Abstract    As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers a hierarchical model by combining three data mining techniques containing two different fuzzy prediction networks and a regression technique for churn prediction, namely Alpha-cut Fuzzy C-Means (αFCM), Improved Fuzzy ARTMAP and Cox proportional hazards regression model, respectively. In particular, the first component of the hybrid model aims to cluster data in two churner and non-churner groups applying the alpha-cut algorithm and filter out unrepresentative data or outliers. Then, the clustered and representative data as the outputs are used to assign customers to churner and non-churner groups by the second technique. Finally, the correctly classified data are used to create the Cox proportional hazards model. To evaluate the performance of the proposed hierarchical model, the Iranian mobile dataset is considered. The experimental results show that the proposed model outperforms the single Cox regression baseline model in terms of prediction accuracy, Type I and II errors, RMSE and MAD metrics.

Keywords    Fuzzy ARTMAP, Fuzzy C-Means, Cox Regression, Customer Relationship management, Churn Prediction

چکیده    از آنجایی که مشتریان هر سازمان جزء ارزشمندترین دارایی­های آن سازمان محسوب می­شود، بنابراین مدیریت ریزش مشتریان به یکی از وظایف اصلی سازمان­ها برای جلوگیری از ریزش مشتریانشان تبدیل شده است. از طرف دیگر، کاربردپذیری و عملکرد بالای روش­های داده کاوی سلسله مراتبی در ادبیات موضوع به اثبات رسیده است. در این مقاله از یک روش سلسله مراتبی شامل سه روش متفاوت αFCM، Fuzzy ARTMAP و رگرسیون کاکس (Cox regression) استفاده شده است به گونه­ای که روش اول بمنظور خوشه­بندی داده­های اولیه به دو دسته ریزش­یافته و ریزش­نیافته است. روش دوم مشتریان مختلف را به این دو کلاس طبقه بندی می­کند و در نهایت تابع بقا و ریزش مشتریان بدست می­آید. جهت اعتبار­سنجی روش سلسله مراتبی پیشنهادی از داده­های یکی از اپراتورهای تلفن همراه ایران استفاده شده است و نتایج حاصل از منظر دقت پیش­بینی، خطاهای نوع اول و دوم، ریشه میانگین مربعات خطا (RMSE) و مقدار مطلق خطا با روش پایه­ای رگرسیون کاکس مقایسه شده­اند.

