4 Applications of Machine Learning (ML) in Retail
The retail industry has benefited greatly from the advancements of machine learning (ML), which have the ability to improve a company’s bottom line. The technology can do so by improving the retail experience for consumers with a better user interface, a personalized recommendation engine, the optimization of stocking and inventory and to more accurately price an object. Many companies have already shifted towards a more digitized platform in order to have a better understanding of when to push products more aggressively and when to use more tact with customers.
There’s no telling how far ML will go in revolutionizing the retail world, but a recent study by McKinsey suggests that U.S. retailers that have adopted data and analytics into their supply chain have experienced up to a 10% increase in operating margin over the last five year. Attaining data and developing the right smart solutions platform with predictive capabilities have been key to boosting businesses’ ROIs. Intelligent automation company WorkFusion has a number of automation products that use data to churn out smart solutions for retail, including its robotic process automation platform RPA Express.
Here are four ways retailers are using ML to improve their retailing needs.
1) Stocking and Inventory
One of the key elements of running a successful business is the ability to streamline the stocking and inventory management process in a swift and automated manner. ML is offering retailers the chance to purchase online and offline data to predict inventory needs in real time, breaking down these factors based on different segments such as day of the week, season of the year and activity in a particular store. This information could be used to create a daily dashboard of suggested orders for a purchasing manager. Machine vision may also be used soon in the form of cameras that can detect the number of items of a particular product throughout the entire store just by looking at it.
2) Predicting Customer Behavior
The technology also has a positive role in analyzing customer data and predicting future behavior. Retailers can use this data to better understand the needs of their customers by examining the price range of their previous purchases, recommending items that they may be interested in. ML algorithms can generate suggestions for items that are complementary to items they are buying instead of simply pushing a hot item that is completely unrelated to what they are purchasing. Additionally, retailers can use add-on options for hygienic and other daily products that they may want to buy on a monthly or quarterly basis if they’re happy with the product.
3) Tracking Behavior for Marketing Purposes
ML can also be used to determine how well a product sells based on the position it’s in relative to the rest of the store. One way to predict how customers react to certain products is with cameras that detect the walking patterns and the direction customers face when walking down the store. These cameras could compile data that measure the interest of various products, which could be use to restructure store layouts. They could also be used to test new items or determine whether products with declining sales should be phased out.
4) Dynamic Pricing
Companies know that ensuring an item is priced accurately can make or break their business. ML now has the capability of offering dynamic pricing options, which means that the price of certain products change over time through an algorithm that considers a variety of pricing variables. These metrics could include the season of the year, as well as supply and demand. With this technology, retailers have more flexibility when generating the right price at the right time without losing sight of their main goals, including profit or revenue optimization. By learning the performance of a product over time, ML can easily adapt to changes in the market and improve a company’s ROI.
