The Value of Personalized Pricing
The Value of Personalized Pricing
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DOI：
10.2139/ssrn.3127719
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发表时间：
2018-10
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影响因子：
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通讯作者：
Adam N. Elmachtoub;Vishal Gupta;Michael L. Hamilton
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文献类型：
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作者：
Adam N. Elmachtoub;Vishal Gupta;Michael L. Hamilton
高质量客户信息的可用性的增加激发了人们对个性化定价策略的兴趣，即预测单个客户对产品的估价，然后提供适合该客户的价格的策略。尽管个性化定价的吸引力显而易见，但它也可能会产生大量成本，包括市场研究、信息技术和分析专业知识投资以及品牌风险。鉴于这些权衡，我们的工作研究了个性化定价策略相对于简单的单一价格策略的价值。我们首先提供理想化个性化定价策略（一级价格歧视）和单一价格策略的利润之间比率的封闭形式下限和上限。我们的界限取决于估值分布的简单统计数据，并揭示了个性化定价几乎没有或具有重大潜在价值的市场类型。其次，我们考虑基于特征的定价模型，可以根据观察到的特征来估计客户估值。我们展示了如何将上述界限转换为基于特征的定价相对于单一定价的价值的下限和上限，具体取决于特征对评估的信息程度。最后，我们演示了如何通过解决易于处理的线性优化问题，结合有关估值分布（矩或形状约束）的附加信息来获得更清晰的边界。这篇论文被收入管理和市场分析部门的 David Simchi-Levi 接受。
Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, that is, strategies that predict an individual customer’s valuation for a product and then offer a price tailored to that customer. Although the appeal of personalized pricing is clear, it may also incur large costs in the forms of market research, investment in information technology and analytics expertise, and branding risks. In light of these trade-offs, our work studies the value of personalized pricing strategies over a simple single-price strategy. We first provide closed-form lower and upper bounds on the ratio between the profits of an idealized personalized pricing strategy (first-degree price discrimination) and a single-price strategy. Our bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has little or significant potential value. Second, we consider a feature-based pricing model where customer valuations can be estimated from observed features. We show how to transform our aforementioned bounds into lower and upper bounds on the value of feature-based pricing over single pricing depending on the degree to which the features are informative for the valuation. Finally, we demonstrate how to obtain sharper bounds by incorporating additional information about the valuation distribution (moments or shape constraints) by solving tractable linear optimization problems. This paper was accepted by David Simchi-Levi, revenue management and market analytics.