Abstract:

Firms often use differentiated pricing strategies based on the consumer segmentation from their purchasing histories, the so-called behavior-based pricing (BBP). The user information increases as the platform commerce grows, and how to use the information resources to improve the classification of users and implement segmented pricing becomes an important question faced by platform sellers. The heterogeneity and bounded rationality of online consumers even complicates the problem. Employing a multi-methodology of analytical modeling and agent-based modeling and simulation, this paper compares a varied set of information and segmentation policies. An in-depth investigation on consumer behavioral factors such as willingness-to-pay, loyalty, and bounded rationality was conducted. The findings indicate that the consumer segmentation based on the historical purchasing records benefits the platform seller more than the segmentation based on the labels of loyalty. Less information may benefit the seller more. The seller benefits from BBP when consumers have bounded rationality, and the benefit of BBP is more prominent when consumers have a lower willingness-to-pay for the product. In addition, when a competitor deploys a BBP strategy, the seller benefits from differentiated pricing instead of uniform pricing to counteract the rival.

Key words:

behavior-based pricing, user segmentation, loyalty, bounded rationality, multi-agent based modeling and simulation

摘要：

行为定价是企业实践中经常采用的策略，是指基于用户行为进行分类并实施区别定价。随着平台经济的发展，平台方拥有丰富的用户信息，竞争企业如何充分利用这些信息资源细分用户并制定价格，成为一个重要的管理问题。平台用户的品牌偏好异质性和有限理性行为使得这个问题更具挑战。基于数学建模和多智能体建模仿真方法，对比分析不同的信息和分类策略的优劣性。进一步探讨包括消费者购买意愿、品牌忠诚、有限理性等多种行为因素对消费者购买决策和价格博弈的影响。研究发现，根据用户的历史购买记录对用户进行分类并采取不同的价格策略，企业受益最大；反之，信息过量可能导致企业受损。面临消费者有限理性行为，企业采用行为定价策略可从中获益；当市场上消费者的产品购买意愿较低时，区别定价能为企业带来更显著的收益提升。此外，当竞争对手采用行为定价策略时，企业实施区别定价而非统一定价应对有利于自身。

关键词:

行为定价, 用户细分, 品牌忠诚, 有限理性, 多智能体建模与仿真