Customer Profiling using Classification Approach for Bank Telemarketing

Shamala Palaniappan, Aida Mustapha, Cik Feresa Mohd Foozy, Rodziah Atan

Abstract


Telemarketing is a type of direct marketing where a salesperson contacts the customers to sell products or services over the phone. The database of prospective customers comes from direct marketing database. It is important for the company to predict the set of customers with highest probability to accept the sales or offer based on their personal characteristics or behavior during shopping. Recently, companies have started to resort to data mining approaches for customer profiling. This project focuses on helping banks to increase the accuracy of their customer profiling through classification as well as identifying a group of customers who have a high probability to subscribe to a long term deposit. In the experiments, three classification algorithms are used, which are Naïve Bayes, Random Forest, and Decision Tree. The experiments measured accuracy percentage, precision and recall rates and showed that classification is useful for predicting customer profiles and increasing telemarketing sales.


Keywords


decision tree; classification; data mining; customer profiling

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References


Kadir, H. M. G. A. Garis Panduan Penasihatan Akademik Di Politeknik (KADIR, H.). Kuala Lumpur (2012).

Asuncion, A., Newman, D. CA: University of California, School of Information and Computer Science, UCI Machine Learning Repository. Irvine, (2012).

Witten, I.H., Frank, E., Hall, M.A., & Pal, C.J. Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann (2016).

John, G., Kohavi, R., and Pfleger. Irrelevant features and the subset selection problem. Int. Conf. on Machine Learning, Morgan Kaufman, San Francisco. (1994), 121-129.

Duch, W., Winiarski, T., Biesiada, J., and Kachel, A. Feature Ranking Selection and Discretization. Int. Conf. on Artificial Neural Networks (ICANN) and Int. Conf. on Neural Information Processing (ICONIP), Istanbul. (2003), 251-254.

Xinguo, L. et al., A Novel Feature Selection Method Based on CFS in Cancer Recognition, IEEE 6th International Conference on System Biology IISB), (2012).

Yogesan, K., Eikelboom, R.H., Barry, C.J. Centre for Ophthalmology and Visual Science. University of Western Australia, 2 Verdun Street, Netherlands, WA 6009, Australia.

Zahlmann, G., Scherf’, M., Wegner, A. Neurofuzzy and EUBAFES as tools for knowledge discovery in visual field data. National Research Centre for Environment and Health, Proceedings of the 20th Annual International Conference of the ZEEE Engineering in Medicine and Biology Society, Vol. 20, No 3 (1998).

Teli, S., Kanikar, P. A Survey on Decision Tree Based Approaches in Data Mining. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 4, (2015).

Ting, S.L., IP, W.H., Tsang, A.H.C. Is Naïve Bayes a Good Classifier for Document Classification? International Journal of Software Engineering and Its Applications, Vol. 5, No. 3, July, (2011).

Gupte, A., Joshi, S., Gadgul, P., Kadam, A. Comparative Study of Classification Algorithms used in Sentiment Analysis. International Journal of Computer Science and Information Technologies, Vol. 5 (5), (2014).

Moro, S., Cortez, P. Rita. P. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, (2014), 62:22-31.


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