Digital Marketing Personalization Under Constraints: Multi-Objective Optimization for Fairness, Privacy, and ROI
Annotatsiya
The research is situated within a rapidly evolving digital marketing landscape where current and future challenges are to use personalized strategies in these algorithmic environments in different contexts like advertising, e-commerce, and content delivery and consumer analytics among others. However, a critical issue in some of this research is that the underlying logic explaining trade-offs of personalization is rarely happening within empirical frameworks. This study will contribute to this domain by identifying these competing objectives and by focusing on an integration of optimization models in decision-making. (TOPSIS variant kept as it also serves the same function) This article aims to construct a multi-objective model of tensions between return on investment and ethical constraints to understand the feasibility and impact of the current deployment of personalization. In the methodology, the performance of every alternative of the decision matrix are evaluated and ranked, and then the TOPSIS technique is used as a benchmark method to determine the relative efficiency of every scenario. An SEM analysis was made to test hypotheses in relation to user trust, quantifying the latent constructs according to the observed indicators, reflecting on the complexity of the decision process by linking to the perceived fairness, and offering an insight into implications for platform designers. It was shown that a balanced strategy (i.e. equity-aware personalization) based on a hybrid framework can yield outcomes at least comparable to that produced by traditional optimization, be that privacy-focused or profit- maximizing as in the baseline models presented. It reveals that multi-objective approaches may depend on the combined dynamics of preferences and constraints with significant implications in the strategic alignment, along with regulatory compliance.