The arrival of Big Data and the ability to process it have led to improvements in machine learning, creating new use cases for this technology. As a result, machine learning, algorithms that execute tasks without preprogrammed rules, has become a crucial part of a retailer’s operations. Some of the most important use cases include better personalization, lowering operational costs and enabling the future of commerce. It will be important for retailers in 2018 to determine how to deploy this technology to drive revenues and lower costs.
Improving the Customer Experience through Personalization
One of the first applications of machine learning was for product recommendations. The improvement in machine learning means that not only have the recommendations become better, but other types of personalization are possible. Many retailers offer personalized search results to shoppers based on their past purchase history and other behavior. eBay has personalized its home page for shoppers with the aim of reducing the time it takes to find an item.
Another area of personalization is price optimization. Groupon Goods uses the Boomerang Commerce Price Platform management tool to optimize its prices. Groupon looks at external factors, such as consumer demand and market elasticity, and at internal factors, such as traffic and sell through targets, to optimize its prices for shoppers.
Decreasing Operational Costs
Machine learning is tackling tough operational challenges to lower costs. One of the best use cases is for inventory management and forecasting. Machine learning programs can make predictions on the optimal amount of inventory to avoid out of stocks or too much inventory. This helps retailers avoid lost sales and markdowns.
" Machine learning programs can make predictions on the optimal amount of inventory to avoid out of stocks or too much inventory "
Another area where machine learning is cutting costs is with the aim of reducing apparel returns. About 30 percent of online apparel purchases are returned. Not all of the merchandise can be resold at full price and the remainder makes its way through an expensive liquidation process. Retailers are turning to machine learning to help shoppers better understand the fit of clothing and apparel to help cut down on a reason for a return. Bold Metrics is a company that provides a machine-learning fitment tool to retailers. The shopper answers four questions about their height, weight, age and either jean size for men or bra size for women. It uses that information in its machine learning to accurately predict the correct size for a brand. The retailer, SnapSuits, uses one of the products and has a return rate of only 13 percent—lower than the average for its category.
It’s also being used to make the supply chain more efficient. Machine learning can determine the type and amount of inventory needed for each warehouse so that the product mix is accurate and can be shipped at a lower cost to appropriate shoppers. Kohl’s is using machine learning to determine how to fulfill an order from one or multiple stores or its fulfillment centers to lower the fulfillment cost per order.
Enabling New Types of Commerce
Machine learning is behind the next forms of commerce. In his 2016 shareholder letter, Jeff Bezos pointed out some of the ways that Amazon uses machine learning in its business, highlighting the AmazonGo and Alexa. Machine learning powers the computer vision that allows the AmazonGo store to be cashierless and powers the natural language processing behind Alexa that allows shoppers to use voice to shop.
General Motors and IBM are partners behind GM’s connected cars with the program, Marketplace. The inclusion of IBM Watson means that the program will learn the driver’s routes and habits to provide recommendations and reminders. For example, it will know if the driver missed his usual weekly shop and recommend that he place an order. Another interesting use case is for click and collect orders. The program will allow retailers to see the driver’s route to their stores and direct the driver to the correct collection point.
Moving Forward with Machine Learning
In 2018, it is important for retailers to understand how to use machine learning in their operations. With numerous use cases available, a retailer will need to evaluate which ones are a priority to the business. Once those are established, retailers will need to know if they have enough data for machine learning to be effective. Without sufficient data, machine learning will not work when built in-house. A retailer can work with third party vendors to overcome this but that means the retailer’s data commingles with other retailers’ data. The machine learning program gets smarter, benefiting all on the platform, including competitors. It will be important for retailers to decide whether it’s worth possibly helping competitors to leverage a better machine learning program.
The final piece is to keep an eye on the bigger picture. Machine learning is enabling new ways for shoppers to shop, such as through voice assistants and cars. There are likely many more experiments underway. It will be necessary to for retailers to track new technologies that can enhance or disrupt their business.