Improving the Omnichannel Customers’ Lifetime Value Using Association Rules Data Mining: A Case Study of Agriculture Bank of Iran

Document Type : Research Paper

Authors

1 PhD Candidate of Business Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

2 Professor, Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

3 Associate Professor, Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

4 Assistant Professor, Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

Abstract

Multi-channel marketing causes the customer to lack a unique identity in different channels. This issue overshadows the synergy of the channels in strengthening the positive attitude of the customers. However, an omnichannel marketing strategy can work properly. The main purpose of this study, which was conducted in Agriculture Bank of Iran, was to develop a comprehensive model for calculating customers’ lifetime values, analyzing customers’ behaviors in different channels by association rules data mining, and analyzing the relationship between omnichannel strategy and CLV. First, the association rules in the big data of customers’ banking transactions in different channels were identified using association rules data mining. Then, the CLV indicators were identified and prioritized using interviews, questionnaires, and AHP methods, and the lifetime values of omnichannel and other customers were calculated and compared using t-test. Then, omnichannel customers were categorized based on the association rules and the lifetime values of omnichannel customers of different categories was compared using ANOVA method. Eleven association rules regarding the use of banking channels by omnichannel customers were identified. The results show that there is a significant difference between the lifetime values of omnichannel customers and other customers and the lifetime values of omnichannel customers is 134% more.

Keywords

Main Subjects

References

Bhatnagar, A., & Syam, S. (2014). Allocating a hybrid retailer’s assortment across retail stores: Bricks-and-mortar vs online. Journal of Business Research, 67(June), 1293-1302.
Cao, L., & Li, L. (2015). The impact of cross-channel integration on retailers’ sales growth. Journal of Retailing, 91(2), 198–216.
Chen, Y., Cheung, C. M. K., & Tan, C. W. (2018). Omnichannel business research: Opportunities and challenges. Decision Support Systems, 109(May), 1-4.
Chopra, S. (2016). How omnichannel can be the future of retailing. Decision, 43(2), 135–144.
Cummins, S., Peltier, J. W., & Dixon, A. (2016). Omnichannel research framework in the context of personal selling and sales management. Journal of Research in Interactive Marketing, 10(1), 2–16.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley
Gallino, S., & Moreno, A. (2014). Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Science, 60(6), 1434–1451.
Gao, F., & Su, X. (2016). Online and offline information for omnichannel retailing. Manufacturing Service & Operations Management, 19(1), 84–98.
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139–155
Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: Retailer level and channel level outcomes of online-offline channel integration. Journal of Retailing, 91(2), 309–325.
Hsia, T. L., Wu, J. H., Xu, X., Li, Q., Peng, L., & Robinson, S. (2020). Omnichannel retailing: The role of situational involvement in facilitating consumer experiences. Information & Management, 57(8), 103390.
Hure, H., Picot-Coupey, K., & Ackermann. C.L. (2017) Understanding omnichannel shopping value: A mixed-method study. Journal of Retailing and Consumer Services, 39 (November), 314-330.
Kim, J. C., & Chun, S. H. (2018). Cannibalization and competition effects on a manufacturer’s retail channel strategies: Implications on an omni-channel business model. Decision Support System, 109(January), 5–14.
Lee, Z., Chan, T. K. H., Yee-Loong C.A., & Thadani, D. R. (2019). Customer engagement through omnichannel retailing: The effects of channel integration quality. Industrial Marketing Management, 77(February), 90-101.
Moliner, J. R. S., & Tena, M. A. M. (2016). Customer equity and clv in Spanish telecommunication services. Journal of Business Research, 69(10), 4694-4705.
Nikkhahan, B., Badrabadi, A. H., & Tarokh, M. J. (2011). Customer lifetime value model in an online toy store. Journal of Industrial Engineering International, 7(12), 19–31.
Saghiri, S., Wilding, R., Mena, C., & Bourlakis, M. (2017). Toward a three-dimensional framework for omni-channel. Journal of Business Research, 77(August) 53–67.
Shao, X. (2021). Omnichannel retail move in a dual-channel supply chain. European Journal of Operational Research, 294 (3), 936-950.
Shen, X.L., Li, Y.J., Sun, Y., & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109(May), 61-73.
Shi, Y., Wu, B., Chen, N., Chen, A., Li, J., & Li, H. (2019). Determination of effective management strategies for scenic area emergencies using association rule mining. International Journal Disaster Risk Reduction. 39, 101208.
Suárez, M. M., & Tejero, N. C. (2021). Why do banks retain unprofitable customers? A customer lifetime value real options approach. Journal of Business Research, 122, 624-626
Tarokh, M. J., & Esmaeili Gookeh, M. (2019). Modeling patient’s value using a stochastic approach: An empirical study in the medical industry. Computer Methods and Programs in Biomedicine, 176(July), 51-59.
Valenzuela, L., Torres, E., Hidalgo, P., & Farias, P. (2014). Salesperson CLV orientation’s effect on performance. Journal of Business Research, 67(December), 550-557
Van Baal, S. (2014). Should retailers harmonize marketing variables across their distribution channels? An investigation of cross-channel effects in multi-channel retailing. Journal of Retailing and Consumer Services, 21(6), 1038–1046.
Verhoef, P. C., Kannan, P. K., & Jeffrey Inman, J. (2015). From multi-channel retailing to omni-channel retailing introduction to the special issue on multi-channel retailing, Journal of Retailing, 91(2), 174–181.
Wagner, G., Schramm-Klein, H., & Steinmann, S. (2018). Online retailing across e-channels and e-channel touchpoints: Empirical studies of consumer behavior in the multichannel e-commerce environment. Journal of Business Research, 107 (November), 256-270.
Wang, F., Liming, L., Kangping, L., Neven, D., Miadreza, S. K., & Joao P. S. C. (2019). Impact factors analysis on the probability characterized effects of time of use demand response tariffs using association rule mining method. Energy Conversion and Management, 197(October), 111891.
Aggelis, V., (2004). Data mining V, UK, Wessex Institute of Technology
Kargari, M., & Eshghi, A. (2018). A Model Based on Clustering and Association Rules for Detection of Fraud in Banking Transactions. Proceedings of the 4th World Congress on Electrical Engineering and Computer Systems and Sciences, Madrid, Spain, August 21–23
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 15, Issue 1
January 2022
Pages 49-68

Files

Share

How to cite

Statistics