A Decision Support Framework for Customer Loyalty Program Managers: Reward Mix Optimization
A new framework helps customer loyalty program managers design effective programs by balancing rewards' attractiveness and cost.
Researchers developed a framework to aid customer loyalty program managers in designing effective programs by optimizing the mix of rewards. The framework considers both the attractiveness of rewards to customers and the unit cost to the organization. This approach can help managers make informed decisions and create programs that are both appealing to customers and cost-effective for the business.
Abstract
Customer Loyalty Programs are a proven methodology for establishing and maintaining customer relationships. With the development of mobile technologies and the power of digitalization, what was once a simple punch card has now evolved into a full-fledged mobile application. The paradigm shift has opened up research areas on an individual customer level, especially in non-contractual traditional commerce, which was previously impossible due to a lack of loyalty data. The cost and budget of Customer Loyalty Programs increase with their strategic value. Balancing the attractiveness of a reward to the customer with the unit cost to the organization is essential for designing effective programs. In this study, we propose a framework that combines the attractiveness and unit cost of rewards to provide an optimized reward mix, thereby aiding Customer Loyalty Program managers in their decision-making processes.
References
- 1.Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of Interactive Marketing, 17(1), 9-24.
- 2.Open Loyalty. (2025). Loyalty Program Trends 2025. Open Loyalty. 11 7, 2025 tarihinde https://www.openloyalty.io/resources/loyalty-program-trendsLink
- 3.Mary, B. (2025, May 26). Customer loyalty programs and their long-term impact. doi:http://dx.doi.org/10.2139/ssrn.5269142DOI
- 4.Zaware, N., & Deokate, R. (2025). Customer engagement impact on brand loyalty: a comprehensive analysis. Asian Journal of Management and Commerce, 6(1), 590-591.
- 5.Bariha, P. P. (2020). Customer loyalty program and retention relationship. Psychology and Education, 57(9), 5069-5074.
- 6.Kim, B.-D., Shi, M., & Srinivasan, K. (2001). Reward programs and tacit collusion. Marketing Science, 20(2), 99-120.
- 7.Chen, Y., Mandler, T., & Meyer-Waarden, L. (2021). Three decades of research on loyalty programs: A literature review and future research agenda. Journal of Business Research, 179-197. doi:https://doi.org/10.1016/j.jbusres.2020.11.057DOI
- 8.Andia-Reyna, J., & Malasquez-Villanueva, Y. (2025). Impact of emerging technologies on customer loyalty: a systematic review. International Journal of Advanced Computer Science and Applications, 16(3), 204-212.
- 9.Danaher, P. J., Sajtos, L., & Danaher, T. S. (2020). Tactical use of rewards to enhance loyalty program effectiveness. International Journal of Research in Marketing, 37(3), 505-520. doi:https://doi.org/10.1016/j.ijresmar.2020.02.005DOI
- 10.Son, Y., Yim, D., & Oh, W. (2017). Loyalty analytics: predicting customer behavior using reward redemption patterns under mobile-app reward scheme. Thirty Eighth International Conference on Information Systems.
- 11.Kim, J. J., Steinhoff, L., & Palmatier, R. W. (2021). An emerging theory of loyalty program dynamics. Journal of the Academy of Marketing Science, 49, 71-95. doi:https://doi.org/10.1007/s11747-020-00719-1DOI
- 12.Zunic, E., Korjenic, K., Hodzic, K., & Donko, D. (2020). Application of Facebook's prophet algorithm for successful sales forecasting based on real-world data. International Journal of Computer Science & Information Technology, 12(2), 23-36.
- 13.Nadar, A. T., Chandane, S., Raj, G. N., Pasi, N. M., & Patil, Y. A. (2023). Automated energy billing with blockchain and the prophet forecasting model: a holistic approach. IEEE International Conference on Multidisciplinary Research in Technology and Management MRTM 2023.
- 14.Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. https://arxiv.org/abs/1703.04247Link
- 15.Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., . . . Liu, X. (2016). Wide & Deep Learning for Recommender Systems. CoRR. https://arxiv.org/abs/1606.07792Link
- 16.PyTorch Foundation. (2025). PyTorch documentation. PyTorch: https://docs.pytorch.org/docs/stable/index.htmlLink
- 17.Ammar, H. A., Adve, R., Shahbazpanahi, S., Boudreau, G., & Srinivas, K. V. (2022). Downlink Resource Allocation in Multiuser Cell-Free MIMO Networks With User-Centric Clustering. IEEE Transactions on Wireless Communications, 21(3), 1482-1497.
- 18.Bertsekas, D. (2020). Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm. https://arxiv.org/abs/2002.07407 adresinden alındıLink
- 19.CVXPY. (2025). Open source Python-embedded modelling language for convex optimization. https://www.cvxpy.org/Link
- 20.Khan, A. A., Adve, R. S., & Yu, W. (2020). Optimizing Downlink Resource Allocation in Multiuser MIMO Networks via Fractional Programming and the Hungarian Algorithm. IEEE Transactions on Wireless Communications, 19(8), 5162-5175.
- 21.Gutin, G., & Karapetyan, D. (2009). A Memetic Algorithm for the Multidimensional Assignment Problem. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics, (s. 125-129).
Salı, A., Yilmaz, S. K., Aslan, M. E., Ergenecoşar, G. T. (2025). A Decision Support Framework for Customer Loyalty Program Managers: Reward Mix Optimization. *The European Journal of Research and Development*, 5(1), 228-245. https://doi.org/10.56038/ejrnd.v5i1.679
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