Abstract: With the development of the information economy, data has emerged as a pivotal production factor within the digital economy, and it has led to the emergence of data transactions involving consumer information in the market. Although data brings tremendous benefits to both enterprises and consumers, the practice of enterprises utilizing consumer data to implement discriminatory pricing has raised significant societal concerns. It is imperative to prioritize research focusing on achieving an optimal balance between the effective utilization of data and preventing the adverse effects of discriminatory pricing on consumer welfare for the sustainable development of the current platform economy. To address this issue, a platform economy is constructed with the participation of three players, namely, platform-merchant-consumers. We propose a game-theoretic framework encompassing three scenarios: no data transactions, data transactions, and optimized data transactions between the merchant and the platform. Our findings indicate that data transactions segment the consumer data acquired by the merchant into various types with distinct values. Consequently, the platform should adopt differentiated data pricing strategies tailored to these distinct data types. Furthermore, a comparative analysis of consumer surplus across the three scenarios reveals that personalized pricing by the merchant, particularly under the optimized data transactions scenario, enhances the welfare of consumers with low willingness to pay. This situation creates a mutually beneficial outcome for the platform-merchant-consumers triad. However, the impact of personalized pricing on consumers with high willingness to pay largely hinges on the extent to which the platform can convert accumulated data into consumer utility. In instances of high conversion rates, personalized pricing contributes positively to the welfare of such consumers. Ultimately, we demonstrate that whether personalized pricing in data transactions can enhance total consumer surplus, producer surplus, and social welfare depends critically on the accuracy of the platform’s data and the conversion of data into utility. This study introduces a novel approach to mitigating the issue of consumer welfare detriment resulting from discriminatory pricing. Additionally, it also holds value and significance in efficiently vitalizing data resources, converting them into valuable data assets, and enhancing the efficacy of data resource distribution.

Key words: data transactions, data pricing, personalized pricing, heterogeneous consumers