Machine Learning In Retail Sector: Benefits, Use Case Explained
Before Artificial Intelligence and machine learning retailers relied on data-driven decisions for the success of their businesses. Thanks to AI and machine learning retailers can now track their customers’ purchase trends through data sources like loyalty programs, purchase history, CRM databases, social media activity, market trends, and consumer demands. The role of machine learning in retail sector can’t be denied.
The ability to apply sophisticated mathematical calculations to big data automatically is now achievable via machine learning. It is significantly contributing to revolutionizing the e-commerce industry through product pricing optimization, personalized product recommendations, and precise ad targeting.
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.
Also Read: How to Approach Building a Healthcare Data Lake Roadmap
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.
Alibaba
It is similar to Amazon, is an extensive e-commerce marketplace. Alibaba relies on its retailers and considers itself a retail ecosystem. The retailer has prioritized big data analysis and made their primary focus in making data more accessible to smaller retailers selling through their service.
The retailer’s latest application brings data to the offline retail world, giving merchants the chance to understand the sales better. Buyers can order online for delivery from Alibaba-backed store. Alternatively, they can make in-store purchases, scan barcodes since the digital price tags are updated in real-time, make payment through the app, and get free delivery. The technology allows Alibaba to capture offline purchase behavior through the mobile application.
The captured data can be analyzed alongside the online data to create a full picture of a customer’s purchase habits.
Robot sale associates from North Face:
North Face is an outdoor clothing retailer that has been using AI and machine learning to provide their website users with a highly personalized customer experience dubbed Shop with IBM Watson. Upon launching North Face’s app, customers speak to their phones to access Watson-AI system from IBM. The virtual assistant walks the shopper through a series of questions like a physical customer service representative would do. This initial engagement with the virtual assistant tailors products seen in the app that match the customer’s preferences and needs.:
Target in detecting pregnancy
Target is a one-stop shop for everything. As opposed to other retailers, it encourages customers to buy everything ranging from clothes, groceries, appliances, and other household items from one place. Research shows that a customer changes their favorite shopping store when they have a significant change in their lives, such as marriage, graduation, or childbirth.
Target used machine learning to analyze customer data to determine which buyer was likely to be pregnant. After identifying common purchase habits from pregnant women who had registered with the Target baby registry, the algorithm was able to detect key patterns.
The identified trends could indicate not only pregnancy but also highlight the current trimester of the victim’s gestation period. Target used the data to send coupons that have relation to pregnancy and parenting to any buyer whose trends matched with the model.
Using this model, a father found out about his 16-year-old daughter unintended pregnancy through the company send the targeted promotions to her. Later, the company adopted the strategy to mix with other offers after customers grew uncomfortable with this degree of personalization.
Netflix uses big data and machine learning to determine its users’ film usage and habits. This has enabled the media giant to deliver the content which a particular viewer wants. The insights obtained from analyzing big data gives the system informed decisions such as the way they release full seasons, recommendations for related films, and autoplay in episodes.
The above Examples of machine learning in the retail industry represent a majority of companies that have implemented machine learning in their service delivery. There are many others which have integrated ML to take customer experience to a different level.
Endnote:
The Importance of machine learning in Retail industry goes beyond improving businesses’ sales. It enables companies to make informed decisions and employ precise marketing plans. Retailers can now capture buyers data with an improved degree of accuracy.
The obtained information helps retailers to provide personalized services to customers. The data can be obtained from the purchase or buying history. When a customer is served with the most appropriate product they might be anticipating to purchase and receive excellent purchasing experience, there are high chances they will become repeat customers.
Machine learning has also led to advanced systems transforming data promotion through automating operational tasks such as determining product assortments and shelf price setting. As competition in retail business increases, AI and machine learning will be critical participants in gaining and keeping a competitive advantage. Retailers, therefore, have to incorporate these technologies into their businesses.