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.
