In digital marketing environments, consumer preferences exhibit significant dynamic evolution and group heterogeneity. Traditional fuzzy multi-criteria decision-making methods generally assume that preference structures remain static over time, making them inadequate for capturing preference drift and the evolution of group consensus in intertemporal decision-making processes. To address this issue, this paper proposes a hesitant fuzzy group decision-making method for dynamic marketing environments that integrates a time-discounted preference measure and an adaptive consensus mechanism. First, a time-discounting factor is introduced to construct a dynamically updated model of intertemporal hesitant-fuzzy preference relationships, capturing the nonlinear characteristics of consumer preference decay over time. Second, an adaptive group consensus iterative algorithm is designed to dynamically adjust feedback intensity and adjustment rules based on the state of consensus evolution, thereby overcoming the shortcomings of fixed-threshold methods, such as sensitivity to outlier preferences and poor convergence efficiency. Furthermore, a scheme ranking rule based on group consensus satisfaction is proposed to achieve dynamic optimization of marketing strategies. The effectiveness and convergence performance of this method are verified through numerical simulation examples and compared with existing methods. This study provides a new methodological tool for real-time marketing decision-making in uncertain market environments.
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