Abstract
Customer churn may be a critical issue for banks. The extant literature on statistical and machine learning for customer churn focuses on the problem of correctly predicting that a customer is about to switch bank, while very rarely consid-ers the problem of generating personalized actions to improve the customer retention rate. However, these decisions are at least as critical as the correct identification of customers at risk. The decision of what actions to deliver to what customers is normally left to managers who can only rely upon their knowledge. By looking at the scientific literature on CRM and personalization, this research proposes a number of models which can be used to generate marketing ac-tions, and shows how to integrate them into a model embracing both the analytical prediction of customer churn and the generation of retention actions. The benefits and risks associated with each approach are discussed. The paper also describes a case of application of a predictive model of customer churn in a retail bank where the analysts have also generated a set of personalized actions to retain customers by using one of the approaches presented in the paper, namely by adapting a recommender system approach to the retention problem.
Highlights
Retail banks often deal with customer churn
The paper describes a case of application of a predictive model of customer churn in a retail bank where the analysts have generated a set of personalized actions to retain customers by using one of the approaches presented in the paper, namely by adapting a recommender system approach to the retention problem
A comprehensive model of how to generate actions might be helpful to develop more effective data mining and statistical models to support Customer Relationship Management (CRM) processes. By looking at both the scientific literature on CRM and personalization, this paper proposes a number of approaches which can be used to generate personalized marketing actions, and discusses benefits and risks associated with each model
Summary
Retail banks often deal with customer churn. Among the several issues addressed by Customer Relationship Management (CRM), identifying the customers who are about to quit the relationship with a company is one of the most important in the financial services industry. The issue addressed by this research is to identify a set of approaches to generating actions to retain customers, once customers at risk are identified and profiled We believe that this issue is important for both businesses launching customer retention campaigns and researchers aiming at building models of customer churn. The problem is to support managers in making the right decision on how to define personalized retention actions. A comprehensive model of how to generate actions might be helpful to develop more effective data mining and statistical models to support CRM processes By looking at both the scientific literature on CRM and personalization, this paper proposes a number of approaches which can be used to generate personalized marketing actions, and discusses benefits and risks associated with each model. The results of this model are compared to those obtained by the same company in a formerly launched retention campaign
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Intelligent Learning Systems and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
