One of the ironclad rules of retail is that the more you know about your customers, the better you can serve them. That same rule applies even to the technology used behind the scenes, where machine learning (ML) has become an integral piece of retailers’ strategy. A form of artificial intelligence, ML enables computer programs to teach themselves based on prior inputs. The most prominent example of this is the algorithms used to curate a shopper’s recommendations online. The more shopping someone does, the more likely those recommended items are to be something he or she would want.

However, the reach of ML extends far beyond what the consumer experiences while shopping. It has ramifications that are changing the way the industry makes decisions, with computers learning how to predict demand surges, personalize marketing messages, and optimize logistics. With their ability to discern patterns and adjust based on new data, ML      solutions      also help retailers fight fraud and analyze customers’ sentiments, all without the need for human intervention.

With computers that are capable of learning 24/7, retail operations can gain numerous benefits. For instance, automating many touchpoints with consumers means employees can concentrate their attention on solving problems or developing creative new solutions. ML is also being used to identify potential new customers by scanning social media posts and predicting which existing customers are most likely to return to make additional purchases. It’s no wonder that the number of retailers making use of these solutions has been increasing significantly over the last few years.    

If you want to learn more about how these innovative business solutions are changing the game in a substantial way, take a look at the accompanying resource. It details many of the advantages ML provides as well as a brief overview of how it works. The following infographic was created by Aptitive.

Graphic created by Aptitive.

Posted by Miley

Leave a reply

Your email address will not be published. Required fields are marked *