Artificial intelligence is changing the world. One application of artificial intelligence is machine learning, which gives machines the ability to access and learn from data. Machine learning gives online retailers the ability to provide a better experience for customers on digital and mobile platforms. 

Machine Learning Offers Personalization for Customers 

Machine learning programs, such as Spark ML, enable e-commerce retailers with the ability to provide their customers with more personalized experiences, which is exactly what shoppers want. One study found that 73% of consumers are fed up with being shown irrelevant content when they are shopping online. It also allows retailers to decrease the likelihood of customer service problems before they even happen. This can reduce cart abandonment rates and increase sales. Machine learning bots also have the ability to provide customers with unbiased solutions. 

Search Results and Product Recommendations are Optimized With Machine Learning 

Machine learning applications can help retailers reap the benefits of better payoffs with improved search results. Machine learning doesn't use keyword matching or other traditional search techniques. Instead, it generates a search ranking that uses the relevance of a particular shopper. For instance, if you are searching for a wedding gift on a website, machine learning can use algorithms that are based off your past shopping behaviors along with the past behaviors of other consumers. 

Machine learning can also be used for product recommendations. Online businesses that use machine learning for product recommendations will have an increased likelihood of higher conversion rates. It is effective because it uses an algorithm that analyzes consumer data from various channels to predict what a consumer wants. In a sense, it acts somewhat like a virtual personal shopper.

Machine Learning can Prevent Fraud 

Machine learning can also be used to prevent fraudulent actions. It can recognize patterns in data and learn the difference between normal and unusual patterns. When there is unusual activity, it can send notifications to retailers. This eases the burden that retailers often face with consumers who spend large amounts on stolen cards or attempt to retract their payments once the items have been delivered. 

Effective Promotions and Price Optimization 

Price optimization has many parameters that can become overwhelming for humans, and the most current software tools are not able to keep up with price optimization like machine learning can. Machine learning uses algorithms to develop a number of decision trees that are based on many sub-groups, and it then develops an effective model that can make predictions that are accurate. This information can be beneficial for retailers who want to look at the potential outcomes of specific sales promotions. Retailers don't need to try out promotions that pose a risk because the machine learning algorithm simulates many possible outcomes for each promotion.

More Efficient Inventory and Supply Chain Planning

In the past, antiquated inventory planning was primarily carried out by trial and error. It was hard for retailers to accurately predict how many consumers would purchase products next month or if a sought after product could lose its popularity, but retailers don't have to worry about these issues with the use of machine learning applications. 

Another way machine learning will change shopping is with root cause analysis. This helps retailers recognize the cause for faults in an existing system. For example, it can identify communication lapses and incomplete data. By using machine learning, guesswork and human bias are no longer needed for recognizing the root cause of a system. It can be used for warehouse processing, vendor management, and more. In addition, discriminant analysis can also be used with machine learning. Discriminant analysis uses machine learning algorithms to enhance segmentation and classification. 

Machine learning is an effective way for retailers to increase efficiency and productivity on mobile and digital platforms. Consumers want a personalized shopping experience, and businesses that use machine learning will have an upper edge over those that don't.