A Targeted Hybrid Model to Customer Churn Prediction in the Insurance Industry (A Case Study)

Ali Baghaei, Monireh Hosseini

Keywords

customer churn prediction, customer valuation, customer relationship management, insurance industry, hybrid model

• [1] F. Wiersema, “The B2B Agenda: The current state of B2B marketing and a look ahead”, Industrial Marketing Management, 4(42), pp. 470-488, 2013.
• [2] S. Y. Hung, D. Yen, and H. Y. Wang, “Applying data mining to telecom churn management”, Expert System with Applications, 31(3), pp. 515-524, 2006.
• [3] T. S. H. Teo, P. Devadoss, and S. L. Pan, “Towards a holistic perspective of customer relationship management (CRM) implementation: A case study of the Housing and Development Board", Singapore, Decision Support Systems, 42(3), pp. 1613–1627, 2006.
• [4] C. F. Tsai, and Y. H. Lu, “Customer churn prediction by hybrid neural networks”, Expert Systems with Applications, 36(10), pp. 12547–12553, 2009.
• [5] C. Hung, and C.F. Tsai, “Segmentation based on hierarchical self-organizing map for markets of multimedia on demand”, Expert Systems with Applications, 34(1), pp. 780–787, 2008.
• [6] M. J. A. Berry, and G. S. Linoff, “Data mining techniques: For marketing, sales, and customer support”, John Wiley & Sons, 2003.
• [7] L. Ryals, “Are your customers worth more than money?”, Journal of Retailing and Consumer Services, 9(2002), pp. 241–251, 2002.
• [8] J. Burez, and D. Van den Poel, “CRM at a Pay-TV Company: Using analytical models to reduce customer attrition by targeted marketing for subscription services”, Expert Systems with Applications, 32(2), pp. 277–288, 2007.
• [9] M. Xu, and J. Walton, “Gaining customer knowledge through analytical CRM”, Industrial management & data systems, 105(7), pp. 955-971, 2005.
• [10] W.F. Wang, D.A. Chiang, M.H. Hsu, C.J. Lin, and I.L. Lin, “A recommender system to avoid customer churn: A case study”. Expert Systems with Applications, 36(4), pp. 8071–8075, 2009.
• [11] K. Chen, Y. H. Hu, and Y. C. Hsieh, “Predicting customer churn from valuable B2B customers in the logistics industry: a case study”, Information Systems and e-Business Management, pp. 1-20, 2014.
• [12] B. Huang, M.T. Kechadi, and B. Buckley, “Customer churn prediction in telecommunications”, Expert Systems with Applications, 39(1), pp. 1414-1425, 2012.
• [13] W. Verbeke, K. Dejaeger, D. Martens, J. Hur, and B. Baesens, “New insights into churn prediction in the telecommunication sector: A profit driven data mining approach”, European Journal of Operational Research, 218(1), pp. 211-229, 2012.
• [14] P. Kisioglu, Y.I. and Topcu, “Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey”. Expert Systems with Applications, 38(6), pp. 7151–7157, 2011.
• [15] G. Nie, W. Rowe, L. Zhang, Y. Tian, and Y. Shi, “Credit card churn forecasting by logistic regression and decision tree”, Expert Systems with Applications, 38(12), pp. 15273-15285, 2011.
• [16] C. Fornell, and B. Wernerfel, “Defensive marketing strategy by customer complaint management: a theoretical analysis”, Journal of Marketing research, pp. 337-346, 1987.
• [17] J. H. Ahn, S. P. Han, and Y. S. Lee, “Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry”, Telecommunications policy, 30(10), pp.552-568, 2006.
• [18] V. D. Mohammadi, A. Albadvi, and B. Teymorpur, ‘Predicting Customer Churn Using CLV in Insurance Industry’, Shiraz Journal of System Management, 2(1), pp. 39-49, 2014.
• [19] C. C. Günther, I. F. Tvete, K. Aas, G. I. Sandnes, and Ø. Borgan, “Modelling and predicting customer churn from an insurance company”, Scandinavian Actuarial Journal, 2014(1), pp. 587–598, 2014.
• [20] M. J. Lenard, G. R. Madey, and P. Alam, “The design and validation of a hybrid information system for the auditor’s going concern decision”, Journal of Management Information Systems, 14(4), pp. 219-237, 1998.
