Customer Behavior in Banking Industry: Comparison of Data Mining Techniques
Mohammad Ali Afshar Kazemi 1, Negar Estemdad 1, and
Alireza Poorebrahimi 2
1. Department of Economics and Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2. Department of Economics and Management, E-Campus, Islamic Azad University, Tehran, Iran
2. Department of Economics and Management, E-Campus, Islamic Azad University, Tehran, Iran
Abstract—Nowadays organizations have perceived the importance of managing customer relationship and its potential benefits. Customer relationship management supports organizations to deliver beneficial relations with customers. Customer satisfaction and retention are the leading objectives of any organization and this cannot be done without knowing the loyalty of the customer. Accordingly, to identify the loyalty of the customers, K-means algorithm was applied to the bank customers' data and clustering was conducted. The customer behavior is estimated by neural network and C5.0 models. Results show that C5.0 better fits the customer behavior. In addition, estimation of customer behavior leads organizations to more successful customer management strategies.
Index Terms—data mining, loyalty, bank industry, neural network, C5.0 algorithm
Cite: Mohammad Ali Afshar Kazemi, Negar Estemdad, and Alireza Poorebrahimi, "Customer Behavior in Banking Industry: Comparison of Data Mining Techniques," Journal of Advanced Management Science, Vol. 3, No. 1, pp. 13-16, March 2015. doi: 10.12720/joams.3.1.13-16
Index Terms—data mining, loyalty, bank industry, neural network, C5.0 algorithm
Cite: Mohammad Ali Afshar Kazemi, Negar Estemdad, and Alireza Poorebrahimi, "Customer Behavior in Banking Industry: Comparison of Data Mining Techniques," Journal of Advanced Management Science, Vol. 3, No. 1, pp. 13-16, March 2015. doi: 10.12720/joams.3.1.13-16
