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Bank Marketing Strategy Based on Consumer Loan Behavior Prediction
- 1 Department of economic, university of California, Santa Barbara, 93117, the United States
- 2 Business administration, HuaQiao University, Quanzhou, 362021, China
- 3 Beijing No.35 High school, Beijing, 102600, China
- 4 Art and Science, University of Colorado, Boulder ,CO80309, the United States
* Author to whom correspondence should be addressed.
Abstract
In recent years, with the continuous improvement of living standards, more people and small enterprises have tended to loan from banks. By analyzing the loan behavior of bank customers and the decision-making process of loan behavior, this paper proposes and optimizes the strategy and marketing model. The customer's marital status, real estate status, and the type of contact information left will affect the customer's loan behavior. And the influence of the three factors is ranked as follows: marriage is more significant than contact information, and contact information is more excellent than real estate.
Keywords
bank marketing, consumer behavior, marketing strategy
[1]. Jain, A. K., Pinson, C., & Malhotra, N. K. (1987). Customer loyalty as a construct in the marketing of banking services. International Journal of Bank Marketing, 5(3), 49-72.
[2]. Holmlund, M., & Kock, S. (1996). Relationship marketing: the importance of customer-perceived service quality in retail banking. Service Industries Journal, 16(3), 287-304.
[3]. Perrien, J., Filiatrault, P., & Ricard, L. (1992). Relationship marketing and commercial banking: a critical analysis. International Journal of Bank Marketing.
[4]. Perrien, J., Filiatrault, P., & Ricard, L. (1993). The implementation of relationship marketing in commercial banking. Industrial Marketing Management, 22(2), 141-148.
[5]. Chye, K. H., & Gerry, C. K. L. (2002). Data mining and customer relationship marketing in the banking industry. Singapore Management Review, 24(2), 1-28.
[6]. Ivanchenko, O. V., Mirgorodskaya, O. N., Baraulya, E. V., & Putilina, T. I. (2019). Marketing relations and communication infrastructure development in the banking sector based on big data mining.
[7]. Durkin, M. G., & Howcroft, B. (2003). Relationship marketing in the banking sector: the impact of new technologies. Marketing Intelligence & Planning.
[8]. Sarel, D., & Marmorstein, H. (2003). Marketing online banking services: the voice of the customer. Journal of Financial Services Marketing, 8(2), 106-118.
[9]. Girchenko, T., & Kossmann, R. (2017). Implementation and development of digital marketing in modern banking business. European Cooperation, 12(19), 68-85.
[10]. Nso, M. A. (2018). The role of e-banking as a marketing tool. Innovative Marketing, 14(4), 56.
[11]. Shavitt, S. (1989). Products, personalities and situations in attitude functions: implications for consumer behavior. ACR North American Advances.
[12]. Thomas, L. C. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International journal of forecasting, 16(2), 149-172.
[13]. Thomas, L. C., Ho, J., & Scherer, W. T. (2001). Time will tell: behavioural scoring and the dynamics of consumer credit assessment. IMA Journal of Management Mathematics, 12(1), 89-103.
[14]. Liberman, N., Trope, Y., & Wakslak, C. (2007). Construal level theory and consumer behavior. Journal of consumer psychology, 17(2), 113-117..
[15]. Trope, Y., Liberman, N., & Wakslak, C. (2007). Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. Journal of consumer psychology, 17(2), 83-95.
Cite this article
Peng,Y.;Liang,J.;Zhang,W.;Liu,M. (2023). Bank Marketing Strategy Based on Consumer Loan Behavior Prediction. Advances in Economics, Management and Political Sciences,6,508-514.
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Volume title: Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2
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