References      1.        Hung, C., Tsai, C.F., “Segmentation based on hierarchical self-organizing map for markets of multimedia on demand”, Expert Systems with Applications, Vol. 34, No. 1, (2008), 780–787. 2.        Berry, M.J.A., Linoff, G.S. “Data mining techniques: For marketing, sales, and customer support”, John Wiley & Sons, (2003). 3.        Van den Poel, D., Lariviere, B., “Customer attrition analysis for financial services using proportional hazard models”, European Journal of Operational Research, Vol. 157, (2004), 196–217. 4.        Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., & Dedene, G. “Bayesian neural network learning for repeat purchase modeling in direct marketing”, European Journal of Operational Research, Vol. 138, No. 1, (2002), 191–211. 5.        Coussement, K., Van den Poel, D., “Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques”, Expert Systems with Applications, Vol. 34, No. 1, (2008), 313–327. 6.        Hung, S.Y., Yen, D.C., Wang, H.Y. “Applying data mining to telecomm churn management”, Expert Systems with Applications, Vol. 31, No. 3, (2006), 515–524. 7.        Bhattacharyya, S., Pendharkar, P.C. “Inductive, evolutionary and neural techniques for discrimination: A comparative study”, Decision Sciences, Vol. 29, (1998), 871–900. 8.        Fayyad, U., Uthurusamy, R., “Data mining and knowledge discovery in databases”, Communications of the ACM, Vol. 39, (1996), 24–27. 9.        Ngai, E.W.T., Xiu, L., Chau, D.C.K., “Application of data mining techniques in customer relationship management: A literature review and classification”, Expert Systems with Applications, Vol. 36, No. 2, (2009), 2592–2602. 10.     Tsai, Ch., Lu, Y., “Customer churn prediction by hybrid neural networks”, Expert Systems with Applications, Vol. 36, (2009), 12547–12553. 11.     Lenard, M.J., Madey, G.R., Alam, P., “The design and validation of a hybrid information system for the auditor’s going concern decision”, Journal of Management Information Systems, Vol. 14, No. 4, (1998), 219–237. 12.     Rumelhart, D.E., Hinton, G.E., Williams, R.J. “Parallel distributed processing: Explorations in the microstructure of cognition”, Chapter on learning internal representations by error propagation, Vol. 1, (1986), 318–362. MIT Press. 13.     Pendharkar, P.C., “A comparison of gradient ascent, gradient descent and genetic-algorithm based artificial neural networks for the binary classification problem”, Expert Systems with Application, Vol. 24, No. 2, (2007), 65–86. 14.     Pendharkar, P.C., Nanda, S., “A misclassification cost minimizing evolutionary-neural classification approach”, Naval Research Logistics, Vol. 53, No. 5, (2006), 432–447. 15.     Pendharkar, P.C., Rodger, J.A., “An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification”, Computers and Operations Research, Vol. 31, (2004), 481–498. 16.     Jain, A., Murty, M., Flyn, P., “Data clustering: A review”, ACM Computing Surveys, Vol. 31, (1999), 264–323. 17.     Dunn, J., “A fuzzy relative of the ISO-data process and its use in detecting compact, well-separated clusters”, Journal of Cybernetics, Vol. 3, No. 3, (1973), 32–57. 18.     Han, J., Kamber, M., “Data Mining: Concepts and Techniques”, Morgan Kaufmann, (2001). 19.     Carpenter, G.A., Grossberg, S., Reynolds, J.H., “ARTMAP: supervised real-time learning and classification of non-stationary data by a self-organizing neural network”, Neural Networks, Vol. 4, (1991), 565-588. 20.     Granger, E., Rubin, M., Grossberg, S., Lavoie, P., “A what-and-where fusion neural network for recognition and tracking of multiple radar emitters”, Neural Networks, Vol. 14, (2001), 325-344. 21.     Cox, D.R., Oakes, D., “Analysis of Survival Data”, London: Chapman and Hall, (1984). 22.     Burez, J., Van den Poel, D., “Crm at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services”, Expert Systems with Applications, Vol. 32, (2007), 277–288. 23.     Burez, J., Van den Poel, D., “Handling class imbalance in customer churn prediction”, Expert Systems with Applications, Vol. 36, No. 3, (2009), 4626-4636. 24.     Buckinx, W., Van den Poel, D., “Customer base analysis: partial defection of behaviorally loyal clients in a non-contractual FMCG retail setting”, European Journal of Operational Research, Vol. 164, No.1, (2005), 252–268. 25.     Buckinx, W., Verstraeten, G., Van den Poel, D., “Predicting customer loyalty using the internal transactional database”, Expert Systems with Applications, Vol. 32, (2007), 125–134. 26.     Coussement, K., Van den Poel, D., “Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers”, Expert Systems with Applications, Vol. 36, (2009) 6127–6134. 27.     Eshghi, A., Haughton, D., Topi, H., “Determinants of customer loyalty in the wireless telecommunications industry”, Telecommunications Policy, Vol. 31, (2007), 93–106. 28.     Gerpott, T. J., Rams, W., Schindler, A., “Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market”, Telecommunications Policy, Vol. 25, (2001), 249–269. 29.     Glady, N., Baesens, B., Croux, Ch., “Modeling churn using customer lifetime value”, European Journal of Operational Research, Vol. 197, (2009), 402–411. 30.     Kim, H.S., Yoon, C.H., “Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market”, Telecommunications Policy, Vol. 28, No. 9/10, (2004), 751–765. 31.     Kim, M.K., Park, M.C., Jeong, D.H., “The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services”, Telecommunications Policy, Vol. 28, (2004), 145–159. 32.     Mazzoni, C., Castaldi, L., Addeo, F., “Consumer behavior in the Italian mobile telecommunication market”, Telecommunications Policy, Vol. 31, (2007), 632–647. 33.     Pendharkar, P., C. “Customer Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services”, Expert Systems with Applications, Vol. 36, (2009), 6714–6720. 34.  Seo, D., Ranganathan, C., Babad, Y., “Two-level model of customer retention in the US mobile telecommunications service market”, Telecommunications Policy, Vol. 32, (2008), 182–196. 35.     Tsai, C., F., Chen, M., Y., “Variable selection by association rules for customer churn prediction of multimedia on demand”, Expert Systems with Applications, Vol. 37, (2010), 2006–2015. 36.     Van den Poel, D., Burez, J., “Handling class imbalance in customer churn prediction”, Expert Systems with Applications, Vol. 36, (2009), 4626–4636. 37.     Verbeke, W., Martens, D., Mues, Ch., Baesens, B., “Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications”, Vol. 38, (2011), 2354–2364. 38.     Wei, C.P., Chiu, I.T., “Turning telecommunications call details to churn prediction: A data mining approach”, Expert Systems with Applications, Vol. 23, No. 2 , (2002), 103–112. 39.     Zhao, Y., Li, B., Li, X., Liu, W., Ren, S., “Customer Churn Prediction Using Improved One-Class Support Vector Machine”, Advanced Data Mining and Applications, Vol. 3584, (2005), 300-306. 40.     Idris, A., Khan, A., Lee, Y.S., “Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification”, Applied Intelligence, Vol. 39 , No.3, (2013), 659-672. 41.     Phadke, C., Uzunalioglu, H., Mendiratta, V. B., Kushnir, D., Doran, D., “Prediction of Subscriber Churn Using Social Network Analysis” Bell Labs Technical Journal, Vol. 17, No. 4, 63-75. 42.     Farquad, M.A.H., Ravi, V., Bapi Raju, S., “Churn prediction using comprehensible support vector machine: An analytical CRM application”, Applied Soft Computing, Vol. 19, (2014), 31-40.