Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings
研究了住宿业中小企业在跨平台销售时，平台选择和卖家历史如何作为信号影响顾客感知价值并决定定价，以及时间价格歧视如何调节这些非核心产品信号的重要性，并运用机器学习方法帮助中小企业预测定价。
Value-based pricing is known to be challenging, especially on online platforms, but is considered a superior pricing strategy. We investigate cross-platform pricing and other factors that influence perceived customer value in the context of the accommodation industry. This industry is characterized by powerful platforms (e.g., Booking.com) as well as small and medium-sized enterprises (SMEs) selling across platforms. We compare the importance of platform choice and seller history as underlying signals conveying value and thus defining pricing beyond core product attributes. Such actor-signaling-actions for value are neglected in previous research. We pay particular attention to how time-based price discrimination affects the importance of these non-core product signals. As cross-platform efforts increase the complexity of value-based pricing, we apply machine learning methods to model how SMEs can successfully predict pricing across platforms. We discuss our methodological and theoretical contributions to value-based pricing and signaling theory.