• [21] A. T. Jahromi, M. M. Sepehri, B. Teimourpour, and S. Choobdar, “Modeling customer churn in a non-contractual setting: the case of telecommunications service providers”, Journal of Strategic Marketing, 18(7), pp. 587–598, 2010.
• [22] W. J. Reinartz, “On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing”, Journal of Marketing, Vol. 64, pp. 17-35, 2000.
• [23] M. Chandar, A. Laha, and P. Krishna, “Modeling churn behavior of bank customers using predictive data mining techniques”, National conference on soft computing techniques for engineering applications (SCT-2006), pp. 24-26, 2006.
• [24] W. Buckinx, and D. Van den Poel, Customer base analysis: partial defection of behaviorally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), pp. 252–268, 2005.
• [25] N. Glady, B. Baesens, and C. Croux, “Modeling churn using customer lifetime value”, European Journal of Operational Research, vol. 197, pp. 402–411, 2009.
• [26] H. Risselada, P. C. Verhoef, and T. H. A. Bijmolt, “Staying power of churn prediction models”, Journal of Interactive Marketing, 24(3), pp. 198–208, 2010.
• [27] A. Sharma, and P. K. Panigrahi, “A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services”, International Journal of Computer Applications, 27(11), pp.26-31, 2011.
• [28] P.S. Fader, and B.G. S. Hardie, “Probability models for customer-base analysis”, Journal of Interactive Marketing, 23(1), pp. 61–69, 2009.
• [29] C. H. Cheng, and Y. S. Chen, “Classifying the segmentation of customer value via RFM model and RS theory”, Expert systems with applications, 36(3), pp. 4176-4184, 2009.
• [30] E. C. Chang, S. C. Huang, and H. H. Wu, “Using K-means method and spectral clustering technique in an outfitter’s value analysis”, Quality & Quantity, 44(4), pp. 807-815, 2010.
• [31] M. C. Chen, A. L. Chiu, and H. H. Chang, “Mining changes in customer behavior in retail marketing”, Expert Systems with Applications, 28(4), pp. 773-781, 2005.
• [32] S. M. S. Hosseini, A. Maleki, and M. R. Gholamian, “Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty”, Expert Systems with Applications, 37(7), pp. 5259-5264, 2010.
• [33] Y. H. Liang, “Integration of data mining technologies to analyze customer value for the automotive maintenance industry”, Expert systems with Applications, 37(12), pp. 7489-7496, 2010.
• [34] D. R. Liu, and Y. Y. Shih, “Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences”, Journal of Systems and Software, 77(2), pp. 181-191, 2005.
• [35] I. C. Yeh, K. J. Yang, and T. M. Ting, “Knowledge discovery on RFM model using Bernoulli sequence”, Expert Systems with Applications, 36(3), pp.5866-5871, 2009.
• [36] R. S. Winer, “A framework for customer relationship management”, California management review, 43(4), pp.89-105, 2001.
• [37] H. C. Chang, and H. P. Tsai, “Group RFM analysis as a novel framework to discover better customer consumption behavior”, Expert Systems with Applications, 38(12), pp.14499-14513, 2011.
• [38] Y. H. Hu, and T. W. Yeh, “Discovering valuable frequent patterns based on RFM analysis without customer identification information”, Knowledge-Based Systems, 61, pp. 76-88, 2014.
• [39] R. A. Soeini, and E. Fathalizade, “Customer Segmentation based on Modified RFM Model in the Insurance Industry”, Proceeding of the 4th International Conference on Machine Learning and Computing, pp. 101-104, 2012.
• [40] J.T. Wei, S. Y. Lin, C. C. Weng, and H. H. Wu, “A case study of applying LRFM model in market segmentation of a children’s dental clinic”, Expert Systems with Applications, 39(5), pp.5529-5533, 2012.
• [41] P. S. Fader, B. G. S. Hardie, and K. L. Lee, “RFM and CLV: Using iso-value curves for customer base analysis”, Journal of Marketing Research, 62, pp. 415–430, 2005.
• [42] K. Coussement, and K. W. De Bock, “Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning”, Journal of Business Research, 66(9), pp. 1629–1636, 2013.
• [43] W. Hengliang, and Z. Weiwei, “A Customer Churn Analysis Model in E-business Environment”, International Journal of Digital Content Technology and its Applications (JDCTA), 6(9), pp. 296–302, 2012.
