Machine Learning In Retail Sector: Benefits, Use Case Explained

Why machine learning matters in the retail industry?

When business leaders or managers think of machine learning in retail, they usually imagine automated processes such as stocking shelves or checkout, helpful in-store robots, or chatbots that answer customers’ questions and suggest products.

However, the impact of machine learning in retail is getting bigger and bigger with time. Artificial Intelligence and Machine Learning in retail are no longer about physical automation and direct replacement of human labor. AI and Machine Learning the current retail experience with data and decision making. They are playing a bigger role in customer experience (CX), mass personalization, and market segmentation and can never be ignored.

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What is machine learning?

Machine learning is a subset of Artificial Intelligence that enables systems to learn on their own. Unlike traditional programming where a software engineer commands a computer on what to do, machine learning enables machines to learn from experience without being explicitly programmed. When a computer (or any other machine) is exposed to new data, the program can change, learn, and make better and more accurate decisions. In other words, machine learning enables computer systems to continually update their knowledge and understanding of rules as it interacts with more examples of how humans react to different
external factors.

From consumer products to financial services, AI has dramatically transformed the global business landscape. Machine learning in retail analytics has enhanced how retailers are capitalizing on big data to improve their marketing strategies. In marketing, machine learning is being used to enhance targeting, response rates, and improving ROI. It involves the automatic analysis of large volumes of data like purchasing behavior and spending habits as well as demographic information. This data is processed using a mathematical algorithm to determine patterns and trends. The same algorithm then tests predictions based on historical campaign data and learns from the forecasts it gets right. As time goes, the algorithm becomes more accurate as it is fed with more data.

When these trends are used to make a marketing strategy or campaign, retailers have less guesswork and higher chances of success. An ideal question would be, why is this important, and how can machine learning help in the retail sector? Retailers can now determine their customers’ shopping behavior with a higher degree of accuracy by knowing the products they like. Machine learning can detect human actions, including both buyers and employees. The information learned by the computer program is beneficial to retailers in improving their service delivery and getting more returns.

Benefits of machine learning in Retail Industry

• Improving customer experience: Given the capabilities of machine learning and AI, creating a more personalized shopping experience for customers is a top priority for retailers. The algorithm used in machine learning facilitates for retailers to have personalized product recommendations for different customers based on each customer’s purchase trends and unique interest. Besides improving customer experience, machine learning can help retailers to segment their markets accurately. Therefore, point-of-sale and marketing strategies will be more targeted to the right audience.
• Increase the customer lifetime value: Repeat customers contribute about 40% of a business’ revenue. A big challenge for retailers is where to make their marketing investment to increase the customer return rate. This boils down to determining which customers have a higher likelihood to return and the factors that influence the value for these customers. These two elements are crucial use cases for machine learning.
• Attract new customers: Although businesses get a sizable revenue from repeat customers, the rate of repeat customers visiting the same retail can decrease due to different reasons such as shifting of location, better opportunities from competitors, and diminished trust. Since more retailers are venturing into the market to compete for the same customers, retailers have to look for ways to attract new customers. Machine learning tools, such as programmatic advertising provide a significant advantage. Programmatic advertising refers to the automated buying and selling of ad space through the use of intricate analytics. For example, if a business wants to target new customers, the algorithm tool can analyze data from the current customer segment and page context to push a well-targeted ad to a prospective customer at the ideal time. Retailers are also testing propensity modeling, a move that targets consumers with a higher likelihood of customer conversion. Machine learning algorithms can track consumers in real-time using data from social media, CRM databases, and other sources to determine the most promising customers.
• Reduce marketing waste: Machine learning ability to improve without the involvement of human factor means systems can identify trends in real-time and adapt according to the situation. This is particularly vital in marketing. Marketers need to plan for campaigns ahead of big seasons such as Christmas, summer, and back to school. Marketers can, however, be involved in a lot of guesswork in determining what customers want. Machine learning helps retailers to predict the future through simulating scenarios that predetermine the outcomes and identify the crucial action areas. Machine learning helps systems to analyze live sales data and identify the products getting good customer response. This allows marketers to adapt to their tactics. Therefore, they can focus on promoting the products which have a higher chance of generating returns.

Use cases of machine learning in the retail industry

There are several examples of various companies using machine learning to improve their sales and enhance their customers’ experience. In today’s business environment, some companies have managed to stay ahead of their competitors due to the incorporation of AI and machine learning in their systems. Retailers understand the need for a customer to have a seamless shopping experience once they visit their shop.

The following are examples of machine learning in the retail industry, illustrating how technology can be of great value:

Walmart:

Predicting customer needs Walmart is a renowned retail giant that has implemented new technologies to anticipate customer needs and optimize operations. The company tested a facial recognition software in 2015 in a move to prevent theft. The facial recognition application can recognize the level of frustration of a specific customer at checkout and then alert a customer service representative to engage the frustrated shopper.

Amazon using ML to drive sales and anticipate demand:

Amazon is one of the companies that has used machine learning to a broader extent. Anyone who has visited their online store can acknowledge the high level of customer experience. For example, the recommended products shown on the first page upon visiting the website are based on your purchasing or browsing history.

Machine learning technology helps Amazon to predict trends in demands, thereby informing supply decisions for the anticipated increases or downfalls. Amazon has access to large volumes of data that not only helps the organization in estimating supply and demand but also in making better business decisions.

Amazon’s ML algorithm works so well that it drives more than 55% of the recommendations of the sales. The insights learned by the algorithm also helps Amazon to predict demand for inventory, therefore, making trend-based and seasonal decisions simpler.

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