H&M is betting on big data to help reverse its recent downturn. The Swedish fast-fashion chain, which in March reported a 44 percent slump in profits and the creation of a new off-price brand to sell off its excess inventory, is hoping to curb markdowns and court new shoppers by using algorithms to help determine what merchandise to stock in stores.
According to The Wall Street Journal, the retailer will analyze localized purchase data, online trends, social media posts and more to find the best assortment for each of its 4,288 locations.
H&M has already seen success testing the concept locally; it slashed the merchandise stocked in one of its Stockholm stores by 40 percent in late 2017, replacing basics and menswear with pricier fashion items and experiential details like a flower stand and a coffee shop, catering to a higher-end, mostly female customer base.
While the plan requires significant upfront investment (the brand is working with 200 data scientists, analysts and engineers, and tailoring merchandise deliveries to individual stores), ultimately executives hope it will reduce unsold inventory and put an end to the nonstop discounting and weak sales that have hurt the company’s bottom line for the past 10 quarters. While over the past three years, H&M’s shares have dropped 56 percent, they rose 1.92 percent to 147.50 SEK ($16.72) on Monday on the heels of the plan’s unveiling.
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It’s the latest in the company’s efforts to adapt to a changing, ever-more-digital retail landscape: This year, it announced the creation of a new affordable luxury brand, /Nyden, which will use the “drop” system popularized by streetwear brands to release new items and enlist influencers to codesign capsule collections.
