Predicting Customer Churn Using CLV in Insurance Industry
الموضوعات :Vahid Dust Mohammadi 1 , Amir Albadvi 2 , Babak Teymorpur 3
1 - Department of Industrial Engineering,
Tarbit Modares University,
Tehran, Iran
2 - Department of Industrial Engineering,
Tarbit Modares University,
Tehran, Iran
3 - Department of Industrial Engineering,
Tarbit Modares University,
Tehran, Iran
الکلمات المفتاحية: Insurance Industry, customer lifetime value, Logistic regression, Customer churn, k-means clustering,
ملخص المقالة :
Today, increased level of customer awareness caused themto access to the other suppliers easily and they can get their servicesfrom the competitors with similar or even better quality and same price.Therefore, focusing on customers and preventing them to leave, has beenthe most important strategy for any company. Researches have shownthat retaining former customers is cheaper than attracting new ones. Inthe proposed model in this article we first identified important factorscausing customers in insurance industry, to have a specific behavior byusing a k-means clustering algorithm, and then we tried to predict thefuture behavior of them by a logistic regression. Our model accuracy is98%.
[1] Berry, M. J. and Linoff, G. S. (2004), Data mining techniques: for marketing,
sales, and customer relationship management: John Wiley & Sons.
[2] Turban, E., Sharda, R., Delen, D., and Efraim, T. (2007), Decision support
and business intelligence systems: Pearson Education India.
[3] Tsai, C. F. and Lu, Y. H. (2009), “Customer churn prediction by hybrid
neural networks,” Expert Systems with Applications, vol. 36, pp. 12547-
12553.
[4] Ling, R. and Yen, D. C. (2001), “Customer relationship management: An
analysis framework and implementation strategies,” Journal of Computer
Information Systems, vol. 41, pp. 82-97.
[5] KhakAbi, S., Gholamian, M. R., and Namvar, M. (2010), “Data mining
applications in customer churn management,” in Intelligent Systems,
Modelling and Simulation (ISMS), International Conference on, pp. 220-
225.
[6] Zhang, Y., Liang, R., Li, Y., Zheng, Y., and Berry, M. (2011), “Behavior-
Based Telecommunication Churn Prediction with Neural Network Approach,”
in Computer Science and Society (ISCCS), International Symposium
on, pp. 307-310.
[7] Benoit, D. F. and Van den Poel, D. (2012), “Improving customer retention
in financial services using kinship network information,” Expert Systems
with Applications, vol. 39, pp. 11435-11442.
[8] Kracklauer, A., Mills, H. D. Q., and Seifert, D. (2004), “Customer
management as the origin of collaborative customer relationship management,”
in Collaborative Customer Relationship Management, Ed:
Springer, pp. 3-6.
[9] Kajvary, (2013), “important factors in brand equity in insurance industry
in customer perspective,” insurance journal.
[10] Chu, B. H., Tsai, M. S., and Ho, C. S. (2007), “Toward a hybrid data
mining model for customer retention,” Knowledge-Based Systems, vol.
20, pp. 703-718.
[11] Kim, H. S. and Yoon, C. H. (2004), “Determinants of subscriber churn
and customer loyalty in the Korean mobile telephony market,” Telecommunications
Policy, vol. 28, pp. 751-765.
[12] Bahman, A. (2014), “modeling CLV in insurane industry,” engineering,
tarbiat modares, Iran.
[13] Malthouse, E. C. and Blattberg, R. C. (2005), “Can we predict customer
lifetime value?” Journal of Interactive Marketing, vol. 19, pp. 2-16.
[14] Amin, R. (2011), “using predicted improvement factors for communication
creteria in binary logistic,” operation management.
