H&M: AI-driven Retail
“AI democratises people’s sense of being unique.”
“Flying high with AI: dealing with data the best possible way”
In the age of social media, fashion companies have less power to drive trends, which come and go much more quickly as influencers promote their “outfit of the day” on Instagram.
That poses a particular problem for H&M, which produces most of its garments in Asia, far from its major markets, making it less responsive than its rival Zara-owner Inditex which boasts it can get new designs to its stores within a week.
Sportswear brand Adidas admitted it had been caught flat-footed when its suppliers failed to keep up with strong U.S. demand for its mid-priced clothing ranges.
H&M had seen stocks of unsold goods pile up over the past several years.
In this data-driven age, it would be impossible for human beings alone to manage a family of global retail brands like H&M.
As H&M’s online retail revenue accounted for an increasing proportion of the company’s total revenue, the company’s management realized that they had to adopt a new approach, maximizing the use of big data and advanced analytics, and constantly inventing new algorithms to help fulfill their customers’ needs.
They established a brand-new cloud-based IT infrastructure and set to work on a series of AI and Machine Learning use cases, realizing their value and proving that they worked.
H&M Group’s Advanced Analytics and Artificial Intelligence unit now produces fully-fledged analytical software platforms for implementation across the globe.
H&M uses artificial intelligence for category prediction. The biggest influencing factor for the development of apparel companies is the accuracy of their judgments on fashion trends. Generally, companies plan half a year in advance which styles the brand needs to display this season. However, how do you judge and predict fashion trends if you determine the style of clothing half a year in advance? The traditional method is to track the past trends of each season, then consider new styles, and finally, brands can make decisions based on these aspects.
And now, the development of social media has changed the meaning of fashion-even the world’s largest clothing brand and clothing store are struggling to keep up with the trend. Therefore, considering only historical data to make decisions about the current situation is an outdated method. The entering of AI can bring considerable changes to the apparel industry.
“The company produces large quantities of garments in a year,” Koolmeister said. “We work on AI use cases that tell us how we can quantify fashion, allocate it and make it personal for each individual customer.”
Here are some tangible examples thanks to implementing AI solutions.
Keeping popular items stocked — H&M relies on staying on top of trends in order to be successful. With the help of algorithms, they analyse store receipts and returns to evaluate purchases in each store. This way, the fashion brand knows which items to promote and stock more of in certain locations.
Predicting market demand — Fashion retailers like H&M rely on fresh products at competitive prices. Data insights help H&M to predict what the market wants so they don’t have to discount their inventory to sell it out.
Automated warehouses — Today customers expect fast, hassle-free deliveries anytime and everywhere. Therefore, H&M Group has invested in automated warehouses that will ultimately offer next-day deliveries for the majority of the European markets. The warehouses and their free shipping, exclusive for loyal customers, are driven by algorithms and data.
Personalised offline customer experience — H&M has introduced its personalised online recommendations also in their physical stores, with the help of RFID technology. Customers get in-store merchandise suggestions selected by algorithms. They can also see if an item they have seen online is available in a physical store, and scan labels to see if an item is available in another store or online.
Tailor-made clothing — Partnering with an AI-technology platform, the Swedish fashion brand has tested on-demand production, which shows great potential to react more specifically to customer’s wishes, and to align product quantity to local demand.
Evaluation
AI algorithms can analyze the product categories of competing brands and compare these products with the customer’s demographics and shopping history to predict the most relevant items to add to the retailer’s inventory.
Big brands like H&M have realized the importance of using AI in product structure planning. H&M aims to predict trends months in advance. The retail giant have hired more than 200 data scientists, analysts and engineers to use AI to review the purchasing patterns of every item in every store.
This data not only contains all the information on the 5 billion visits to its stores from 2019 and the traction on its website, but also from external sources. H&M’s 4,958 stores around the world have adopted AI-driven merchandising approach and did a good job, breaking the stereotypes and making loyalty drive to ride fashion cycle, which can be an excellent exsample for other retails and big brands.
