AI Case Study
H&M improve single store sales by improving inventory planning through machine learning to discover trends on social media
H&M is beginning to use machine learning to analyse social media and online content for trend discovery, leading to better inventory and pricing planning.
Consumer Goods And Services
"H&M, like most retailers, relies on a team of designers to figure out what shoppers want to buy. Now, it’s using algorithms to analyze store receipts, returns and loyalty-card data to better align supply and demand, with the goal of reducing markdowns. To detect trends three-to-eight months in advance, H&M is analyzing data on a large scale from blog posts, search engines and other sources rather than relying mainly on staff. With the help of about 200 data scientists, analysts and engineers—internal staff and external contractors—H&M also is using analytics to look back on purchasing patterns for every item in each of its stores. The chain uses algorithms to take into account factors such as currency fluctuations and the cost of raw materials, to ensure goods are priced right when they arrive in stores. But H&M’s strategy of using granular data to tailor merchandise in each store to local tastes, rather than take a cookie-cutter approach that groups stores by location or size, is largely untested in the retail industry, consultants say."
"H&M retail chain is ramping up its use of data to customize what it sells in individual stores, breaking with its longstanding practice of stocking stores around the globe with similar merchandise. The world’s largest clothing brand [4,288 stores] is turning to artificial intelligence to win back shoppers, as it works to reverse one of the worst sales slumps in its history."
"The company says sales at [one Swedish] store—an early adopter of the technology it has begun rolling out globally—have improved significantly but declined to provide figures."
"H&M analyzes blog posts, search terms, and social media using machine learning, natural-language processing and image recognition. Algorithms analyze inputs including historical sales on every product in every store and online trends, to arrive at unique sales predictions per product and store."
"The data pool includes information collected from five billion visits last year to its stores and websites, along with what it buys or scrapes from external sources [social media]."