In the context of online retail, e-commerce platforms can construct precise user profiles by leveraging multi-dimensional consumer data, including geographical location, purchase history, and online search behavior. Through data mining algorithms and other advantageous data empowerment technologies, these platforms can formulate personalized pricing strategies and improve profitability via accurate market positioning and differentiated product pricing. Personalized pricing not only provides consumers with a tailored shopping experience, but also increases the likelihood of sales conversions. However, given the distinct sales modes employed by platforms and manufacturers (i.e., reselling vs. agency selling), it is essential to examine how platforms determine their data empowerment efforts under different sales modes and how personalized pricing affects their sales mode selection strategies.To address the issue of sales mode selection (reselling vs. agency selling) under data empowerment, this study posits that platforms can use data empowerment to implement personalized pricing for consumers. We formulate a Stackelberg game model to analyze the interaction between a platform and a manufacturer. When data empowerment is not available, under the reselling mode, the manufacturer first sets the wholesale price and then the platform determines the retail price; while under the agency selling mode, the manufacturer directly sells products to consumers and sets the retail price. When data empowerment is available, under the reselling mode, the manufacturer sets the wholesale price, and the platform determines the level of data empowerment efforts before implementing personalized pricing. On the other hand, under the agency selling mode, the platform determines the level of data empowerment efforts and shares the results with the manufacturer, who then sets the personalized price. This study examines four scenarios: agency selling without data empowerment, reselling without data empowerment, agency selling with data empowerment, and reselling with data empowerment, and derives the equilibrium outcomes for each scenario.Firstly, this study investigates the platform’s decision on levels of data empowerment efforts under different sales modes. By comparing the equilibrium effort levels between reselling and agency selling modes, we find that the platform exerts a higher level of efforts under the reselling mode when data empowerment efficiency is high. This is primarily because, under reselling mode, the platform retains pricing power and is incentivized to invest more in data empowerment to improve the accuracy of personalized pricing, thereby maximizing profits. Thus, higher data empowerment efficiency amplifies the platform’s effort under the reselling mode.Secondly, this study explores the impact of personalized pricing on platform’s sales mode selection strategies under data empowerment. By comparing the platform’s equilibrium profits under reselling and agency selling, we find that, in addition to the commission ratio, product type and consumer awareness of data privacy protection also significantly influence the accuracy of personalized pricing, thereby affecting the platform’s sales mode selection. For experience-based products, whose consumer quality perceptions vary widely, personalized pricing can be adjusted within a broad range. Higher data empowerment efficiency motivates the platform to adopt the reselling mode to expand market demand. Conversely, when data empowerment efficiency is low, the agency selling mode becomes dominant, to avoid the dual marginalization effect in the supply chain. For search-based products, whose consumer quality perceptions are more homogeneous, the range of personalized pricing adjustments is narrower. In such cases, the platform must balance the benefits of personalized pricing against the negative effects of the double marginalization, leading to potential flexibility in choosing either sales mode.Thirdly, this study analyzes how personalized pricing under data empowerment affects the potential for win-win situation between platforms and manufacturers in sales mode selection. By examining Pareto optimal solutions under reselling and agency selling, we find that, win-win situation is only achievable under agency selling in scenarios without data empowerment, while in scenarios with data empowerment, personalized pricing generates spillover effects, enabling the platform and the manufacturer to achieve win-win outcomes under both sales modes. Under reselling, the platform’s pricing power and higher data empowerment efficiency incentivize greater effort in data empowerment, thereby increasing market demand. The manufacturer benefits from the spillover effects of data empowerment, in turn making reselling preferable. Under agency selling, while the double marginalization effect is avoided, the manufacturer’s “free riding” behavior may suppress the platform’s investment in data empowerment. Thus, win-win situation under agency selling is only feasible when the commission ratio is moderate.Finally, this study examines the impact of personalized pricing on consumer surplus. On one hand, personalized pricing can enhance firms’ ability to extract greater consumer surplus by setting more precise personalized prices. On the other hand, it can also attract consumers with lower willingness to pay, converting them from non-purchasers to purchasers and improving consumer surplus, through the implementation of appropriate personalized pricing. By comparing consumer surplus with and without data empowerment, under reselling and agency selling, we find that when consumers have moderate or high privacy protection awareness, and the efficiency of data empowerment is not exceedingly high, it leads to incomplete market coverage. In such cases, the platform can increase its efforts in data empowerment to establish more appropriate personalized pricing, thereby attracting additional potential consumers and expanding market demand. Ultimately, consumers benefit from personalized pricing.In summary, this study investigates the interaction between data-driven personalized pricing and platform sales mode selection, and explores its implications for platform-manufacturer cooperation and consumer surplus. The findings provide theoretical insights for optimizing sales mode collaboration and data empowerment strategies in practice. Specifically, this study highlights that personalized pricing can yield positive outcomes under certain conditions, offering valuable guidance for policymakers in formulating relevant regulations.