IJE TRANSACTIONS C: Aspects Vol. 27, No. 9 (September 2014) 1405-1414    Article Under Proof

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M. Mohammadi, S. H. Iranmanesh, R. Tavakkoli-Moghaddam and M. Abdollahzadeh
( Received: March 06, 2013 – Accepted: May 22, 2014 )

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.



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