Predicting Customer Churn: Revolutionizing Retail with Machine Learning - Jung, Sun Kyoung; Tsang, Seng-Su; Phumchusri, Naragain (International Journal for Applied Information Management, 2023)
The ultimate objective of every business is to increase sales and profits. When a company's regular customers suddenly stop buying from them, it can cause a significant decrease in revenue. It is a widely accepted fact that retaining existing customers is less expensive than acquiring new ones, which is why Customer Relationship Management (CRM) places a high emphasis on it, particularly in the retail industry. When a customer stops shopping at a store, the business loses the opportunity to make more sales and even cross-sell. Therefore, companies must identify customers who are at risk of leaving and take preventative measures to retain them. This article highlights the effectiveness of using machine learning in conjunction with transaction data for predicting customer churn in the retail industry. The study involved 5,115,472 customer loyalty card records from a European retailer's data warehouse, which were utilized to train the machine learning models. The results showed that machine learning models outperformed their linear regression counterparts.