Transforming Product Detail Pages (PDPs) into Intelligent Merchandising Engines: A Theoretical Framework for Adaptive E-Commerce Experiences

In the digital retail landscape, the Product Detail Page (PDP) serves as the most influential point in the consumer’s online purchase journey. Despite its centrality, the PDP is often underutilized in terms of personalization, dynamic content adaptation, and data-driven merchandising. This review presents a novel theoretical model—the Adaptive PDP Enhancement Model (APEM)—designed to transform static PDPs into responsive, intelligent platforms that integrate behavioral, contextual, emotional, and transactional data to deliver personalized, immersive shopping experiences. Drawing on literature from retail marketing, artificial intelligence, and human-computer interaction, this review synthesizes key research developments, identifies existing theoretical gaps, and introduces a multi-layered data integration framework for PDP optimization. A comparative analysis demonstrates that the proposed model outperforms traditional and AI-lite PDP systems across key e-commerce metrics such as conversion rates, engagement, and bounce rates. Practical implications for retailers and policymakers are explored, and recommendations are made for future research in the areas of cross-cultural personalization, ethical AI, and long-term customer value modeling. The APEM offers a forward-looking roadmap for practitioners and researchers aiming to build responsive and intelligent PDP architectures in a hyper-competitive digital economy.