Estimating unconstrained demand rate functions using customer choice sets
Estimating unconstrained demand rate functions using customer choice sets
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DOI：
10.1057/rpm.2010.1
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发表时间：
2011-09
影响因子：
1.6
通讯作者：
A. Haensel;G. Koole
中科院分区：
文献类型：
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作者：
A. Haensel;G. Koole
良好的需求预测应该是每个收入管理模型的核心。然而，大多数需求模型关注的是产品需求，而没有考虑到客户在提供的替代品下的选择行为。我们使用客户选择集的思想来模拟客户的购买行为。客户选择集是一组产品类别，代表了特定客户群的购买偏好和选择决策。在这篇文章中，我们提出了这些选择集的需求估计方法。该过程是基于最大似然法，并克服不完整的数据或信息的问题，我们还应用期望最大化方法。通过使用每个选择集的需求信息，收入经理可以清楚地了解潜在需求。这样，可以比较不同预订控制操作的销售结果，并最大化整体收入。
A good demand forecast should be at the heart of every revenue management model. Yet most demand models focus on product demand and do not incorporate customer choice behavior under offered alternatives. We use the ideas of customer choice sets to model the customer's buying behavior. A customer choice set is a set of product classes representing the buying preferences and choice decisions of a certain customer group. In this article we present a demand estimation method for these choice sets. The procedure is based on the maximum likelihood method, and to overcome the problem of incomplete data or information we additionally apply the expectation maximization method. Using this demand information per choice sets, the revenue manager obtains a clear view of the underlying demand. In doing so, the sales consequences from different booking control actions can be compared and the overall revenue maximized.