A Classification Based Model to Assess Customer Behavior in Banking Sector

A. Rahman, M. N. A. Khan

Abstract


A customer relationship management system is used to manage company relationships with current and possible customers. Following a thorough review of contemporary literature, different data mining techniques employed in different types of business, corporate sectors and organizations are analyzed. A model that would be helpful to identify customers’ behavior in the banking sector is then proposed. Three classifiers, k-NN, decision tree and artificial neural networks are used to predict customer behavior and are assessed in order to determine which classifier performs better for predicting customer behavior in the banking sector.


Keywords


customer; relationship; management; profitability; behavior; data mining; prediction

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References


T. F. Bahari, M. S. Elayidom, “An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour”, Procedia Computer Science, Vol. 46, pp. 725-731, 2015

A. Shrivastava, B. Kumari, “Implementation of classifiers and their performance evaluation”, International Journal of Engineering Research Online, Vol. 3, No. 2, pp. 71-78, 2015

N. Khan, F. Khan, “Fuzzy based decision making for promotional marketing campaigns”, International Journal of Fuzzy Logic Systems, Vol. 3, No. 1, pp. 64-77, 2013

H. A. Elsalamony, “Bank Direct Marketing Analysis of Data Mining Techniques”, International Journal of Computer Applications, Vol. 85, No. 7, pp. 12-22, 2014

A. Nachev, “Application of Data Mining Techniques for Direct Marketing”, in: Computational Models for Business and Engineering Domains, pp.86-95, ITHEA, Rzeszow – Sofia, 2014

M. Karim, R. M. Rahman, “Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing”, Journal of Software Engineering and Applications, Vol. 6, pp. 196-206, 2013

S. Emtiyaz, M. Keyvanpour, “Customers behavior modeling by semi-supervised learning in customer relationship management”, arXiv preprint arXiv:1201.1670, 2012

H. Ahn, J. J. Ahn, K. J. Oh, D. H. Kim, “Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5005-5012, 2011

I. Bose, X. Chen, “Exploring business opportunities from mobile services data of customers: An inter-cluster analysis approach”, Electronic Commerce Research and Applications, Vol. 9, No. 3, pp. 197-208, 2010

Y. L. Chen, M. H. Kuo, S. Y. Wu, K. Tang, “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data”, Electronic Commerce Research and Applications, Vol. 8, No. 5, pp. 241-251, 2009

J. D’Haen, D. Van den Poel, D. Thorleuchter, “Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique”, Expert Systems with Applications, Vol. 40, No. 6, pp. 2007-2012, 2013

P. Duchessi, E. J. Lauria, “Decision tree models for profiling ski resorts’ promotional and advertising strategies and the impact on sales”, Expert Systems with Applications, Vol. 40, No. 15, pp. 5822-5829, 2013

A. Griva, C. Bardaki, S. Panagiotis, D. Papakiriakopoulos, “A Data Mining Based Framework to Identify Shopping Missions”, Mediterranean Conference on Information Systems, August 4, 2014

S. M. S. Hosseini, A. Maleki, M. R. Gholamian, “Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty”, Expert Systems with Applications, Vol. 37, No. 7, pp. 5259-5264, 2010

M. Khajvand, K. Zolfaghar, S. Ashoori, S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study”, Procedia Computer Science, Vol. 3, pp. 57-63, 2011

S. H. Liao, Y. J. Chen, M. Y. Deng, “Mining customer knowledge for tourism new product development and customer relationship management”, Expert Systems with Applications, Vol. 37, No. 6, pp. 4212-4223, 2010

V. L. Migueis, A. S. Camanho, J. F. Cunha, “Customer data mining for lifestyle segmentation”, Expert Systems with Applications, Vol. 39, No. 10, pp. 9359-9366, 2012

B. Shim, K. Choi, Y. Suh, “CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7736-7742, 2012

A. Rahman, M. N. A. Khan,”An Assessment of Data Mining Based CRM Techniques for Enhancing Profitability”, International Journal of Education and Management Engineering, Vol. 7, No. 2, pp.30-40, 2017




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