Bayesian strategies for dynamic pricing in e‐commerce
Bayesian strategies for dynamic pricing in e‐commerce
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DOI：
10.1002/nav.20204
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发表时间：
2007-04
期刊：
影响因子：
--
通讯作者：
Eric Cope
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文献类型：
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作者：
Eric Cope
电子商务平台为零售商提供了前所未有的客户购买行为可见性，并提供了一个环境，在这种环境中，价格可以快速且廉价地更新，以应对不断变化的市场状况。本研究通过主动学习消费者需求对价格的反应，研究了互联网零售渠道收益最大化的动态定价策略。提出了一种动态定价信息产品和其他非易腐产品的一般方法，这些产品的库存水平不是定价中的基本考虑因素。需求不确定性的贝叶斯模型包括狄利克雷分布或作为先验分布的这种分布的混合分布，它捕获了关于客户需求的广泛信念。我们提供了分析公式和有效的近似方法，以便在观察到销售数据后更新这些先验分布。然后，我们研究了几种基于指数函数的序贯定价策略，这些策略同时考虑了选择价格的潜在收入和信息价值。与静态定价和被动学习方法相比，这些策略需要可管理的计算量，对许多类型的先前错误指定具有健壮性，并产生高收入。©2006威利期刊公司，海军研究物流，2007
E‐commerce platforms afford retailers unprecedented visibility into customer purchase behavior and provide an environment in which prices can be updated quickly and cheaply in response to changing market conditions. This study investigates dynamic pricing strategies for maximizing revenue in an Internet retail channel by actively learning customers' demand response to price. A general methodology is proposed for dynamically pricing information goods, as well as other nonperishable products for which inventory levels are not an essential consideration in pricing. A Bayesian model of demand uncertainty involving the Dirichlet distribution or a mixture of such distributions as a prior captures a wide range of beliefs about customer demand. We provide both analytic formulas and efficient approximation methods for updating these prior distributions after sales data have been observed. We then investigate several strategies for sequential pricing based on index functions that consider both the potential revenue and the information value of selecting prices. These strategies require a manageable amount of computation, are robust to many types of prior misspecification, and yield high revenues compared to static pricing and passive learning approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007