Applying Data Mining to Insurance Customer Churn Management

According to competition in insurance industry in Iran in recent years and entrance of private sector, keeping customers has become more important for insurer companies and reasons of churning is challenging. Thus in this research, data mining methods is used for Customer churn management (CCM). In first step, customers with equal characteristics were selected by clustering K-means method and in the second step, using churn index and decision tree CART, reasons of customer churn were analyzed. Data mining process was done by Clementine software on set of data gathered from seven "Iran Insurance" branches in Anzali as population size. Costumer clustering and knowing the reasons of churning by decision tree CART make company choose better policy to reduce that.

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

[2]  Vadlamani Ravi,et al.  Data Mining via Rules Extracted from GMDH: An Application to Predict Churn in Bank Credit Cards , 2010, KES.

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

[4]  Donald K. Wedding,et al.  Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..

[5]  Shu-Chen Kao,et al.  Targeting customers via discovery knowledge for the insurance industry , 2005, Expert Syst. Appl..

[6]  Vadlamani Ravi,et al.  Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards , 2009, RSFDGrC.

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

[8]  Cao Kang,et al.  Customer Churn Prediction Based on SVM-RFE , 2008, 2008 International Seminar on Business and Information Management.

[9]  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 .

[10]  Jing Zhao,et al.  Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[11]  Rupesh K. Gopal,et al.  Customer Churn Time Prediction in Mobile Telecommunication Industry Using Ordinal Regression , 2008, PAKDD.

[12]  Hong Xu,et al.  Churn Prediction in Telecom Using a Hybrid Two-phase Feature Selection Method , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[13]  Maurizio Naldi.  A Simple Model for the Effectiveness of Delaying Strategies for Telecommunications Churn Reduction , 2008, Tenth International Conference on Computer Modeling and Simulation (uksim 2008).

[14]  Ning Wang,et al.  Credit card customer churn prediction based on the RST and LS-SVM , 2009, 2009 6th International Conference on Service Systems and Service Management.

[15]  Chen Yun,et al.  Predict the churn and silent customers: A case study of individual investors , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[16]  Chen Wang,et al.  An experimental study on four models of customer churn prediction , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[17]  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.

[18]  Bart Baesens,et al.  Modeling churn using customer lifetime value , 2009, Eur. J. Oper. Res..

[19]  Przemyslaw Kazienko,et al.  The Influence of Customer Churn and Acquisition on Value Dynamics of Social Neighbourhoods , 2009, WSKS.

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

[21]  Yeli Li,et al.  Behavior-Based Telecommunication Churn Prediction with Neural Network Approach , 2011, 2011 International Symposium on Computer Science and Society.

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

[23]  Jiayin Qi,et al.  A hybrid KNN-LR classifier and its application in customer churn prediction , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[24]  W. Flannery,et al.  A study of the relationships between service failures and customer churn in a telecommunications environment , 2009, PICMET '09 - 2009 Portland International Conference on Management of Engineering & Technology.

[25]  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..

[26]  E Xu,et al.  An Algorithm for Predicting Customer Churn via BP Neural Network Based on Rough Set , 2006, 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06).

[27]  Dirk Van den Poel,et al.  Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department , 2008, Expert Syst. Appl..

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

[29]  Y. Ilker Topcu,et al.  Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey , 2011, Expert Syst. Appl..

[30]  Wei Jiang,et al.  Churn detection via customer profile modelling , 2006 .

[31]  Dan Zhang,et al.  A Study on Prediction of Customer Churn in Fixed Communication Network Based on Data Mining , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

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

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

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

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

[36]  Li Xiu,et al.  The Application ofAdaBoost in Customer Churn Prediction , 2007, 2007 International Conference on Service Systems and Service Management.

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

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

[39]  N. F. F. Ebecken,et al.  Telco Churn Analysis Classification Using AWavelet And RBF Approach , 2006 .

[40]  Sun Kim,et al.  An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case , 2005, ICNC.

[41]  Zhou Min,et al.  Study on the Dual Optimization of Service Marketing and Customer Churn Management , 2008, 2008 International Seminar on Business and Information Management.

[42]  Kristof Coussement,et al.  Integrating the voice of customers through call center emails into a decision support system for churn prediction , 2008, Inf. Manag..

[43]  Dirk C. Mattfeld,et al.  Synergies of Operations Research and Data Mining , 2010, Eur. J. Oper. Res..

[44]  Yeon Hur,et al.  Customer Churning Prediction Using Support Vector Machines in Online Auto Insurance Service , 2005, ISNN.

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

[46]  Yong Shi,et al.  The Analysis on the Customers Churn of Charge Email Based on Data Mining Take One Internet Company for Example , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[47]  Kun Chang Lee,et al.  Bayesian Network Approach to Predict Mobile Churn Motivations: Emphasis on General Bayesian Network, Markov Blanket, and What-If Simulation , 2010, FGIT.

[48]  Behzad Moshiri,et al.  A Hybrid Approach to Predict Churn , 2010, 2010 IEEE Asia-Pacific Services Computing Conference.

[49]  Bogdan Gabrys,et al.  A Non-sequential Representation of Sequential Data for Churn Prediction , 2009, KES.

[50]  Luo Bin,et al.  Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service , 2007, 2007 International Conference on Service Systems and Service Management.

[51]  Hee-Su Kim,et al.  Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market , 2004 .

[52]  Gwo-Hshiung Tzeng,et al.  Combined rough set theory and flow network graph to predict customer churn in credit card accounts , 2011, Expert Syst. Appl..

[53]  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.

[54]  Xiaohang Zhang,et al.  Predicting customer churn by integrating the effect of the customer contact network , 2010, Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics.

[55]  Katharina Morik,et al.  Analysing Customer Churn in Insurance Data - A Case Study , 2004, PKDD.

[56]  Lijia Guo,et al.  Applying Data Mining Techniques in Property/Casualty Insurance , 2003 .

[57]  Vadlamani Ravi,et al.  Predicting credit card customer churn in banks using data mining , 2008, Int. J. Data Anal. Tech. Strateg..

[58]  Zhenyu Chen,et al.  Building comprehensible customer churn prediction models: A multiple kernel support vector machines approach , 2011, ICSSSM11.

[59]  M. Tahar Kechadi,et al.  Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications , 2010, Expert Syst. Appl..