The Art and Science of Fashion

The combination of predictive analytics and social media is helping retailers anticipate the whims of fashion -- but it's not yet a substitute for expert human judgment.

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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, a senior solutions specialist at SAS, who is responsible for product management of retail solutions. 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. (Editor's note: An earlier version of this story incorrectly identified Gupta as a director at IBM.)

And retailers can enhance models with knowledge, such as the fact that certain types of fabrics are becoming less attractive to buyers. "It's about bringing in extra evidence, not one killer attribute," says Colin Linsky, predictive analytics worldwide retail sector leader at IBM. But the real value of predictive analytics in fashion is not just that it can pick winners, Linsky says. "It also gives a strong indication of the why, and that's important in understanding what you should be doing when making merchandising decisions," he says.

On the other hand, predictive analytics doesn't always work as well when a new fashion doesn't follow previous patterns, when there's limited or no historical data for key attributes, or when the style falls into a different line, such as when it moves from dresses to sweaters, says the CIO of a large fashion designer and retailer that sells online and through more than 500 stores, who spoke on the condition that his name and company (we'll call it Company Z) not be identified.

"Someone has to model that based on their knowledge, and that's where the art of merchandisers comes into play," the CIO says. "You still hear in the buying meetings, we believe this will happen. This is the forever battle of science versus art."

But none of this will work, he says, unless the right systems are in place to supply the same data, consistently, to all parts of the business. At Company Z, that means having a master data model and an enterprise service bus to move the data between subsystems, and to share data across sales channels and buyer silos. And final validation requires human review and approval across all functional areas, including plan allocation, production sourcing and finance, as well as approval by the merchants.

"At the end of the day, if you don't have good data you use across the enterprise, the results aren't the same," the CIO says. "That's very important to predictive systems."

The CIO's company isn't the only retailer doing this, but it's ahead of the curve, according to IBM's Gupta. "Everyone says they understand attributes, but how to use them to predict demand is not something a lot of companies do well."

Mining Social Intelligence

To augment traditional analytics, some retailers and fashion designers have applied analytic techniques to social media interactions to get real-time feedback on where fashion is going and what consumers think of their upcoming designs.

Social analytics are changing the game in retail, says Doug Stephens, president of research consultancy Retail Prophet. "We're moving from an outside-in approach, to a world where inventory and demand planning and product development will all be driven by social media," he says.

At one large retailer that creates its own fashions, designers use the feedback in an iterative loop to evolve fashion items, tuning each for the most enthusiastic consumer response, according to an IT executive who spoke anonymously.

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