面向不同更新频率的数据产品定价策略研究

Pricing Strategies for Data Products with Different Update Frequencies

摘要: 数据要素价值化是推动数据市场化配置改革的重要路径。考虑到数据产品的价值往往呈现时效性特征，如何设计合理定价策略以提升数据交易双方匹配效率成为关键问题。根据供需双方交易行为分析，考虑数据需求者的初始感知价值具有异质性，基于更新频率构建数据交易双方的期望效用函数，通过分析均衡状态下数据供应商的收益情况，探讨不同数据产品质量和数据更新频率组合策略下的最优动态定价。研究发现：当数据更新成本偏低时，向初始感知价值偏高的数据需求者设定较低的数据更新频率为数据供应商的最优决策。当数据更新成本偏高时，“质量偏高、更新频率偏低”下的数据产品交易价格高于“质量偏低、更新频率偏高”下的交易价格。此外，当市场竞争强度偏高时，数据供应商预先决策数据产品质量下的收益高于同时决策质量和更新频率下的收益。本文的研究结论可进一步丰富现有的数据产品定价研究，并为数据供应商设计合理的数据更新频率策略提供指导借鉴。

Abstract: Valuing data elements is a crucial path to promoting market-based allocation reform of data. Considering the time-sensitive nature of data product value, designing reasonable pricing strategies to improve matching efficiency between data transaction parties becomes a key issue. Based on the analysis of supply and demand behavior, and considering the heterogeneity of the initial perceived value of data demanders, this paper constructs the expected utility function of both parties in data transactions based on update frequency. By analyzing the revenue of data suppliers under equilibrium conditions, it explores the optimal dynamic pricing under different combinations of data product quality and data update frequency strategies. The study finds that when data update costs are low, setting a lower data update frequency for data demanders with higher initial perceived value is the optimal decision for data suppliers. When data update costs are high, the transaction price of data products with “high quality and low update frequency” is higher than that with “low quality and high update frequency.” Furthermore, when market competition is intense, the revenue of data suppliers who pre-determine data product quality is higher than the revenue of simultaneously deciding on both quality and update frequency. The findings of this paper can further enrich existing research on data product pricing and provide guidance for data suppliers to design reasonable data update frequency strategies.