Document Type : Original Research


1 Assistant Professor, Department of Industrial Engineering, Alzahra University, Tehran, Iran

2 Assistant Professor, Department of Industrial Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

3 Department of Industrial Engineering, Alzahra University, Tehran, Iran


In this paper, we proposed a new model to evaluate a customer's lifetime value, considering non-financial elements such as the customer’s churn probability, cooperation capability, willingness to refer, willingness to recommend, and innovation. We tested our proposed model on customer data from a mobile phone operator to evaluate the effect of each element on the customer's lifetime value. Four hundred and twenty questionnaires were distributed and 400 questionnaires were determined to be suitable for our study. We employed structural equation modeling using Smart-PLS software and we have found that the innovation, customer’s churn, willingness to refer, and cooperation elements have the strongest effect on the customer's lifetime value.


Blattberg, R. C., Malthouse, E. C., & Neslin, S. A. (2009). Customer lifetime value: Empirical generalizations and some conceptual questions. Journal of Interactive Marketing, 23(2), 157-168.‏
Blattberg, R. C., & Deighton, J. (1996). Manage marketing by the customer equity test. Harvard business review, 74(4), 136.‏
Blattberg, R. C., Getz, G., & Thomas, J. S. (2001). Customer equity: Building and managing relationships as valuable assets. Harvard Business Press.‏
Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: marketing models and applications. Journal of interactive marketing, 12(1), 17-30.‏
Boles, J. S., Barksdale, H. C., & Johnson, J. T. (1997). Business relationships: an examination of the effects of buyer-salesperson relationships on customer retention and willingness to refer and recommend. Journal of Business & Industrial Marketing, 12(3/4), 253-264.‏
Chan, S. L., Ip, W. H., & Cho, V. (2010). A model for predicting customer value from perspectives of product attractiveness and marketing strategy. Expert Systems with Applications, 37(2), 1207-1215.‏
Cheng, C. H., & Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-4184.‏
Chen, Z. Y., & Fan, Z. P. (2013). Dynamic customer lifetime value prediction using longitudinal data: An improved multiple kernel SVR approach. Knowledge-Based Systems, 43, 123-134.‏
Cheng, C. J., Chiu, S. W., Cheng, C. B., & Wu, J. Y. (2012). Customer lifetime value prediction by a Markov chain based data mining model: Application to an auto repair and maintenance company in Taiwan. Scientia Iranica, 19(3), 849-855.‏
Donkers, B., Verhoef, P. C., & de Jong, M. G. (2007). Modeling CLV: A test of competing models in the insurance industry. Quantitative Marketing and Economics, 5(2), 163-190
Glady, N., Baesens, B., & Croux, C. (2009). A modified Pareto/NBD approach for predicting customer lifetime value. Expert Systems with Applications, 36(2), 2062-2071.‏
Glifford E., customer relationship management, website/article/customer relationship management, 2005.
Han, S. H., Lu, S. X., & Leung, S. C. (2012). Segmentation of telecom customers based on customer value by decision tree model. Expert Systems with Applications, 39(4), 3964-3973.‏
Haenlein, M., Kaplan, A. M., & Beeser, A. J. (2007). A model to determine customer lifetime value in a retail banking context. European Management Journal, 25(3), 221-234.‏
Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26(2), 181-188.‏
Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.‏
Liu, C. T., Guo, Y. M., & Lee, C. H. (2011). The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management, 31(1), 71-79.‏
Liang, Y. H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications, 37(12), 7489-7496.‏
Rudolf-Sipötz, E. (2001). Kundenwert: Konzeption, Determinanten, Management. Thexis.
Shin, D. H., & Kim, W. Y. (2008). Forecasting customer switching intention in mobile service: An exploratory study of predictive factors in mobile number portability. Technological Forecasting and Social Change, 75(6), 854-874.‏
Tang, C., Seal, C. R., & Naumann, S. E. (2013). Emotional labor strategies, customer cooperation and buying decisions. Journal of Management and Marketing Research, 13, 1-15.‏