Abstract
We propose a predictive model of redemption and liability to support short, medium, and long term planning and operational decision-making in Loyalty Reward Programs (LRPs). The proposed approach is an aggregate inventory model in which the liability of points is modeled as a stochastic process. An illustrative example is discussed as well as a real-life implementation of the approach to facilitate use and deployment considerations in the context of a frequent flyer program, an airline industry based LRP.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
National post newspaper article on Loyalty programs published on October 7, 2005.
References
Bartholomew, D. J. (1977). The analysis of data arising from stochastic processes. In C. A. O’Muircheartaigh & C. Payne (Eds.), The analysis of survey data—model fitting (pp. 145–174). New York: Wiley. Chapter 5.
Berman, B. (2006). Developing an effective customer loyalty program. California Management Review, 49(1), 123–148.
Bobb, L. M., & Veral, E. (2008). Open issues and future directions in revenue management. Journal of Revenue & Pricing Management, 7(3), 291–301.
Cortinãs, M., Elorz, M., & Veral, E. (2008). The use of loyalty-cards databases: differences in regular price and discount sensitivity in the brand choice decision between card and non-card holders. Journal of Retailing and Consumer Services, 15, 52–62.
Diaby, M., Nsakanda, A. L. (2008) Coping with revenue recognition in the loyalty reward programs industry. Proceedings of the American Conference on Applied Mathematics, Cambridge (USA) March 24–26, 2008, pp 79–86.
Jain, D. C., & Singh, S. S. (2002). Customer lifetime value research in marketing: a review and future directions. Journal of Interactive Marketing, 16(2), 34–46.
Kadar, M., & Kotanko, B. (2001). Designing loyalty programs to enhance value growth. Mercer on Transport & Travel, 8(2), 28–33.
Kim, B. D., Shi, M., & Srinivasan, K. (2001). Reward programs and tacit collusion. Marketing Science, 20(4), 99–120.
Kim, B. D., Shi, M., & Srinivasan, K. (2004). Managing capacity through reward programs. Management Science, 50(4), 503–520.
Labbi, A., & Berrospi, C. (2007). Optimizing marketing planning and budgeting using Markov decision processes. IBM Journal of Research & Development, 51(3/4), 421–431.
Lacey, R., & Sneath, J. Z. (2006). Customer loyalty programs: are they fair to consumers? Journal of Consumer Marketing, 23(7), 458–464.
Liu, Y. (2007). The long-term impact of loyalty programs on consumer purchase behavior and loyalty. Journal of Marketing, 71(4), 19–35.
Meyer-Waarden, L., & Benavent, C. (2006). Impact of loyalty programmes on repeat purchase behaviour. Journal of Marketing Management, 22(1), 61–88.
Nicholls, M. G. (2009). The use of Markov models as an aid to the evaluation, planning and benchmarking of Doctoral Programs. Journal of Operational Research Society, 60, 1183–1190.
Reutterer, T., Mild, A., Natter, M., & Taudes, A. (2006). A dynamic segmentation approach for targeting and customizing direct marketing campaigns. Journal of Interactive Marketing, 20(3–4), 43–55.
Shen, Z. M., & Su, X. (2007). Customer behavior modeling in revenue management and auctions: a review and new research opportunities. Production and Operations Management, 16(6), 713–728.
Singh, S. S., Jain, D. C., & Krishnan, T. V. (2008). Customer loyalty programs: are they profitable? Management Science, 54(6), 1205–1211.
Suzuki, Y. (2003). Airline frequent flyer programs: equity and attractiveness. Transportation Research, Part E, 39, 289–304.
Zhang, Z., John, A. K., & Dhar, S. K. (2000). The optimal choice of promotional vehicles: front-loaded or rear-loaded incentives? Management Science, 46(3), 348–362.
Ziliani, C. (2006). PTarget promotions: how to measure and improve promotional effectiveness through individual customer information. Journal of Targeting, Measurement and Analysis for Marketing, 14(3), 249–259.
Acknowledgements
This research work is supported by the National Science and Engineering Research Council of Canada (NSERC). We thank the editors and the anonymous referees for their comments and suggestions that have proved very helpful in revising the previous version of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nsakanda, A.L., Diaby, M. & Cao, Y. An aggregate inventory-based model for predicting redemption and liability in loyalty reward programs industry. Inf Syst Front 13, 707–719 (2011). https://doi.org/10.1007/s10796-010-9247-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-010-9247-z