Revenue Management and Policy Design in the Presence of Customer Multi-Item Shopping Behavior

收入管理(RM)在许多数十亿美元的行业取得了重大成功，如航空公司、酒店和零售业。成功的需求管理的关键要素是需求建模。将客户选择整合到需求建模中的重要性已经得到了很好的证明。然而，现有的大多数客户选择模型都假设每个客户至多购买一种产品。这一假设在很大程度上偏离了现实。例如，顾客经常在同一产品类别中购买多项商品，如软饮料和谷类食品。网上购物者经常购买不同产品类别的多件商品，事实上，他们经常受到激励这样做是为了有资格参加促销活动或免费送货。忽视这些行为可能会导致预测错误，使运营问题的解决方案和促销政策的有效性降低。到目前为止，尽管多商品购买行为很重要，但在RM中对其建模的工作很少。拟议的研究计划试图弥合这一差距，并在RM文献中推进客户多项目购买行为的建模。我们将“多项目”定义为多个项目单位，其中一些项目可以是相同的，在同一产品类别内或跨不同类别。这些项目可以是互补的、可替代的或相互独立的。也就是说，就像在现实世界中一样，客户选择他们自己的产品捆绑包或篮子。因此，我们的研究与捆绑文献(客户选择预先确定的补充产品包装)和购物篮文献(客户从多个类别中挑选不同的产品)有很大不同，也更具一般性。该研究计划将积极利用选择建模、非线性优化、博弈论方法和新兴大数据工具等方法，以实现以下目标：(1)先进的分析模型，以捕获客户的多商品购买行为，并使用分析和数据驱动的方法，寻求解决高影响的RM问题的解决方案，例如分类和价格优化；以及(2)设计纳入此类客户选择行为的最优政策，以帮助公司做出更高效和更有利可图的运营决策。RM分析的进步有可能对经济产生重大影响，因为即使先进的RM分析带来1%的收入小幅改善，也可能转化为加拿大零售业超过50亿加元的额外销售额。因此，这项研究的结果对RM研究人员和行业从业者都很重要。在这个项目中，HQP将获得管理科学和高级数据分析的知识，以及将这些知识应用于现实世界问题的技能，并为在学术或行业职业生涯中取得成功做好准备，因为这些技能的需求很高。

Revenue management (RM) has achieved major success in many multi-billion sectors, such as airline, hotel, and retail industries. The crucial element in successful RM is demand modeling. The importance of integrating customer choice into demand modeling has been well documented. However, most of the extant customer choice models assume that each customer purchases at most one product. This assumption largely deviates from reality. For example, customers often buy multiple items in the same product category, such as soft drinks and cereals. Online shoppers often purchase multiple items across different product categories, and in fact are often incentivized to do so to qualify for a promotional campaign or free shipping. Ignoring such behavior may result in prediction errors, rendering solutions to the operational problems and promotional policies less effective. To date, little work has been done in RM to model the multi-item purchase behavior despite its importance. The proposed research program seeks to bridge this gap and advance the modeling of the customer multi-item purchase behavior in the RM literature. We define "multi-item" as multiple units of items, some of which can be the same, within the same product category or across different categories. The items can be complementary, substitutable or independent of each other. That is, just as in real-world circumstances, customer choose their own bundle or basket of products. Our research is thus vastly different from and much more general than the bundling literature (where a customer chooses a pre-determined package of complementary products) and the basket shopping literature (where a customer picks a different product from multiple categories). This research program will actively leverage methodologies such as choice modeling, non-linear optimizations, game theoretic approaches, and emerging Big Data tools to address the following objectives: (1)Advance analytical models to capture customer multi-item purchase behaviors, as well as seek solutions to high-impact RM problems, such as assortment and price optimizations, using both analytical and data-driven approaches; and (2)Design optimal policies that incorporate such customer choice behaviors to help companies make more efficient and profitable operational decisions. Advances in RM analytics have the potential for significant economic impact, as even a moderate improvement of 1% in revenue as a result of advanced RM analytics could translate to additional sales of over CAD $5B in the Canadian retail industry. Therefore, the outcomes of this research are important for both RM researchers and industry practitioners. HQP in this project will gain knowledge of management science and advanced data analytics and skills in applying the knowledge to real-world problems, and get prepared for success in an academic or industry career where such skills are in high demand.