Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining

Document Type : Research Paper

Authors

1 Prof. of System Engineering, Iran University of Science and Technology, Tehran, Iran

2 MSc. Student in Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

One of the most important issues in Customer Relationship Management is customer segmentation and product offer based on their needs. In practice, Customer’s behavior will change over the time by changes in technology, increase in the number of new customers and new competitors, and product variety. Traditional segmentation models that are static over time cannot predict these changes in customer’s behavior and ignore them. This challenge is especially critical in Telecommunication with high churn rates. In this research, we have used temporal fuzzy clustering to detect significant changes in customers' behavior for a telecom company during a 10-month period. The aim of this study is to find factors that affect structural and gradual changes in clustering model. In addition, we have suggested a method based on Frechet distance to extract similar patterns in customer’s usage behavior. Provided that combining the temporal clustering with trajectory analysis is an effective way to recognize customers’ behavior among the clusters, the results showed that there are seven distinct customer behavior patterns two of which lead to the customer drop or churn. These patterns can be used to reduce the risk and costs of customers churn and to design optimum services.

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آخوندزاده نوقابی، ا.، البدوی، ا.، اقدسی، م. (1393). کاوش پویایی مشتری در طراحی بخش‎بندی با استفاده از روش‎های داده‎کاوی. فصلنامۀ مدیریت فناوری اطلاعات، 6 (1)، 30-1.  
خدابنده‎لو، س.، نیک‎نفس، ع. ا. (1395). ارائۀ روشی جدید برای بخش‌بندی مشتریان بر اساس میزان وفاداری آنها و تعریف راهبردهایی مناسب برای هر بخش. فصلنامۀ مدیریت فناوری اطلاعات، 8 (1)، 122-101.
عزیزی، ش.، حسین‎آبادی، و.، بلاغی اینانلو، م. (1393). بخش‎بندی کاربران بانکداری اینترنتی بر مبنای انتظارات: رویکرد داده‌کاوی. فصلنامۀ مدیریت فناوری اطلاعات، 6 (3)، 434- 419.
کریمی علویجه، م. ح.، خدنگی، س.، ترکستانی، م. ص. (1395). روش فرا ابتکاری در یکپارچه‎سازی مدل بخش‎بندی بازار مشتریان تلفن همراه تهران با استفاده از شبکه‎های خودسازمان‎ده و روش میانگین کا. فصلنامۀ مدیریت فناوری اطلاعات، 8 (2)، 372- 351.
Akhundzadeh Noghabi, A., Albadvi, A. & Aghdasi, M. (2014). Mining customer dynamics in designing customer segmentation using data mining techniques. Information Technology Management, 6 (1), 1-30. (in Persian)
Angstenberger, L. (2001). Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering (1st ed.). Springer Netherlands.
Azizi, SH., Hosein Abadi, V. & Blaghi, M. (2014). Segmentation of Internet Banking Users Based on Expectations: A Data Mining Approach. Information Technology Management, 6 (3), 419-434. (in Persian)
Bae, S.M., Park, S.C. & Ha, S.H. (2003). Fuzzy Web Ad Selector Based on Web Usage Mining. IEEE Intelligent Systems, 18(6), 62–69.
Bae, S.M., Ha, H. & Park, S.C. (2005). A web-based system for analyzing the voices of call center customers in the service industry. Expert Systems with Applications, 28(1), 29-41.
Baesens, B., Viaene, S., Van Den Poel, D., Vanthienen, J. & Dedene, G. (2002). Bayesian neural network learning for repeat purchase modelling in direct marketing. European Journal of Operational Research, 138(1), 191–211.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191–203.
Bose, I. & Chen, X. (2014). Detecting temporal changes in customer behavior. In 2014 International Electrical Engineering Congress (iEECON) (pp. 1–4). IEEE.
 
