AI-DRIVEN DIGITAL MARKETING AND SUPPLY CHAIN OPTIMIZATION IN TEXTILE E-COMMERCE

The rapid expansion of textile e-commerce has transformed consumer purchasing behavior while creating new challenges in customer engagement, demand forecasting, inventory management, and supply chain coordination. Conventional digital marketing strategies often fail to provide personalized customer experiences and efficient operational decision-making due to limited integration between marketing intelligence and supply chain processes. This study proposes an integrated Artificial Intelligence (AI)-driven framework that combines customer behavior analytics, AI-enabled personalization, predictive demand forecasting, and supply chain optimization to improve business performance in textile e-commerce. Primary data were collected from textile e-commerce consumers, digital marketing professionals, and industry stakeholders using structured questionnaires, while secondary information was obtained from transaction records, platform analytics, and historical sales databases. The collected data were analyzed using descriptive statistics, regression analysis, and hypothesis testing to evaluate the relationships among trust, perceived value, interaction intensity, customer satisfaction, AI personalization, forecasting capability, and supply chain responsiveness. The findings demonstrate that perceived value, customer satisfaction, AI personalization, and AI forecasting significantly enhance purchase intention, customer loyalty, operational efficiency, and sustainability performance, whereas perceived risk negatively influences consumer purchasing behavior. The proposed framework provides practical insights for integrating AI-driven marketing intelligence with supply chain operations to improve customer experience, resource utilization, organizational agility, and long-term competitiveness in textile e-commerce.