순차적 자료융합방법을 이용한 은행고객의 가치예측 = Sequential Data Fusion Approaches for Predicting the Value of Bank Customers

다국어 초록 (Multilingual Abstract)

This study applies data fusion approach to predict the value of bank customers. The three approaches applied include the Euclidean distance-based fusion, the Mahalanobis distance-based fusion, and the regression/logit-based fusion. The data consist of 1,000 customers with their activities associated with various accounts, demographic variables, and class segmentation for the fiscal year of 2006 and 2007. For the comparison of model performance, MAD and the paired samples t-test are used for the 10 numeric variables, while correct classification ratio and the McNemar test are used for the three categorical variables. The experiment results show that with respect to MAD, model performance is superior in the order of the Euclidean model, the Mahalanobis model, and the regression model. Correct classification ratio is best for the logit model, and the Euclidean model and the Mahalanobis model follow the next. The contribution of the current study is that we attempt to extend the scope of data fusion into sequential data fusion, which is necessary for the systematic data accumulation and analysis under CRM strategy.

참고문헌 (Reference)

1 김성호, "자료융합방법의 성과에 대체수준이 미치는 영향에 관한 연구：몬테카를로 시뮬레이션 접근방법" 한국경영과학회 19 (19): 129-141, 2002

2 김성호, "마할라노비스 거리를 이용한 자료융합전략의 성과측정" 한국경영학회 34 (34): 1853-1867, 2005

3 Gronroos, C., "Value-driven Relational Marketing: from Products to Resources and Competencies" 13 (13): 407-419, 1997

4 Gummesson, E., "Relationship Marketing and Imaginary Organizations: a Synthesis" 30 (30): 31-44, 1996

5 Kromrey, J.D., "Nonrandomly Missing Data in Multiple Regression: An Empirical Comparison of Common Missing-Data Treatments" 54 (54): 573-593, 1994

6 Kalawani, M.U., "Long-term Manufacturer-Supplier Relationships: Do They Pay Off for Supplier Firms" 59 : 1-16, 1995

7 Gebert, H., "Knowledge-enabled Customer Relationship Management: Integrating Customer Relationship Management and Knowledge Management Concepts" 7 (7): 107-123, 2003

8 Xu, M., "Gaining Customer Knowledge through Analytical CRM" 105 (105): 955-971, 2005

9 Kamakura, W.A., "Factor Analysis and Missing Data" 37 : 490-498, 2000

10 Cho, S., "Exploring Artificial Intelligence-based Data Fusion for Conjoint Analysis" 24 (24): 287-294, 2003

11 Berry, M.A., "Data Mining Techniques" Wiley 2003

12 Baker, K., "Data Fusion: An Appraisal and Experimental Evaluation" 39 (39): 227-271, 1997

13 Baker, K., "Data Fusion: An Appraisal and Experimental Evaluation" 31 (31): 153-212, 1989

14 Gurau, C., "Customer-Centric Strategic Planning: Integrating CRM" 4 : 199-214, 2003

15 Bose, R., "Customer Relationship Management: Key Components for IT Success" 102 (102): 89-97, 2002

16 Zineldin, M., "Beyond Relationship Marketing: Technologicalship Marketing" 18 (18): 9-23, 2000

17 Landerman, L.R., "An Empirical Evaluation of the Predictive Mean matching Method for Imputing Missing Values" 26 (26): 3-33, 1997

18 Baxter, N., "Agent-based Modeling - Intelligent Customer Relationship Management" 21 (21): 126-136, 2003

19 Kim, J.S., "A Preliminary Study on Common Variable Selection Strategy in Data Fusion" 31 : 716-720, 2004