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

Abstract This paper investigates the differential pricing strategies of third-party retailers based on Private Domain Traffic Pool (hereinafter referred to as Private Domain) and the corresponding public traffic allocation strategies of e-commerce platforms. Against the backdrop of diminishing internet demographic dividends and intensified traffic competition, we develop a two-stage dynamic game-theoretic model to analyze three scenarios: (a) a retailer not building a Private Domain; (b) a retailer building one Private Domain within the platform; (c) a retailer building one Private Domain outside the platform. The key findings are as follows. First, a retailer’s optimal pricing strategy in its Private Domain depends on its personalized service level and the platform’s opportunity cost of public traffic allocation. Notably, the retailer may engage in big-data-enabled price discrimination (“killing the familiar”) under specific conditions. Compared to an in-platform Private Domain, an off-platform Private Domain can offer a dual advantage in both price and service level, since the lower usage costs associated with private traffic outside the platform for the retailer. Secondly, the platform’s public traffic allocation strategy reflects a clear regulatory stance: it consistently allocates the lowest quota to retailers with off-platform Private Domain, while its allocation for in-platform Private Domain is conditional, encouraging high-quality personalized service. Third, establishing a Private Domain outside the platform always harms the platform’s long-term profit, while the impact of an in-platform Private Domain is uncertain and depends on user stickiness and personalized service level. Fourth, a retailer’s profit-maximizing choice of Private Domain location is primarily determined by the unit cost of personalized service inside versus outside the platform. Fifth, from a consumer welfare perspective, we find that building an off-platform Private Domain consistently yields the lowest per capita consumer surplus in the first stage. In contrast, in-platform Private Domains can enhance per capita consumer surplus at the second stage when offering sufficiently high personalized service levels, whereas off-platform Private Domains require even higher service standards to achieve comparable welfare improvements. This highlights the structural advantage of in-platform ecosystems in delivering superior price-service value to consumers. This study provides theoretical insights and practical implications for platform governance and retailer strategy in the era of private domain operations.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-10-09
DOI
https://doi.org/10.1057/s41599-026-08469-1
Primary Topic
Digital Platforms and Economics
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Qin Wan, Cuiting Yu, Nan Zhou, Lin Liang et al.
Humanities and Social Sciences Communications
Digital Platforms and Economics
article

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

Qin Wan, Cuiting Yu, Nan Zhou, Lin Liang, Xiyu Yang, Yifei Xiong
article en

Abstract

Abstract This paper investigates the differential pricing strategies of third-party retailers based on Private Domain Traffic Pool (hereinafter referred to as Private Domain) and the corresponding public traffic allocation strategies of e-commerce platforms. Against the backdrop of diminishing internet demographic dividends and intensified traffic competition, we develop a two-stage dynamic game-theoretic model to analyze three scenarios: (a) a retailer not building a Private Domain; (b) a retailer building one Private Domain within the platform; (c) a retailer building one Private Domain outside the platform. The key findings are as follows. First, a retailer’s optimal pricing strategy in its Private Domain depends on its personalized service level and the platform’s opportunity cost of public traffic allocation. Notably, the retailer may engage in big-data-enabled price discrimination (“killing the familiar”) under specific conditions. Compared to an in-platform Private Domain, an off-platform Private Domain can offer a dual advantage in both price and service level, since the lower usage costs associated with private traffic outside the platform for the retailer. Secondly, the platform’s public traffic allocation strategy reflects a clear regulatory stance: it consistently allocates the lowest quota to retailers with off-platform Private Domain, while its allocation for in-platform Private Domain is conditional, encouraging high-quality personalized service. Third, establishing a Private Domain outside the platform always harms the platform’s long-term profit, while the impact of an in-platform Private Domain is uncertain and depends on user stickiness and personalized service level. Fourth, a retailer’s profit-maximizing choice of Private Domain location is primarily determined by the unit cost of personalized service inside versus outside the platform. Fifth, from a consumer welfare perspective, we find that building an off-platform Private Domain consistently yields the lowest per capita consumer surplus in the first stage. In contrast, in-platform Private Domains can enhance per capita consumer surplus at the second stage when offering sufficiently high personalized service levels, whereas off-platform Private Domains require even higher service standards to achieve comparable welfare improvements. This highlights the structural advantage of in-platform ecosystems in delivering superior price-service value to consumers. This study provides theoretical insights and practical implications for platform governance and retailer strategy in the era of private domain operations.

Humanities and Social Sciences Communications
Southwest Petroleum University (CN), Chengdu Neusoft University
Openalex Percentile: Top 9%
Digital Platforms and Economics
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