• [44] E. W. T Ngai, L. Xiu, and D. C. K. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification”, Expert Systems with Application, 36(2), pp. 2592–2602, 2009.
• [45] X. Zhang, J. Zhu, S. Xu, and Y. Wan, “Predicting customer churn through interpersonal influence”, Knowledge-Based Systems, Volume 28, pp. 97–104, 2012.
• [46] K. Coussement, and D. Van den Poel, “Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers”, Expert Systems with Applications, 36(3), pp. 6127–6134, 2009.
• [47] V.L. Miguéis, D. Van den Poel, A. S. Camanho, and J. F. Cunha, “Modeling partial customer churn: On the value of first product-category purchase sequences”, Expert Systems with Applications, 39(12), pp. 11250–11256, 2012.
• [48] V.L. Miguéis, D. Van den Poel, A. S. Camanho, and J. F. Cunha, “Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences”, Advances in Data Analysis and Classification, 6(4), pp. 337-353, 2012.
• [49] Y. Huang, B. Huang, and M. T. Kechadi, “A Rule-Based Method for Customer Churn Prediction in Telecommunication Services”, Advances in Knowledge Discovery and Data Mining, 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, Proceedings, Part I, pp. 411-422, 2011.
• [50] K. Morik, and H. Köpcke, “Analysing customer churn in insurance data–a case study”, In European Conference on Principles of Data Mining and Knowledge Discovery, pp. 325-336, 2004.
• [51] M. Ballings, and D. Van den Poel, “Customer event history for churn prediction: How long is long enough?”, Expert Systems with Applications, 39(18), pp. 13517-13522, 2012.
• [52] I. Bose, and X. Chen, “Hybrid models using unsupervised clustering for prediction of customer churn”, Journal of Organizational Computing and Electronic Commerce, 19(2), pp. 133-151, 2009.
• [53] J. Burez, and D. Van den Poel, “Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department”, Expert Systems with Applications, 35(1), pp. 497-514, 2008.
• [54] J. Burez, and D. Van den Poel, “Handling class imbalance in customer churn prediction”, Expert Systems with Applications, 36(3), pp. 4626-4636, 2009.
• [55] Z.Y Chen, Z.P. Fan, and M. Sun, “A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data”, European Journal of Operational Research, 223(2), pp. 461-472, 2012.
• [56] K. Coussement, and P. Van den, “Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques”. Expert Systems with Applications, 34 (1), pp. 313-327, 2008.
• [57] K. Coussement, and P. Van den, “Integrating the voice of customers through call center emails into a decision support system for churn prediction”, Information & Management, 45(3), pp. 164-174, 2008.
• [58] K. Coussement, D. F. Benoit, and D. Van den Poel, “Improved marketing decision making in a customer churn prediction context using generalized additive models”, Expert Systems with Applications, 37(3), pp. 2132-2143, 2010.
• [59] K. W. De Bock, D. and Van den Poel, “An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction”, Expert Systems with Applications, 38(10), pp. 12293-12301, 2011.
• [60] K. W. De Bock, D. and Van den Poel, “Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models”, Expert Systems with Applications, 39(8), pp. 6816-6826, 2012.
• [61] B. Huang, T. Kechadi, B. Buckley, G. Kiernan, E. Keogh, T. and Rashid, “A new feature set with new window techniques for customer churn prediction in land-line telecommunications”, Expert Systems with Applications, 37(5), pp. 3657-3665, 2010.
• [62] B. Huang, B. Buckley, and T. Kechadi, “Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications”, Expert Systems with Applications, 37(5), pp. 3638-3646, 2010.
• [63] A. T. Jahromi, S. Stakhovych, and M. Ewing, “Managing B2B customer churn, retention and profitability”, Industrial Marketing Management, 43(7), pp. 1258-1268, 2014.
• [64] C. Kirui, L. Hong, W. Cheruiyot, and H. Kirui, “Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining”, IJCSI International Journal of Computer Science, 10(2), pp. 165-172, 2013.
• [65] Y. Lai, and J. Zeng, “Analysis of customer churn behavior in digital libraries”, Program: electronic library and information systems, 48(4), pp. 370-382, 2014.
• [66] S. Lessmann, and S. Voß, “A reference model for customer-centric data mining with support vector machines”, European Journal of Operational Research, 199(2), pp. 520-530, 2009.
