H&M Looks to Big Data for Store Insights

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To reduce markdowns and break out of a lull in sales, H&M is turning to artificial intelligence (AI) and Big Data to tailor its merchandising mix in its brick-and-mortar stores.

The fashion retailer is using algorithms to gain insights from returns, receipts and data from loyalty cards to improve its bottom lines, according to news source Retail Dive reports. But just like the huge clothing inventory H&M possess; big data can be hard to handle without a form of AI automation in place. This will be moving data from on-premise servers to the cloud, which enables big data insights. Price reductions for storing big data are already declining while its processing speeds are doubling.

Big data and AI in H&M

H&M is utilizing the technology in a store located in an upscale section of Stockholm, Sweden. It has so far learned that women make up most of its customer base, and that fashionable items such as floral skirts have sold at better-than-predicted rates. Sales have improved with these insights, and H&M is moving away from the idea of stocking each location with a similar selection. That strategy previously led to unsold inventory and subsequent markdowns, as the retailer needed to clear out approximately $4 billion in excess merchandise.

Geoff Ruddell and Amy Curry analysts from Morgan Stanley wrote in a press that the scale of reduction will surprise H&M. They have categorized it as underweight back in January. It will leave the bears questioning why H&M still enjoys a growth stock rating.



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