Customer Churn Prediction Using Hierarchical Clustering and Ensemble Learning

The capacity to forecast which consumers are most likely to discontinue their use of a service or leave is known as customer churn prediction. This prediction holds significant importance for many companies, as acquiring new customers often proves to be more costly than retaining existing ones. Once customers at risk of churn are identified, it becomes crucial to determine the most effective marketing efforts for each individual customer in order to maximize their likelihood of staying. This entails recognizing that customers exhibit different behaviors, preferences, and reasons for canceling their subscriptions. Therefore, actively engaging with each customer is essential in order to maintain them on the customer list. Understanding which marketing activities are most effective for individual customers, as well as the optimal timing for these activities, is typically achieved through training a supervised machine learning model. This model was trained on historical data, which may have included customer service interactions, account activity, and purchase history, and it reached an extremely impressive accuracy of 87 percent. The model is then applied to new data to forecast things like whether a new customer would churn within a given period of time.