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

本研究项目旨在对忠诚度奖励计划（lrp）所面临的计划和操作问题进行建模。如今，忠诚计划行业是一个重要的经济部门，涉及数百万加拿大人，他们接受了许多存在于各行各业（旅游、酒店、零售、电信、银行等）的lrp。世界各地正在建立新的lrp，现有的lrp已进行改组，以促进价值增长和提高其竞争优势。近年来，许多lrp的增长相当可观，例如在会员登记和活动方面；与客户合作伙伴的合资企业；收集点的来源多样化；奖励方式多样化；以及所发生的总奖励成本。这种增长大大增加了企业管理其忠诚度计划所面临的复杂性。然而，尽管复杂性有所增加，但目前很少有学术模型能够支持忠诚计划行业的规划和运营决策。这项工作旨在通过开发和实施基于分析的（运筹学/管理科学）模型来解决一些对lrp组织的效率和有效性至关重要的问题，从而有助于缩小这一差距。本研究工作的目标包括：(a)发展聚合和分解模型，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)探讨如何将收入管理方法应用于最低限额方案；(c)提出设计和管理伙伴关系的模式；(d)开发和比较不同的方法来处理破碎系数的确定，这是忠诚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.