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An aggregate inventory-based model for predicting redemption and liability in loyalty reward programs industry

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  • Aaron Luntala Nsakanda

    (Carleton University)

  • Moustapha Diaby

    (University of Connecticut)

  • Yuheng Cao

    (Carleton University)

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.

Suggested Citation

  • Aaron Luntala Nsakanda & Moustapha Diaby & Yuheng Cao, 2011. "An aggregate inventory-based model for predicting redemption and liability in loyalty reward programs industry," Information Systems Frontiers, Springer, vol. 13(5), pages 707-719, November.
    • Handle: RePEc:spr:infosf:v:13:y:2011:i:5:d:10.1007_s10796-010-9247-z
    DOI: 10.1007/s10796-010-9247-z
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    References listed on IDEAS

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    Cited by:

    1. Guo, Xingchen & Jiang, Changmin & Jiang, Siming & Guo, Huanxiu, 2023. "Making airline coalition frequent-flyer programs profitable: An analytical investigation," Transport Policy, Elsevier, vol. 141(C), pages 245-262.
    2. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.

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