The retail industry, like many others, is being disrupted and permanently changed by the ever-increasing capabilities and applications of artificial intelligence (AI) and machine learning (ML). As I wrote in my last post, we’re just beginning to see the benefits of AI and ML implementations in the retail sector, but they’ve already helped forward-thinking companies shorten time to market, increase margins, and better align their offerings with customers’ preferences and expectations.
Retailers and brands, analysts and research firms are in agreement that AI and ML applications will significantly aid in product development, selection, planning and buying in the near future. They also agree that successfully implementing these solutions will require major organizational changes. However, the pain of change is worth it. Automating functions through AI not only frees humans from having to perform mundane tasks, but can also affect everything from product launch success to demand forecasting, areas where even minor improvements can have major effects on profit margins compared with older, more convoluted processes.
According to a recent study from the IBM Institute for Business Value in collaboration with the National Retail Federation, 85 percent of retail companies and 79 percent of consumer product companies plan to use intelligently automated supply chains by 2021. Furthermore, 79 percent of retail and consumer product companies plan to integrate intelligent automation to gain customer intelligence by 2021.
Although the terms “artificial intelligence” and “machine learning” may call to mind robotic reasoning, it’s paramount that humans oversee and work in alignment with these technologies, and that retailers and brands work to maintain a personal touch with customers. A recent article from CIO Review noted that an “excellent customer experience is achievable only when the retail[er] starts developing a personal selling relationship with the consumer.” Successful retailers and brands understand the need to have a strong personal relationship with customers, but few companies fully recognize how AI and ML can help them build and strengthen those bonds.
One benefit of these applications is that they can identify purchase patterns that would be difficult for humans to spot. For example, ML can identify patterns in customer purchasing behavior and predict what items are typically bought together. Home Depot is one retailer that has integrated ML to better understand the needs of its customers. The home improvement company’s mobile app uses ML to identify the type of project a customer is working on, and then provides product recommendations and project guides to assist them. This technology helps customers find what they’re looking for more easily, enhances their experience, and boosts revenue for Home Depot.
AI and ML can help retailers and brands in many other ways, too, including making decisions about pricing, planning, store assortments, merchandising, and choosing what’s sold online. However, the data these applications draw on must be all-inclusive, customer-informed intelligence. By concentrating on the customers they already have, retailers can access an invaluable sample of the habits and desires of their entire potential customer base. Using voice-of-customer data, including information gleaned from social media content, product reviews and surveys, as inputs for AI and ML applications can help ensure retailers get the most out of their investment in these technologies.
These applications should be a priority for retailers and brands, which can use existing AI and ML infrastructure to get ahead of their competition before the whole industry is transformed. Transparency is important, and companies should ensure that both employees and customers understand what data are being gathered and how they’re being used in AI applications. Doing so will help to maintain trust both internally and externally. In the case of store associates, a better understanding of AI and ML technology will allow them to build even stronger relationships with their customers. Those relationships, built on a deep understanding of what customers want and stewarded by advanced ML technologies, will continue to be one of the keys to retail success.
Jim Shea is chief commercial officer for First Insight, the leading customer-centric merchandising platform used by retailers and brands worldwide.