Bose, I. & Chen, X. (2015). Detecting the migration of mobile service customers using fuzzy clustering. Information & Management, 52(2), 227–238.
Bray, J.P. (2008). Consumer Behaviour Theory: Approaches and Models. Available in: http://eprints.bournemouth.ac.uk/10107/.
Chen, N., Ribeiro, B., Vieira, A. & Chen, A. (2013). Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Systems with Applications, 40(1), 385–393.
Cho, Y.H. & Kim, J. K. (2004). Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26(2), 233–246.
Crespo, F. & Weber, R. (2005). A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets and Systems, 150(2), 267–284.
De Oliveira, J. V. & Pedrycz, W. (Eds.). (2007). Advances in fuzzy clustering and its applications. John Wiley & Sons.
Dennis, C., Marsland, D. & Cockett, T. (2001). Data Mining for Shopping Centres – Customer Knowledge-Management Framework. Journal of Knowledge Management, 5(4), 368–374.
Denny, Williams, G. J. & Christen, P. (2010). Visualizing temporal cluster changes using Relative Density Self-Organizing Maps. Knowledge and Information Systems, 25(2), 281–302.
Eiter, T. & Mannila, H. (1994). Computing discrete Fréchet distance. AAA: http://www.kr.tuwien.ac.at/staff/eiter/et-archive/cdtr9464.pdf.
Ha, H., Bae, S.M. & Park, S.C. (2002). Customer’s time-variant purchase behavior and corresponding marketing strategies: An online retailer's case. Computers and Industrial Engineering, 43(4), 801–820.
Kim, S.Y, Jung, T.S. & Suh, E. H. & Hwang, H.S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1), 101–107.
Karimi, M., Khadangi, S., Torkestani, M. (2016). Ultra Innovative Approach to Integrate Cellphone Customer Market Segmentation Model Using Self Organizing Maps and K-Means Methodology. Information Technology Management, 8 (2), 351-372. (in Persian)
Khodabandelu, S., Niknafs, A. (2016). Proposing a New Method for Customer Segmentation Based on Their Level of Loyalty and Defining Appropriate Strategies for Each Segment. Information Technology Management, 8 (1), 101-122. (in Persian)
Kotler, P. (2000). Marketing Management. Prentice-Hall, Englewood Cliffs, NJ.
Lariviere, B. & Van den Poel, D. (2005). Investigating the post-complaint period by means of survival analysis. Expert Systems with Applications, 29(3), 667-677.
Lee, S. C., Suh, Y. H., Kim, J. K. & Lee, K. J. (2004). A cross-national market segmentation of online game industry using SOM. Expert systems with applications, 27(4), 559-570.
Li, C. & Biswas, G. (2002). Applying the hidden Markov model methodology for unsupervised learning of temporal data. International Journal of Knowledge Based Intelligent Engineering Systems, 6(3), 152-160.
Minke, A., Ambrosi, K. & Hahne, F. (2009). Approach for dynamic problems in clustering. Information Technologies in Environmental Engineering, 373-386.
Prinzie, A. & Van den Poel, D. (2006). Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models. European Journal of Operational Research, 170(3), 710-734.
Sarlin, P. (2013). Self-organizing time map: An abstraction of temporal multivariate patterns. Neurocomputing, 99, 496-508.
Seret, A., Vanden Broucke, S. K., Baesens, B. & Vanthienen, J. (2013, August). An Exploratory Approach for Understanding Customer Behavior Processes Based on Clustering and Sequence Mining. In International Conference on Business Process Management (pp. 237-248). Springer, Cham.
Verdú, S. V., Garcia, M. O., Senabre, C., Marín, A. G. & Franco, F. G. (2006). Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Transactions on Power Systems, 21(4), 1672-1682.
Wei, C. P. & Chiu, I. T. (2002). Turning telecommunications call details to churn prediction: a data mining approach. Expert systems with applications, 23(2), 103-112.
Wu, C. H., Kao, S. C., Su, Y. Y. & Wu, C. C. (2005). Targeting customers via discovery knowledge for the insurance industry. Expert Systems with Applications, 29(2), 291-299.
Yang, Y. & Padmanabhan, B. (2005). GHIC: A hierarchical pattern-based clustering algorithm for grouping Web transactions. IEEE Transactions on Knowledge and Data Engineering, 17(9), 1300-1304.
Yang, Q. & Wu, X. (2006). 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making, 5(04), 597-604.
Yao, Z. (2013). Visual Customer Segmentation and Behavior Analysis A SOM-Based Approach. (Doctoral Dissertation). Turku Centre for Computer Science, Finland.
Ye, L., Qiuru, C., Haixu, X., Yijun, L. & Guangping, Z. (2013). Customer segmentation for telecom with the k-means clustering method. Information Technology Journal, 12(3), 409-413.
Zhu, T., Wang, B., Wu, B. & Zhu, C. (2011). Role defining using behavior-based clustering in telecommunication network. Expert Systems with Applications, 38(4), 3902-3908.