安排服务的选择建模与定价
Choice Modeling and Pricing for Scheduled Services
AI总结：
该研究提出了一种用于安排服务选择建模和定价的框架，通过参数化模型和决策树划分市场，提高了定价效率和性能。
摘要：
我们描述了一个新的框架，用于离散选择建模和价格优化，适用于提供给客户安排服务选项（通常为层次结构）的场景，这在许多企业中都适用，包括亚马逊的一些业务。在这种商业环境中，客户会看到多个选项，这些选项通常可以互相替代，具有各自的功能和价格。这些选项通常在服务请求的开始和/或结束时间上有所不同，例如服务日期或服务时间窗口。成本和需求在这些不同选项之间可以有很大差异，因此需要不同的价格。我们提出了一种系统，该系统可以使用决策树来划分市场（以特定业务定义的市场），并在每个市场细分中使用参数化的离散选择模型，以准确估计转化行为。使用参数化的离散选择模型使我们能够捕捉到重要的行为特征，如参考价格效应，这在安排服务应用中自然会发生。此外，我们还提供了自然且快速的启发式方法来进行价格优化。对于一个我们进行了实时A/B测试的亚马逊业务，这种新框架在所有关键指标上都优于现有的定价系统，将我们的目标性能指标提高了19%，同时提供了一个稳健的平台，以支持企业未来的新服务。该模型框架现在已在此业务中全面投入生产，自2023年第四季度以来。
英文摘要：
We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which is applicable across many businesses including some within Amazon. In such business settings, the customers would see multiple options, often substitutable, with their features and their prices. These options typically vary in the start and/or end time of the service requested, such as the date of service or a service time window. The costs and demand can vary widely across these different options, resulting in the need for different prices. We propose a system which allows for segmenting the marketplace (as defined by the particular business) using decision trees, while using parametric discrete choice models within each market segment to accurately estimate conversion behavior. Using parametric discrete choice models allows us to capture important behavioral aspects like reference price effects which naturally occur in scheduled service applications. In addition, we provide natural and fast heuristics to do price optimization. For one such Amazon business where we conducted a live A/B experiment, this new framework outperformed the existing pricing system in every key metric, increasing our target performance metric by 19%, while providing a robust platform to support future new services of the business. The model framework has now been in full production for this business since Q4 2023.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
