A Big Data Discriminatory Pricing Strategy for Takeaway Platforms Considering Different Attributional Behaviors of Users

Abstract

To address the big data discriminatory pricing problem of two takeout platforms with different attribution behaviors of platform users, the equilibrium pricing of the two platforms under different attribution behaviors is analyzed and compared based on the Hotelling model, and the effects of switching costs and network effects on pricing strategies are examined. The results show that: in the single-attribution-single-attribution model, the network effect affects the user scale of the platform, and then the pricing of the platform is affected by the switching costs and the network effect, and the pricing of the platform in the first stage affects the pricing decision in the second stage; in the single-attribution-partial-multi-attribution model, the pricing of the platform is affected not only by the switching cost and the network effect but also by the switching costs and the network effect of the user base. In the single-attribution-partial-multi-attribution model, platform pricing is not only affected by switching costs and network effects but also by users' base utility, and the platform's pricing in the first stage partially affects the pricing decision in the second stage.