Evalu ations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn Prediction: Case Study Insurance Industry

C ompetitive advantage for survival and maintenance of the old companies to new companies need to identify accurately understand behavior customers. So many different ways for organizations to predict the company's customers churn. The most common methods of predicting customer churn, data mining methods. Data mining methods to determine the optimal method of prediction is of special importance. So in this article using Clementine software and the database contains 300 records of customers Iran Insurance Company in the city of Anzali, Iran will be collected using a questionnaire. First, determine the optimal number of clusters in K-means clustering and clustering customers based on demographic variables. And then the second step is to evaluate binary classification methods (decision tree QUEST, decision tree C5.0, decision tree CHAID, decision trees CART, Bayesian networks, Neural networks) to predict customers churn. Keyw ords: Dat a mining, Customer churn prediction, K-means clustering, classification binary, insurance

[1]  Dirk Van den Poel,et al.  Customer attrition analysis for financial services using proportional hazard models , 2004, Eur. J. Oper. Res..

[2]  Kristof Coussement,et al.  Improved marketing decision making in a customer churn prediction context using generalized additive models , 2010, Expert Syst. Appl..

[3]  Bojana Dalbelo.  Churn Prediction Model in Retail Banking Using Fuzzy C-Means Algorithm , 2009 .

[4]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[5]  Yuan-yuan Zhang,et al.  An Empirical Study of Customer Churn in E-Commerce Based on Data Mining , 2010, 2010 International Conference on Management and Service Science.

[6]  Bart Baesens,et al.  Ant-Based Approach to the Knowledge Fusion Problem , 2006, ANTS Workshop.

[7]  Dirk Van den Poel,et al.  Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting , 2005, Eur. J. Oper. Res..

[8]  Chih-Ping Wei,et al.  Turning telecommunications call details to churn prediction: a data mining approach , 2002, Expert Syst. Appl..

[9]  Eric W. T. Ngai,et al.  Customer churn prediction using improved balanced random forests , 2009, Expert Syst. Appl..

[10]  Cheng-Seen Ho,et al.  Toward a hybrid data mining model for customer retention , 2007, Knowl. Based Syst..

[11]  Kweku-Muata Osei-Bryson,et al.  Evaluation of decision trees: a multi-criteria approach , 2004, Comput. Oper. Res..

[12]  Chih-Fong Tsai,et al.  Variable selection by association rules for customer churn prediction of multimedia on demand , 2010, Expert Syst. Appl..

[13]  Xin Yao,et al.  A novel evolutionary data mining algorithm with applications to churn prediction , 2003, IEEE Trans. Evol. Comput..

[14]  Ashutosh Tiwari,et al.  Computer assisted customer churn management: State-of-the-art and future trends , 2007, Comput. Oper. Res..

[15]  Chih-Fong Tsai,et al.  Customer churn prediction by hybrid neural networks , 2009, Expert Syst. Appl..

[16]  Guo-en Xia,et al.  Model of Customer Churn Prediction on Support Vector Machine , 2008 .

[17]  David C. Yen,et al.  Applying data mining to telecom churn management , 2006, Expert Syst. Appl..

[18]  Bart Baesens,et al.  Building comprehensible customer churn prediction models with advanced rule induction techniques , 2011, Expert Syst. Appl..

[19]  Girish Keshav Palshikar,et al.  Employee churn prediction , 2011, Expert Syst. Appl..

[20]  Koen W. De Bock,et al.  Ensembles of Probability Estimation Trees for Customer Churn Prediction , 2010, IEA/AIE.

[21]  Daniel Westreich,et al.  Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. , 2010, Journal of clinical epidemiology.

[22]  Dirk Van den Poel,et al.  CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services , 2007, Expert Syst. Appl..

[23]  Cheng-Jung Lin,et al.  Goal-oriented sequential pattern for network banking churn analysis , 2003, Expert Syst. Appl..

[24]  Wagner A. Kamakura,et al.  Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models , 2006 .

[25]  Kristof Coussement,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .

[26]  Bart Baesens,et al.  Domain knowledge integration in data mining using decision tables: case studies in churn prediction , 2009, J. Oper. Res. Soc..

[27]  Jiawei Han,et al.  Data Mining: Concepts and Techniques, Second Edition , 2006, The Morgan Kaufmann series in data management systems.