Distributed AdTech Systems for Personalized Fashion Marketing

Abstract

The rapid evolution of advertising technology (AdTech) has transformed the fashion industry, enabling data-driven personalization at unprecedented scales. However, centralized AdTech models often face limitations in latency, scalability, and privacy compliance, making them less effective in meeting the demands of dynamic fashion marketing. This study investigates the role of distributed AdTech systems in enhancing personalization, consumer engagement, and trust within the fashion sector. Using a mixed-methods approach, data were collected from e-commerce platforms, social media interactions, and in-store transactions of 2,500 fashion consumers. The distributed system was tested across seasonal promotions, flash sales, and routine shopping scenarios, and results were compared against a centralized model. Findings show that distributed systems significantly outperformed centralized models by reducing latency by 67%, tripling scalability, and improving personalization accuracy, particularly in routine shopping contexts. Consumer behavior metrics revealed higher click-through and conversion rates, increased repeat purchases, and reduced cart abandonment, while consumer trust was strengthened through blockchain-enabled transparency and federated learning for privacy preservation. Regression analysis identified recommendation accuracy and latency as key predictors of conversion, and cluster analysis revealed four distinct consumer groups with unique engagement patterns. These results underscore the transformative potential of distributed AdTech systems in balancing efficiency, personalization, and consumer trust, offering both theoretical contributions and practical strategies for fashion marketers.