محمد رضائی - PhD Candidate of Business Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran
علی صنایعی - Professor, Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran
سیدفتح الله امیری عقدایی - Associate Professor, Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran
آذرنوش انصاری - Assistant Professor, Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran

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 ۱۳۴% more.

Omnichannel Marketing, Omnichannel Banking, CLV, Association Rule Data Mining, Big data