Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty

  • H. Alizadeh Department of Computer Engineering, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran
  • B. Minaei Bidgoli School of Computer Engineering, Iran University of Science & Technology, Tehran, Iran
Volume: 6 | Issue: 6 | Pages: 1235-1240 | December 2016 |


The main aim of this study was introducing a comprehensive model of bank customers᾽ loyalty evaluation based on the assessment and comparison of different clustering methods᾽ performance. This study also pursues the following specific objectives: a) using different clustering methods and comparing them for customer classification, b) finding the effective variables in determining the customer loyalty, and c) using different collective classification methods to increase the modeling accuracy and comparing the results with the basic methods. Since loyal customers generate more profit, this study aims at introducing a two-step model for classification of customers and their loyalty. For this purpose, various methods of clustering such as K-medoids, X-means and K-means were used, the last of which outperformed the other two through comparing with Davis-Bouldin index. Customers were clustered by using K-means and members of these four clusters were analyzed and labeled. Then, a predictive model was run based on demographic variables of customers using various classification methods such as DT (Decision Tree), ANN (Artificial Neural Networks), NB (Naive Bayes), KNN (K-Nearest Neighbors) and SVM (Support Vector Machine), as well as their bagging and boosting to predict the class of loyal customers. The results showed that the bagging-ANN was the most accurate method in predicting loyal customers. This two-stage model can be used in banks and financial institutions with similar data to identify the type of future customers.

Keywords: Loyalty, data mining, clustering, classification, evaluation


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E. W. T. Ngai, L. Xiu, D. C. K. Chau, “Application of data mining techniques in customer relationship management: a literature review and classification”, Expert Systems with Applications, Vol. 36, No. 2, pp. 2592-2602, 2009 DOI:

R. Ling, D. C. Yen, “Customer relationship management: An analysis framework and implementation strategies”, The Journal of Computer Information Systems, Vol. 41, No. 3, pp. 82-97, 2001

D. K. Tiwary, “Application of data mining in customer relationship management (crm)”, Advances in Computational Sciences and Technology, Vol. 3, No. 4, pp. 527-540, 2010

S. Rosset, E. Neumann, U. Eick, N. Vatnik, “Customer lifetime value models for decision support”, Data Mining and Knowledge Discovery. Vol. 7, No. 3, pp. 321-339, 2003

Y. H. Liang, “Integration of data mining technologies to analyze customer value for the automotive maintenance industry”, Expert Systems with Applications. Vol. 37, No. 12, pp. 7489-7496, 2010 DOI:

M. Re, G. Valentini, “Ensemble methods: a review” in Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall, 2012 DOI:

M. Alvandi, S. Fazli, F. S. Abdoli, “K-Mean Clustering Method for Analysis Customer Lifetime Value with LRFM Relationship Model in Banking Service”, .International Research Journal of Applied and Basic Sciences, Vol. 3, No. 11, pp. 2294-2302, 2012

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 DOI:

J. Pablo Maicas Lopez, Y. Polo Redondo, F. J. Sese Olivan, “The impact of customer relationship characteristics on customer switching behavior: Differences between switchers and stayers, “Managing Service Quality: An International Journal”, Vol. 16, No. 6, pp. 556-574, 2006 DOI:

B. Fang, S. Ma, “Data mining technology and its Application In CRM of Commercial Banks”, First International Workshop on Database Technology and Applications, pp. 243-246, 2009 DOI:

M. Khajvand, M. J. Tarokh, “Estimating customer future value of different customer segments based on adapted RFM model in retail banking context”, Procedia Computer Science, Vol. 3, pp. 1327-1332, 2011 DOI:

P. Kolter, Marketing Management, 11th edition, Pearson Education, New Jerse,. 2003

J. T. Wei, S. Y. Lin, C. C. Weng, H. H. Wu, “A case study of applying LRFM model in market segmentation of a children’s dental clinic”, Expert Systems with Applications, Vol. 39, No. 5, pp. 5529-5533, 2012 DOI:

C. H. Cheng, Y. S. Chen, “Classifying the segmentation of customer value via RFM model and RS theory”, Expert Systems with Applications, Vol. 36, No. 3, pp. 4176-4184, 2009 DOI:

H. Khodzi, E. Akhondzadeh-Noughabi, B. Minaei-Bidgoli, “A New Application of RFM Clustering for Guild Segmentation to Mine the Pattern of Using Banks’ e-Payment Services”, Journal of Global Marketing, Vol. 27, No. 3, pp. 178-190, 2014 DOI:

Z. Zalaghi, Y. A. Varzi, “Measuring customer loyalty using an extended RFM and clustering technique”, Management Science Letters, Vol. 4, No. 5, pp. 905-912, 2014 DOI:

A. Azevedo, M. F. Santos, “KDD, SEMMA and CRISP-DM: a parallel overview”, IADIS European Conference on Data Mining, Amsterdam, The Netherlands, July 24-26, 2008

S. S. Rohanizadeh, M. B. Moghadam, “A proposed data mining methodology and its application to industrial procedures”, Journal of Industrial Engineering, Vol. 4, pp. 37-50, 2009

M. Bizhani, M. J. Tarokh, “Behavioral rules of bank’s point-of-sale for segments description and scoring prediction”, International Journal of Industrial Engineering Computations, Vol. 2, No. 2, pp. 337-350, 2011 DOI:


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