring that any style or color they order is in stock, but also protects us so we don't overproduce," says Nihad Aytaman, director of business applications at Elie Tahari.
Analytics have made an indelible mark on the retail fashion business over the past decade, helping with everything from predicting the best pricing and markdown strategies to forecasting the right mix of products, colors and sizes for every location. There's one critical area, though, that Elie Tahari and many other retailers and designers still don't use predictive analytics for: choosing which new styles will be next season's winners.
But thanks to new technologies, that could be changing.
"Maybe tie-dye is going to be huge or pink will be big. Those are decisions that the merchant has always made, but that can be assisted with sophisticated algorithms that point out patterns that [they] may have missed," says Cathy Hotka, principal of retail consulting firm Cathy Hotka & Associates.
Predictive analytic tools, which rely on historical data to make future demand projections for any given product, can play a role even in predicting the whims of fashion. But right now, the hottest area for picking fashion winners lies at the intersection of analytics and social media.
While predictive analytics can help identify fashion winners, most merchandisers aren't using the technology for that purpose, for two reasons: Unlike products that are carryovers or that will simply be revised for the next season, new fashions don't have the historical sales data that predictive analytic tools need to work their magic, and retail buyers are wary of allowing science to intrude on the art of picking fashion winners.
"For us right now, key styles are picked by merchants in their discussions with designers, who present products that are inspired by trends and what's happening in the world," says Louise Callagy, a spokesperson for Gap Inc. But Gap expects analytics to play a bigger role in the future. "Although it's in the early stages, we apply analytics from our early online sales globally and in certain markets to help gauge a better read of what we predict will sell in stores," she says.
"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.