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

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.