How BI is helping to predict fashion trends

Manya Mayes, director of predictive analytics at Attensity, says text analytics are being used on data provided from social media sites such as Storify, which lets online users create their own visual stories about what outfits they like. "The analytics identify which clothing combinations are put together most often and which ones they are keeping," she says.

Merchants are also mining "fashion haul" videos, in which teens show off goods they bought at the mall and voice strong opinions about them. Some fashion haul posts have gone viral, with as many as 1 million hits in the first week, says Jill Puleri, vice president of global retail at IBM, citing videos by young women named Blair Fowler, Ellie and Fiona. "That's something you can input into your trending models," she says.

Predictive analytics reduces the overall risk on fashion selections, allowing the business to take some chances, says Schmults. "The art is introducing things that consumers wouldn't have thought about before," he says.

Crowdcast offers a different spin on collective intelligence. Its service lets employees within an organization, such as buyers, store managers or employees, bet virtual money on which products will be winners.

"The collective wisdom of several merchants is usually better than the single estimation of one," says Greg Girard, an analyst at IDC. In the Crowdcast model, participants win more money when they're right, allowing them to place bigger bets, which gives them greater weight when all bets are tallied. In this way, he says, a group of buyers can all bet on this season's line of clothing.

So far, most users have been manufacturers, which use the tool to predict when products will ship or how well they will sell, but Crowdcast is pitching it to fashion retailers. "When you have very little data to make big decisions, that's where you can benefit from collective intelligence expertise," says Mat Fogarty, the company's founder and CEO.

Timing is another challenge. It's not enough to know that a fashion item keys into a popular trend, says Company Z's CIO. Retailers need to know when those trends will hit. Company Z uses crowd-sourcing and collective intelligence tools similar to what First Insight and Crowdcast offer. But it also does test marketing in stores and through its e-commerce channel and then feeds the results into its data warehouse, where it's used as additional input for its predictive modeling engine.

"Predictive analytics doesn't change the way we run our business," the CIO says. "All it does is streamline the processes so we're more analytical."

Pulling It Together

The impressions and insights from social media analytics can be fed into traditional predictive analytic engine models, providing another input to help determine fashion winners, says IBM's Linsky. First Insight's data can fit within predictive analytic data models, says Petro. "It's just a matter of mapping it," he explains.

Going forward, social analytics will reshape the merchandiser's job into "social merchants," says Girard. But for now, using analytics -- social or otherwise -- to pick fashion winners is still a "missionary market," with many retailers still on the sidelines, merchants and designers not completely sold on the idea, and everyone waiting for the first big success story.

As for cultural resistance, Petro thinks the technology will gradually win over merchants as they see the results and understand where the tools fit. Predictive analytics is no substitute for human judgment, he says: "It's an instrument in the cockpit, not a replacement for the pilots themselves."

This story, "How BI is helping to predict fashion trends" was originally published by Computerworld.

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