Models and methods for planning and operational decision-making in loyalty programs industry

这项研究计划是针对忠诚度奖励计划(LRP)所面临的计划和操作问题的建模。今天，忠诚度计划行业是一个重要的经济部门，涉及数百万加拿大人，他们接受了存在于不同行业(旅游、酒店、零售、电信、银行等)的许多LRP。世界各地都在建立新的有限责任公司，现有的有限责任公司已经进行了重组，以促进价值增长并增强其竞争优势。近年来，许多有限责任计划在会员登记和活动、与客户合作伙伴的合资企业、积分来源的多样化、奖励措施的多样化以及所产生的总奖励成本等方面都有相当大的增长。这种增长大大增加了公司在管理其忠诚度计划方面面临的复杂性。然而，尽管复杂性增加，但目前几乎没有学术模型来支持忠诚度计划行业的规划和运营决策。这项工作力求通过开发和实施以分析为基础的(业务研究/管理科学)模型来解决对有限责任方案组织的效率和效力至关重要的若干问题，从而有助于缩小这一差距。这项研究工作的目标包括：(A)开发聚合和非聚合模型，以解决预测建模问题，如负债预测、赎回预测和奖励供需预测；(B)探索如何将收入管理方法应用于有限责任计划；(C)提出设计和管理合作伙伴关系的模型；以及(D)开发和比较替代方法，以应对破坏系数的确定，这是忠诚度计划行业收入确认的关键组成部分。

This research program is directed towards the modeling of planning and operational issues faced by loyalty reward programs (LRPs). The loyalty programs industry is today an important economic sector that involves millions of Canadians who have embraced the many LRPs that exist across a spectrum of industries (travel, hotel, retail, telecommunication, banking, etc.). New LRPs are being established worldwide and existing ones have been restructured to contribute to value growth and enhance their competitive advantage. The growth of many LRPs has been considerable in recent years in terms of, for example, membership enrolment and activities; joint ventures with customer partners; diversification of sources for collection of points; diversification of reward offerings; and total rewards cost incurred. This growth has considerably increased the complexities faced by firms in managing their loyalty programs. However, despite increases in complexities, few academic models exist today to support planning and operational decision making in loyalty program industry. This work seeks to contribute to the reduction of this gap by developing and implementing analytical-based (operations research/management science) models to address a number of issues that are central for the efficiency and the effectiveness of LRPs organizations. The objectives on this research work include: (a) developing aggregate and disaggregate models to address predictive modeling issues such as liability forecasting, redemption forecasting, and prediction of award supply and demand; (b) exploring how revenue management methodologies can be applied to LRPs; (c) proposing models for designing and managing partnership relationships; and (d) developing and comparing alternate methods to cope with the determination of the breakage factor, a key component for revenue recognition in loyalty programs industry.