Data-Driven Marketing Strategy Optimization in Personalized E-Commerce: A Game-Theoretic Analysis of Platform Channel Choice

The rapid expansion of the platform economy has made data-driven marketing a key mechanism through which e-commerce platforms create value, coordinate market participation, and shape downstream welfare outcomes. This study examines how competing e-commerce platforms choose between reselling and agency modes when only one platform can endogenously implement data-driven marketing. We develop a game-theoretic model consisting of one manufacturer, one data-capable platform, one traditional platform, and a consumer market. Four channel structures are considered: reselling–reselling, agency–reselling, reselling–agency, and agency–agency. The analysis combines Stackelberg game modeling within each channel structure with Nash equilibrium analysis across platform channel choices. The results show that channel equilibrium is jointly determined by the agency fee ratio and data-driven marketing efficiency. The data-capable platform is generally more inclined to adopt the agency mode, whereas the traditional platform’s preferred response varies across parameter regions. Under a low-commission environment, the two platforms may fall into a prisoner’s-dilemma structure in which joint reselling is Pareto superior but not stable in equilibrium. The results further show that manufacturer profit decreases with the agency fee ratio but increases with data-driven marketing efficiency, while consumer surplus rises with stronger data-driven marketing and is generally higher when the data-capable platform adopts agency. By linking platform competition, channel strategy, and stakeholder outcomes under data-driven marketing, this study contributes to research on value creation and sustainable governance in the platform economy, and it offers managerial implications for data monetization, channel coordination, and welfare-enhancing digital retail design.