A Decision Support Framework for Customer Loyalty Program Managers: Reward Mix Optimization
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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.
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References
Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of Interactive Marketing, 17(1), 9-24. DOI: https://doi.org/10.1002/dir.10045
Open Loyalty. (2025). Loyalty Program Trends 2025. Open Loyalty. 11 7, 2025 tarihinde https://www.openloyalty.io/resources/loyalty-program-trends
Mary, B. (2025, May 26). Customer loyalty programs and their long-term impact. doi:http://dx.doi.org/10.2139/ssrn.5269142 DOI: https://doi.org/10.2139/ssrn.5269142
Zaware, N., & Deokate, R. (2025). Customer engagement impact on brand loyalty: a comprehensive analysis. Asian Journal of Management and Commerce, 6(1), 590-591. DOI: https://doi.org/10.22271/27084515.2025.v6.i1g.500
Bariha, P. P. (2020). Customer loyalty program and retention relationship. Psychology and Education, 57(9), 5069-5074. DOI: https://doi.org/10.17762/pae.v58i1.2012
Kim, B.-D., Shi, M., & Srinivasan, K. (2001). Reward programs and tacit collusion. Marketing Science, 20(2), 99-120. DOI: https://doi.org/10.1287/mksc.20.2.99.10191
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.2020.11.057 DOI: https://doi.org/10.1016/j.jbusres.2020.11.057
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. DOI: https://doi.org/10.14569/IJACSA.2025.0160320
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.2020.02.005 DOI: https://doi.org/10.1016/j.ijresmar.2020.02.005
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.
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/s11747-020-00719-1 DOI: https://doi.org/10.1007/s11747-020-00719-1
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. DOI: https://doi.org/10.5121/ijcsit.2020.12203
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.
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/1703.04247 DOI: https://doi.org/10.24963/ijcai.2017/239
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/1606.07792 DOI: https://doi.org/10.1145/2988450.2988454
PyTorch Foundation. (2025). PyTorch documentation. PyTorch: https://docs.pytorch.org/docs/stable/index.html
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. DOI: https://doi.org/10.1109/TWC.2021.3104456
Bertsekas, D. (2020). Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm. https://arxiv.org/abs/2002.07407 adresinden alındı
CVXPY. (2025). Open source Python-embedded modelling language for convex optimization. https://www.cvxpy.org/
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. DOI: https://doi.org/10.1109/TWC.2020.2990176
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). DOI: https://doi.org/10.1007/978-3-642-03751-1_12