• [67] C.S Lin, G. H. Tzeng, and Y. C. Chin, “Combined rough set theory and flow network graph to predict customer churn in credit card accounts”, Expert Systems with Applications, 38(1), pp. 8-15, 2011.
• [68] D. S. Liu, and C. H. Ju, “Customer Churn Analysis Model in Manufacturing Industry”, In Advanced Materials Research, 69, pp. 675-679, 2009.
• [69] N. Lu, H. Lin, J. Lu, and G. Zhang, “A customer churn prediction model in telecom industry using boosting”, Industrial Informatics, IEEE Transactions on, 10(2), pp. 1659-1665, 2014.
• [70] S. Nabavi, S. and Jafari, “Providing a Customer Churn Prediction Model Using Random Forest and Boosted TreesTechniques (Case Study: Solico Food Industries Group)”, Journal of Basic and Applied Scientific Research, 3 (6), pp. 1018-1026, 2013.
• [71] M. Owczarczuk, “Churn models for prepaid customers in the cellular telecommunication industry using large data marts”, Expert Systems with Applications, 37(6), pp. 4710–4712, 2010.
• [72] P. C. Pendharkar, “Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services”, Expert Systems with Applications, 36(3), pp. 6714- 6720, 2009.
• [73] J. Qi, L. Zhang, Y. Liu, L. Li, Y. Zhou, Y. Shen, L. Liang, and H. Li, “ADTreesLogit model for customer churn prediction”, Annuls of Operations Research, 168(1), pp. 247–265, 2009.
• [74] C. F. Tsai, and M. Y. Chen, “Variable selection by association rules for customer churn prediction of multimedia on demand”, Expert Systems with Applications, 37(3), pp. 2006-2015, 2010.
• [75] T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis, and K. C Chatzisavvas, “A comparison of machine learning techniques for customer churn prediction”, Simulation Modelling Practice and Theory, 55, pp. 1-9, 2015.
• [76] W. Verbeke, D. Martens, B. and Baesens, “Social network analysis for customer churn prediction”, Applied Soft Computing, 14, pp. 431-446, 2014.
• [77] W. Verbeke, D. Martens, C. Mues, and B. Baesens, “Building comprehensible customer churn prediction models with advanced rule induction techniques”, Expert Systems with Applications, 38(3), pp. 2354-2364, 2011.
• [78] T. Verbraken, W. Verbeke, and B. Baesens, “Profit optimizing customer churn prediction with Bayesian network classifiers”, Intelligent Data Analysis, 18(1), pp. 3-24, 2014.
• [79] Y. Xie, X. Li, E. W. T. Ngai, and W. Ying, “Customer churn prediction using improved balanced random forests”, Expert Systems with Applications, 36(3), pp. 5445 5449, 2009.
• [80] G. E. Xia, and W. D. Jin, “Model of customer churn prediction on support vector machine”, Systems Engineering-Theory & Practice, 28(1), pp. 71-77, 2008.
• [81] X. Yu, S. Guo, J. Guo, X. Huang, “An extended support vector machine forecasting framework for customer churn in e-commerce”, Expert Systems with Application, 38(3), pp. 1425–1430, 2011.
• [82] C. Bolancé, M. Guillen, and A. E. Padilla-Barreto, “Predicting probability of customer churn in insurance”, International Conference on Modeling and Simulation in Engineering, Economics and Management (pp. 82-91). Springer, Cham, 2016.
• [83] J. R. Miglautsch, “Thoughts on RFM scoring”, The Journal of Database Marketing, 8(1), pp.67-72, 2000.
• [84] J. Q. Zhang, A. Dixit, and R. Friedmann, “Customer loyalty and lifetime value: an empirical investigation of consumer-packaged goods”, Journal of Marketing Theory and Practice, 18(2), pp. 127-140, 2010.
• [85] B. Luo, P. Shao, and J. Liu, “Customer churn prediction based on the decision tree in personal handy phone system service”, International Conference on Service Systems and Service Management, pp. 1–5, 2007.
• [86] G. Li, and X. Deng, “Customer Churn Prediction of China Telecom Based on Cluster Analysis and Decision Tree Algorithm”, Emerging Research in Artificial Intelligence and Computational Intelligence, International Conference, AICI 2012, Springer Berlin Heidelberg, China, October 26-28, pp. 319-327, 2012.