Retailers and e-commerce businesses are actively working to understand their customers' behavior, identify the factors that lead to customer churn, and devise strategies to retain customers. These efforts are particularly important for customers who have a high lifetime value.

By analyzing data such as purchasing history, demographics, and interactions with the company, retailers can gain valuable insights into why customers may stop shopping with them. Furthermore, by implementing machine learning algorithms that can analyze this data and identify patterns or trends, companies can more effectively predict which customers are at risk of churning and what strategies can be employed to retain them. 

Retailers can enable targeted and personalized retention efforts, such as email campaigns, discounts, or addressing operational issues by building a customer churn prediction model and training it on historical customer data. Additionally, by analyzing the characteristics of different customer segments, retailers can tailor these retention efforts to meet the needs of specific segments while improving revenue and improving ROI on retention budgets. The business can also address customer satisfaction and revenue challenges by incorporating relevant data into the churn prediction model and monitoring key customer metrics in real-time.