Korean Management Science Review (경영과학)

The Korean Operations Research and Management Science Society (한국경영과학회)
권호 표지

Social Network Analysis to Analyze the Purchase Behavior Of Churning Customers and Loyal Customers

사회 네트워크 분석을 이용한 충성고객과 이탈고객의 구매 특성 비교 연구

  • Published : 2009.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Customer retention has been a pressing issue for companies to get and maintain the loyal customers in the competing environment. Lots of researchers make effort to seek the characteristics of the churning customers and the loyal customers using the data mining techniques such as decision tree. However, such existing researches don't consider relationships among customers. Social network analysis has been used to search relationships among social entities such as genetics network, traffic network, organization network and so on. In this study, a customer network is proposed to investigate the differences of network characteristics of churning customers and loyal customers. The customer networks are constructed by analyzing the real purchase data collected from a Korean cosmetic provider. We investigated whether the churning customers and the loyal customers have different degree centralities and densities of the customer networks. In addition, we compared products purchased by the churning customers and those by the loyal customers. Our data analysis results indicate that degree centrality and density of the churning customer network are higher than those of the loyal customer network, and the various products are purchased by churning customers rather than by the loyal customers. We expect that the suggested social network analysis is used to as a complementary analysis methodology with existing statistical analysis and data mining analysis.

Keywords

References

  1. 김용학, ‘사회연결망 분석’, 박영사, 2003
  2. 김용학, ‘사회연결망 이론’, 박영사, 2004
  3. 손동원, ‘사회 네트워크 분석’, 경문사, 2002
  4. 윤영수, 채승병, '복잡계 개론', 삼성경제연구소, 2007
  5. 김상용, 송지연, 이기순, 'CRM 고객 데이터 분석을 통한 이탈고객 연구', '한국마케팅저널', 제7권, 제1호(2005), pp.21-42
  6. 김재경, 채경희, 송희석, 'SOM을 이용한 온라인 게임 제공업체의 고객이탈방지 방법론', '경영과학', 제21권 제3호(2004), pp.85-99
  7. 박수진, 박길순, 김서연, '여대생의 메이크업에 대한 행동 및 의식 조사연구-대전, 충남지역을 중심으로', '한국패션뷰티학회지, 제4권, 제4호(2006), pp.87-99
  8. 변현수, 김진화, '인터넷 쇼핑몰 사용자가 구축하는 연결망 구조분석을 통한 구매행동 모델링', '대한경영학회지' 제20권, 제5호(2007), pp.2069-2091
  9. 송기룡, 노성호, 이재광, 최일영, 김재경, '고객의 동적 선호 탐색을 위한 순차패턴 분석: (주)더페이스샵 사례', 'Information Systems Review', 제10권, 제2호(2008), pp.195-209
  10. 윤성준, '데이터마이닝 기법을 통한 백화점의 고개이탈예측 모형 연구', '한국마케팅저널', 제6권, 제4호(2005), pp.45-72
  11. 이희연, 김홍주, '네트워트 분석을 통한 수도권의 공간구조 변화 1980-2000년', '국토계획', 제41권, 제1호(2006), pp.133-150
  12. 장덕진, '연결망과 사회이론', '사회와 이론' 제4집(2003), pp.11-61
  13. 조영빈, 조윤호, '구매순서를 고려한 개선된 협업필터링 방법론', '한국지능정보시스템학회 논문지', 제13권, 제2호(2007), pp.69-80
  14. 최창현, '조직의 비공식 연결망에 관한 연구: 사회연결망 분석의 적용', '한국사회와 행정연구', 제17권, 제1호(2006), pp.1-23
  15. A. Berson, S. Smith, K. Thearling, Building data mining applications for CRM McGraw-Hill, 2000
  16. Kauffiman, S., The Origins of Order, OxfordUniversity Press, Oxford, 1993
  17. Breiman, L., J.H. Freidman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees, Wadsworth and Brooks/Cole Advanced Books and Software, Pacific Grove, California 1984
  18. Anderson, C., A. HeIlervik, and K. Lindgren, 'A Spatial Network Explanation for a Hierarchy of Urban Power Laws', Physica A : Statistical Mechanics and its Applications, Vol.345(2004), pp.227-244 https://doi.org/10.1016/j.physa.2004.07.027|
  19. Barnes, J. 'Class and committees in a Nor wegian island parish,' Human Relations, Vol.7(1954), pp.39-58 https://doi.org/10.1177/001872675400700102
  20. Bonacich, P. 'Power and Centrality: A Family of Measures,' American Journal of Sociology, Vol.92(1987), pp.1170-1182 https://doi.org/10.1086/228631
  21. Cho, Y.H. and J.K. Kim, 'Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce,' Expert Systems with Applications, Vol.26(2004), pp,233-246 https://doi.org/10.1016/S0957-4174(03)00138-6
  22. Cho, Y.H., J.K. Kim, and S.H. Kim, 'A personalized Recommender System based on Web Usage Mining and Decision Tree Induction,' Expert Systems with Applications, Vol.23(2002), pp.329-342 https://doi.org/10.1016/S0957-4174(02)00052-0
  23. Kim, Y, H. Song, and S. Kim, 'A dynamic procedure for defection detection and prevention based on SOM and a Markov chain,' International Conference of Korea Intelligent Information Systems Society, (2003), pp.141-148
  24. Lawrence, R.D., G.S. Almasi, V. Kotlyar, M.S. Viveros, and S.S. Duri, 'Personalization of Supermarket Product Recommendation,' Data Mining and Knowledge Discovery, Vol. 5, No.1-2(2001), pp.11-32 https://doi.org/10.1023/A:1009835726774
  25. Ng, K and H. Liu, 'Customer retention via data mining,' Artificial Intelligence Review, Vol.14, No.6(2000), pp.569-590 https://doi.org/10.1023/A:1006676015154
  26. Raghavan, N., R.M. Bell, and M. Schonlau, 'Defection Detection: Using Online Activity Profiles to Predict ISP Customer Vulnerability,' In Proceedings of the sixth International Conference of Knowledge Discovery and Data Mining, (2000), pp.506-515
  27. Sarwar, B., G. Karypis, J.A. Konstan, and J. Riedl, 'Item_based collaborative filtering recommendation algorithm,' In Proceedings of The Tenth International World Wide Web Conference, (2001), pp.285-295
  28. Sarwar, B., G. Karypis, J.A. Konstan, and J. Riedl, 'Analysis of recommendation algorithms for e-commerce,' In Proceedings of ACM E-commerce 2000 conference, (2000), pp.158-167
  29. Sarwar, B., Karypis, G., Konstan, J.A. and Riedl, J, 'Application of Dimensionality Reduction in Recommender System A case study,' In Proceedings of the ACM web KDD-2000 Workshop, 2000
  30. Song, H., J. Kim, Y. Cho, and S. Kim, 'A personalized defection detection and prevention procedure based on the self-organizing map and association rule mining: Applied to online game sit,' Artificial Intelligence Review, Vol.21 , No.2(2004), pp.161-184 https://doi.org/10.1023/B:AIRE.0000021067.66616.b0
  31. Trubik, E., and M Smith, 'Developing a model of customer defection in the Australian banking industry,' Managerial Auditing Journal, Vol.15, No.5(2000), pp.199-208 https://doi.org/10.1108/02686900010339300
  32. Yeo, A, K. Smith, R. Willis, and M. Brooks, 'Modeling the effect of premium changes on motor insurance customer retention rates using neural networks,' Lecture notes in computer science, (2001), pp.390-399