Modeling and analysis of private domain operations: platform traffic allocation and retailer pricing

A two-stage dynamic game-theoretic model predicts that retailers’ optimal prices depend on their personalized service levels and the platform’s opportunity cost of allocating public traffic. The study compares three scenarios: a retailer without a private domain, a retailer that builds one within the e-commerce platform, and a retailer that builds one outside the platform. Its findings describe relationships generated by the model, rather than pricing, traffic allocations or consumer outcomes measured from actual transactions.

Within the model, a retailer’s private-domain pricing strategy can include big-data-enabled price discrimination, described by the researchers as “killing the familiar,” under particular conditions. The strategy is not predicted to occur in every configuration. Instead, the results connect the possibility of differentiated pricing to personalized service and the platform’s opportunity cost for assigning public traffic. This gives the paper a technically focused account of how private-domain operations can interact with a platform’s broader allocation decisions, while also making clear that the modeled outcomes remain conditional rather than universal.

The location of the private domain produces a distinct strategic contrast. Compared with an in-platform private domain, an off-platform private domain can offer the retailer an advantage in both price and service level because private traffic outside the platform has lower usage costs. The platform responds by consistently allocating the lowest public-traffic quota to retailers operating an off-platform private domain. Allocation to an in-platform private domain is conditional rather than uniformly restrictive, with the model allowing the platform to favor high-quality personalized service. Thus, the analysis presents platform traffic allocation as a strategic response to where a retailer develops its private domain and to the quality of service associated with that arrangement.

Platform profitability also differs between the two private-domain locations. In the modeled game, establishing a private domain outside the platform always harms the platform’s long-term profit. The effect of an in-platform private domain is uncertain: it depends on user stickiness and personalized service level. For the retailer, the profit-maximizing location is determined primarily by the unit cost of personalized service inside versus outside the platform. Cost therefore emerges as the central comparison in the retailer’s location decision, while the platform’s response and long-term profit depend on whether the private domain remains within its ecosystem.

Consumer-welfare results are similarly conditional. In the first stage of the game, an off-platform private domain yields the lowest per-capita consumer surplus among the three scenarios. In the second stage, an in-platform private domain can increase per-capita consumer surplus when personalized service is sufficiently high. An off-platform private domain must meet an even higher service standard to achieve comparable welfare improvements. The researchers interpret these results as evidence of a structural advantage for in-platform ecosystems in delivering price-service value to consumers, but the comparison applies to the stages and assumptions of the model. The study offers predictions for platform governance, retailer strategy and consumer welfare without establishing that any particular company has adopted the modeled strategies or realized the predicted outcomes.

Subject of Research: Game-theoretic analysis of private-domain operations, retailer pricing and e-commerce platform traffic allocation.

Article Title: Modeling and analysis of private domain operations: platform traffic allocation and retailer pricing

Article References: Wan, Q., Yang, X., Liang, L., Xiong, Y., Zhou, N., & Yu, C. (2026). Modeling and analysis of private domain operations: platform traffic allocation and retailer pricing. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-08469-1

Image Credits: AI Generated

DOI: 10.1057/s41599-026-08469-1

Keywords: private-domain operations, platform traffic allocation, retailer pricing, dynamic game theory, price discrimination, personalized service, consumer welfare, e-commerce platforms, platform governance, Modeling, analysis, private