Personalized Pricing in Digital Markets: Static Efficiency, Dynamic Competition and Social Welfare Risks

Abstract

Driven by big data and artificial intelligence, personalized pricing has become prevalent across digital platforms, triggering fierce debates over its social welfare impacts. This paper defines personalized pricing as data-based price discrimination built on consumers' estimated willingness to pay. It systematically evaluates the policy from three dimensions: static efficiency improvement, dynamic innovation incentives, and regulatory feasibility alongside ethical rationality. While personalized pricing reduces deadweight loss under uniform pricing and aids cost recovery for high-fixed-cost digital industries, its large-scale diffusion triggers severe side effects: cross-border arbitrage distorts market demand, public perceptions of unfairness erode consumer trust and shrink market scale, scale advantages of giant platforms consolidate market concentration and suppress long-run innovation, and high monitoring costs plus rent-seeking risks render effective regulation difficult. Dutch consumer survey data, cases including Daigo luxury parallel imports, Uber surge pricing and Coca-Cola's weather-based vending machine pricing support the arguments. Overall, the social costs of universal personalized pricing outweigh its efficiency gains. Targeted institutional guidelines are required to constrain abusive discrimination and balance resource allocation efficiency with consumer rights protection.