Consumer privacy and platform profitability: exploring the effects of data analysis capability on pricing and privacy design strategy

Purpose This study aims to examine the impact of the data acquisition and analysis abilities of two platforms on privacy design and pricing strategy choice in a competitive scenario and tries to find out the optimal privacy design and pricing strategy and conditions for platforms and consumers to achieve a win-win situation. Design/methodology/approach This study constructs a Hotelling model to analyze the impact of data acquisition and analysis capabilities of two competitive platforms on privacy design and pricing strategy selection. Findings This research finds that the platform that doesn’t provide privacy labels can also achieve higher profits than the platform that does. In a market where consumers value the quality of the platform’s products or services, if the data acquisition and analysis ability of a competing platform is weak, choosing uniform pricing is the best strategy for the platform. When the data acquisition and analysis ability of the two platforms are both weak, choosing differentiated pricing can achieve a win-win situation for both the platform and the consumers on it. Originality/value This study demonstrates that platforms can maintain profitability by enhancing their data acquisition and analysis capabilities, regardless of whether consumers focus more on the quality of platform products or services or prioritize privacy protection, which challenges the prevailing notion of “the necessity of personalized privacy customization,” offering a novel perspective for decision-making in platform privacy design. We also propose a novel approach for platforms to dynamically adjust pricing strategy based on their own and competitors’ data capabilities, which means due to the asymmetric impact of data capability, platforms must conduct two-way evaluations of their data capability matrix relative to rivals.