Home NEWS Science Machine Learning Helps Retailers Sell More Fashions Customers Love

Machine Learning Helps Retailers Sell More Fashions Customers Love

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Thanks to social media, everyone from middle school children to seasoned clotheshorses can see what celebrities and other opinion leaders are wearing, challenging retailers to keep up with fast-changing trends. Cue machine learning, allowing fashion retailers to immediately get a handle on the colors and styles that are selling when demand is peaking, and before it’s too late to bring it into stores. During our onsite VIDEO conversation, Hannah Vogt, Knowledge Management for SAP Leonardo Machine Learning Foundation at SAP, showed me an example at the recent SAP TechEd Barcelona event


Machine learning helps retailers bring #fashions into stores when consumer demand is peaking @SAPLeonardo @SAPTechEd

Faster time to market

Using machine learning, managers could in seconds find out what colors are trending in Paris, and likely to cross the ocean to the United States, staying one step ahead of increasingly fickle and demanding fashion consumers.

“Managers can react faster to match trends, triggering production to adjust inventory in stores that’s more in synch with up-to-the-minute fashions,” said Vogt. “There’s no time to lose in the current market, and you have to move faster than the competition in order to survive.”

Intelligence empowers shoppers

Analysts at Forrester Research predict intelligent agents will directly influence 10 percent of purchase decisions this year alone. That figure is certain to rise over time as machines get smarter and collect more information about market likes and dislikes, based on everything from what opinion leaders are wearing, to which fashions are flying off the racks in any location.  Retailers able to capture these insights faster than the competition will emerge strongest. In the lightning-fast fashion world, one thing is certain – machine learning is here to stay.

This blog was originally posted on the SAP News Center.

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