The company is turning to tech to build a stronger business, drive efficiencies in its supply chain and operations, and give consumers what they want thanks to the insights provided from big data and artificial intelligence about fashion trends and their customers’ preferences. Only time will tell if their investment is enough to catapult them out of their sales slump and if their bet on AI and big data will pay off. Here are a few ways H&M is using tech to their business advantage.
Data insights help avoid bad product cycles
About 20 years ago, fast-fashion retailers became disruptors that built strong businesses by trading in quality for better prices and fresh products. However, in order to succeed, fast-fashion retailers such as H&M need to predict what the market wants to avoid a bad product cycle and the reality of discounting inventory, even more, to move it out. Since the price points are already incredibly low for fast-fashion retailers, it's tough to recover from bad purchase decisions ant to move unwanted inventory. The stakes are high for fast-fashion retailers and the insights provided by data can help build a more flexible and faster supply chain, facilitate trend detection, manage inventory and set prices.
Inventory for individual stores
Previously, you could walk into any H&M store whether it was located in Sweden, the United Kingdom or the United States and it would carry very similar merchandise. Unfortunately, the retailer was continually faced with needing to cut prices to clear out unsold inventory in its 4,288 stores around the world. In an effort to better stock individual stores with merchandise local clientele desires, H&M is using big data and Artificial Intelligence (AI) to analyze returns, receipts and loyalty card data to tailor the merchandise for each store. This is known as localization and can be trickier to execute for a global chain such as H&M that typically can leverage economies of scale with its global network of suppliers.
Timely, incisive articles delivered directly to your inbox.