An Intelligent Customer Churn Prediction and Response Framework

Abstract

Customer retention is one of the most important issues for companies. Companies always seek to reduce customer churn in order to increase the customer lifetime value and reduce the cost of acquisition of new customers. By focusing on customer churn prediction and identification, companies can predict in advance which customers are going to churn and therefore decrease customers churn rate through related personalized actions. The key issue here is how to predict customer churn at an early stage. This paper identifies related issues in customer churn prediction and provides new definitions and classifications on customer churn identification and strategies. This paper also establishes a customer churn prediction and response framework consists of three main stages: customer churn prediction, customer churn understanding and customer churn response. The framework presents the characteristics and challenges of related stages of customer churn as well. These outcomes can be used for customized or personalized product and service developments, to improve customer service efficiency and related decision-making more effective and more particularly enabling strategic promotion campaigns to customers with high churn risk.