Anticipating Customer Retention: A Machine Learning Approach

Abstract

Customer churn stands as a significant challenge in the Telecommunications Industry, marked by a substantial number of clients swiftly switching service providers within a brief timeframe. In essence, customer churn signifies the loss of either complete or partial services from a customer by any organization. Business decision-makers and analysts underline the fact that acquiring new customers comes at a higher cost compared to retaining existing ones. To delve into this pressing issue, we have focused on the telecommunications market and chosen the IBM Watson Analysis Dataset for our in-depth case study. Significant advancements have been achieved in the realm of churn prediction systems through the utilization of Machine Learning techniques, including Random Forest Classifier (RF), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors Classifier (KNN). These developments have resulted in improved performance and the delivery of cost-effective solutions. The evaluation of various metrics such as accuracy, precision, and recall has played a crucial role in assessing the efficacy of these models. Business analysts and Customer Relationship Management (CRM) analysts must understand the factors contributing to customer churn and analyze the behaviour patterns evident in existing churn customer data. This project primarily aims to reduce customer churn in the telecommunications sector. By identifying key churn factors from customer data, Customer Relationship Management (CRM) can enhance productivity. It allows for the recommendation of targeted promotions to a group of potential churn customers with similar behaviour patterns, leading to significant improvements in the company's marketing campaigns.