Affiliations: Department of Computer Engineering, Alzahra University, Tehran, Iran
Correspondence:
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Corresponding author: Seyed Mohammad Hossein Hasheminejad, Department of Computer Engineering, Alzahra University, Tehran, Iran. E-mail: SMH.Hasheminejad@Alzahra.ac.ir.
Abstract: In today’s business world, identifying the customers and analysis of their behavior is important for banking industry. Customer Relationship Management (CRM) is the process of maintaining profitable customer relationships by delivering customer value and loyalty. Moreover, CRM helps to improve the business relationships with customers. The goal of CRM is to maximize the lifetime value of a customer to an organization. Customer Lifetime Value (CLV) can rank and classify customers based on their lifetime value to identify valuable customers and retain them. There are several models for CLV estimation using the past data of customers. This subject helps organizations in their attempts to retain valuable customers. The banks must use appropriate data mining techniques to extract pattern and information from the existing data to gain competitive advantage. Therefore, data mining techniques have an important role to extract the hidden knowledge and information. The goal of this study is to review data mining techniques used for analyzing bank customers in order to help the banks to better identify their customers and design more efficient marketing strategies. The literature covered in this paper is related to the past seventeen years (2001–2017) and these approaches are compared in terms of data sets, prediction accuracy, and so on. We also provide a list of data sets available for the scientific community to conduct research in this field. Finally, open issues and future works in each of these items are presented.
Keywords: Customer relationship management, customer lifetime value, bank industry, data mining, loyalty
