Chapter 3: Customer Churn Prediction for Retention Analysis

This abstract provides a comprehensive overview of the research on Customer Churn Prediction for Retention Analysis. In today’s corporate context, understanding and mitigating customer churn has become critical for long-term success. This study focusses on the building and testing of predictive churn models aimed at forecasting customer attrition behaviour. Using advanced deep learning methods such as artificial neural networks (ANNs), the study examines past customer data to uncover trends and indications linked with attrition. It also investigates the integration of diverse information including customer involvement, contentment and transactional history to improve forecast accuracy. The proposed approach comprehends the heterogeneity of client bases and employs customer segmentation using the K-means algorithm to personalise retention strategies to distinct customer groups, detecting and addressing varied requirements and preferences. The project’s unique feature is the inclusion of duration prediction for churn, which allows organisations to prioritise retention efforts based on the projected duration of churn for individual customers. In essence, the project aims to enhance the field of customer churn prediction and retention analysis by combining cutting-edge methodologies to apply targeted and timely retention measures, eventually nurturing customer loyalty and increasing the lifetime value of their customer base.