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소셜 네트워크 분석을 기반으로 한 이동통신 잠재고객 이탈에 대한 연구

Analysis to Customer Churn Provoker's Roles Using Call Network of a Telecom Company

  • 전희주 (동덕여자대학교 통계학과) ;
  • 임병학 (부산외국어대학교 데이터경영학과)
  • Chun, Heuiju (Department of Statistics & Information, Dongduk Women's University) ;
  • Leem, Byunghak (Division of Business Administration, Busan University of Foreign Studies)
  • 투고 : 2012.10.04
  • 심사 : 2012.12.10
  • 발행 : 2013.02.28

초록

본 연구에서는 국내 한 이동통신회사의 해지 고객 중심의 통화 네트워크 데이터를 가지고 고객들 간의 관계 구조를 나타내는 소셜 네트워크 분석의 일종인 자아 네트워크(Ego-Network) 분석을 통해 핵심 연결자 역할 및 중개 역할을 하는 이탈고객이 다른 고객의 이탈에 어떻게 영향을 미쳤는지를 분석하고 이를 기반으로 고객 이탈 예측 및 이탈방지를 위한 방안을 제시하고자 한다. 해지고객들 간 양방향 통화를 갖는 네트워크를 살펴본 결과, 해지고객들 간의 무더기 이탈 현상을 확인할 수 있었다. 이러한 이탈 그룹에는 그룹 이탈에 영향을 주는 이탈유발자가 존재하고 있었으며, 이러한 이탈유발자의 특징은 그룹 내에서 많은 구성원들과 연결되어 있는 핵심 연결자 역할을 하면서, 정보전달의 매개자 역할을 동시에 해내는 고객이었다. 즉 긴밀한 네트워크일수록, 이탈유발자 비중이 높고, 이들 이탈유발자와의 관계에 의한 이탈현상은 이탈유발자의 영향이 큰 것으로 볼 수 있을 것이다.

In this study, we investigate how churn customers (who play a central connector or broker role) affect other customers' churn in their call networks with ego-network analysis using call data of a mobile telecom company in Korea. As a result of investigating Reciprocal Network, we found a relationship of attrition among churn customers. Churn provokers who influence other customers' attrition exist in customer churn networks. The characteristics of churn provokers is that they play a central connector and broker role in their groups. The proportion of churn provokers increases and the churn provoker's influence increases because the network is a reciprocal one.

키워드

참고문헌

  1. Baran, R. J., Galka, R. J. and Strunk, D. P. (2008). Principles of Customer Relationship Management, Thomson South-Western.
  2. Buckinx , W. and Poel, D. V. D. (2003). Customer base analysis: Partial defection of behaviorally-loyal clients in a non-contractual FMCG retail setting, European Journal of Operational Research, 164, 252-268.
  3. Chun, H. (2008). Customer loyalty score model development, Korean Journal of Applied Statistics, 21, 1-9. https://doi.org/10.5351/KJAS.2008.21.1.001
  4. Chun, H. (2010). Membership subscription effect and positioning study of a mobile telephone company, Journal of the Korean Data Analysis Society, 12, 973-982.
  5. Coussement, K. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter selection techniques, Expert Systems with Applications, 34, 313-327 https://doi.org/10.1016/j.eswa.2006.09.038
  6. Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A. and Joshi, A. (2008). Social ties and their relevance to churn in mobile telecom networks. EDBT '08: Proceedings of the 11th international conference on Extending database technology, 668-677, New York, NY, USA.
  7. Datta, P., Masand, B., Mani, D. R. and Li, B. (2000). Automated cellular modeling and prediction on a large scale, Artificial Intelligence Review, 14, 485-502. https://doi.org/10.1023/A:1006643109702
  8. Dyche, J. (2002). The CRM Handbook: A Business Guide to Customer Relationship Management, Addison Wesley Professional.
  9. Goldenberg, J., Libai, B. and Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth, Marketing Letters, 12, 211-223. https://doi.org/10.1023/A:1011122126881
  10. Gopal, R. K. and Meher, S. K. (2008). Customer Churn Time Prediction in mobile telecommunication industry using ordinal regression, Advances in Knowledge Discovery and Data Mining, 5012, 884-889. https://doi.org/10.1007/978-3-540-68125-0_88
  11. Granovetter, M. (1978). Threshold models of collective behavior, American Journal of Sociology, 83, 1420-1443. https://doi.org/10.1086/226707
  12. Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N. and Sriram, S. (2006). Modeling customer lifetime value, Journal of Service Research, 9, 139-155. https://doi.org/10.1177/1094670506293810
  13. Hadden, J., Tiwari, A., Roy, R. and Ruta, D. (2006). Churn prediction using complaints data, Proceedings of World Academy Of Science, Engineering and Technology, 13, 158-163.
  14. Han, S., Kangg, H., Choi, H., Do, J. and Shin, S. (2009). A study on development of customer attrition model in financial institution data, Journal of the Korean Data Analysis Society, 11, 279-288.
  15. Hill, S., Provost, F. and Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks, Statistical Science, 22, 256-275.
  16. Hwang, H., Jung, T. and Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry, Expert Systems with Applications, 26, 181-188. https://doi.org/10.1016/S0957-4174(03)00133-7
  17. Jones, M. A., Mothersbaughb, D. L. and Beattyc, S. E. (2000). Switching barriers and repurchase intentions in services, Journal of Retailing, 76, 259-274. https://doi.org/10.1016/S0022-4359(00)00024-5
  18. Keaveney, S. M. (1995). Customer switching behavior in service industries: An exploratory study, The Journal of Marketing, 59, 71-82.
  19. Kim, Y. (2007). Social Network Analysis, Parkyoungsa, Seoul.
  20. Knoke, D. and Yang, S. (2008). Social Network Analysis, 2nd, SAGE Publications.
  21. Lu, J. (2002). Predicting customer churn in the telecommunications industry an application of survival analysis modeling using SAS SUGI27.
  22. Mavri, M. (2008). Customer switching behavior in greek banking services using survival analysis, Managerial Finance, 34, 186-197. https://doi.org/10.1108/03074350810848063
  23. Milgram, S. (1967). The Small World Problem, Physiology Today, 2, 60-67.
  24. Morik, K. and Kopcke, H. (2004). Analysing customer churn in insurance data: a case study, PKDD'04: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases..
  25. Ng, K. and Liu, H. (2000). Customer retention via data mining, Artificial Intelligence Review, 14, 569-590. https://doi.org/10.1023/A:1006676015154
  26. Reichheld, P. F. (1996). The Loyalty Effect: The Hidden Force Behind Growth, Profits, and Lasting Value, Cambridge University Press.
  27. Sohn, D. W. (2002). Social Network Analysis, Kyungmoonsa, Seoul.
  28. Wasserman, S. and Faust, K. (1994). Social Network Analysis, Cambridge University Press.

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