Knowledge Discovery On Investment Fund Transaction Histories and Socio-Demographic Characteristics for Customer Churn

Fatih Cil, Tahsin Cetinyokus, Hadi Gokcen
  • Fatih Cil
    Finansbank A.Ş., Turkey
  • Hadi Gokcen
    Gazi University, Turkey


The need of turning huge amounts of data into useful information indicates the importance of data mining. Thanks to latest improvement in information technologies, storing huge data in computer systems becomes easier. Thus, “knowledge discovery” concept becomes more important. Data mining is the process of finding hidden and unknown patterns in huge amounts of data. It has a wide application area such as marketing, banking and finance, medicine and manufacturing. One of the most commonly used application areas of data mining is recognizing customer churn. Data mining is used to obtain behavior of churned customers by analyzing their previous transactions. In the same manner using with obtained tendency, other active customers are held in the system. It is possible to make by various marketing and customer retention activities. In this paper, it is aimed to recognize the churned customers of a bank who closed their saving accounts and determine common socio-demographic characteristics of these customers.


Data mining, Customer churn, Decision trees and classification rules, Mutual funds

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Submitted: 2018-04-12 12:33:17
Published: 2018-12-27 18:57:41
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Han, J., Kamber, M., Data mining: concepts and techniques 1st ed., Morgan Kaufmann, USA, 3-16, (2001).

Chien C.-F., Chen, L.-F., Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry, Expert Systems with Applications, 34(1): 280-290 (2008).

Giudici, P., Applied data mining: statistical methods for business and industry 1st ed., John Wiley & Sons, England, 1-15, 85-110, (2003).

Fayyad, U., Piatetsky-Shapiro G., Smyth P., From data mining to knowledge discovery in databases, American Association for Artificial Intelligence, 3(17): 37-54 (1996).

Apte, C., Weiss, S., Data mining with decision trees and decision rules, Future Generation Computer Systems, 13, 197–210 (1997).

Fernandeza, I.B., Zanakisa, S. H., Walczakb, S., Knowledge discovery techniques for predicting country investment risk. Computers & Industrial Engineering, 43, 787–800. (2002).

Witten, I., H., Frank, E., Data mining: practical machine learning tools and techniques 2nd ed., Morgan Kaufmann, USA, 62-415 (2005).

Masand, B., Datta, P., Mani, D.R., Li, B., CHAMP: A prototype for automated cellular churn prediction, Data Mining and Knowledge Discovery 3, Netherlands, 219 - 225 (1999).

Poel, D., and Lariviere, B., Customer attrition analysis for financial services using proportional hazard models, European Journal of Operational Research, 2004, vol. 157, No 1, 196-217.

Ahn, J.H., Han, S.P., Lee, Y.S., Customer churn analysis: churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry, Science Direct, Telecommunications Policy, 30: 552 - 568 (2006).

Ruta, D., Nauck, D., Azvine, B., K nearest sequence method and its application to churn prediction, Springer-Verlag, Berlin Heidelberg, 207 - 215 (2006).

Chu, B.H., Tsai, M.S., Ho, C.S., Toward a hybrid data mining model for customer retention, Knowledge-Based Systems, 20 (8) (2007), pp. 703–718.

Aydoğan, E., Gencer, C., Akbulut, S., Churn analysis and customer segmentation of a cosmetics brand using data mining techniques, Journal of Engineering and Natural Sciences, Sigma, 26 (2008).

Gopal, R. K., Meher, S. K., Customer churn time prediction in mobile telecommunication industry using ordinal regression, Springer-Verlag, Berlin Heidelberg, 884 - 889 (2008).

Farquad, M.A.H., Ravi, V., Raju, S.B., Data mining using rules extracted from svm: an application to churn prediction in bank credit cards, Springer-Verlag, Berlin Heidelberg, 390 - 397 (2009).

Huang, B. Q., Kechadi, M. T., Buckley, B., Customer churn prediction for broadband internet services, Springer-Verlag, Berlin Heidelberg, 229 - 243 (2009).

Chen, Z.-Y., Fan, Z.-P., Distributed customer behavior prediction using multiplex data: a collaborative MK-SVM approach, Knowledge-Based Systems, 35 (2012), pp. 111–119.

Zhang, X., Zhu, J., Xu, S., Wan, Y., Predicting customer churn through interpersonal influence, Knowledge-Based Systems, 28 (2012), pp. 97–104.

Slavescu, E., Panait, I., Improving customer churn models as one of customer relationship management business solutions for the telecommunication industry, “Ovidius” University Annals, Economic Science Series, 2012, vol. 12, Issue 1/201.

Devi P., U., Madhavi, S., Prediction of churn behavior of bank customers using data mining tools, Business Intelligence Journal, 2012, vol.5, 96-101.

Jamal, Z., Tang, H.K., Methods and systems for identifying customer status for developing customer retention and loyalty strategies, United States, Patent Application Publication, (2013).

Verbeke, W., Martens, D., Baesens, B., Social network analysis for customer churn prediction, Applied Soft Computing, 2014, vol. 14, 431–446.

Farquad, M.A.H., Ravi, V., Raju, S.B., Churn prediction using comprehensible support vector machine:An analytical CRM application, Applied Soft Computing, 2014, vol. 19, 31–40.

Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., Abbasi, U., Improved churn prediction in telecommunication industry using datamining techniques, Applied Soft Computing, 2014, vol.24, 994–1012.

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