PERSONA-BASED COMMERCE CATALOG IN ECOMMERCE USING AI WITH SPRING FRAMEWORK

Abstract: Personalization has become essential for success in the ever-changing world of ecommerce. One of the cutting-edge trends is the use of persona-based commerce catalogs, which customize product recommendations and displays based on segmented customer personas derived from behavioral and demographic data. The evolution of eCommerce demands highly personalized shopping experiences. This paper presents a persona-based commerce catalog system powered by Artificial Intelligence (AI) and implemented using the Spring Framework. By analyzing user behavior, preferences, and demographic data, the system dynamically generates product catalogs tailored to individual personas. The integration of AI-driven recommendation engines with Spring's modular architecture ensures scalability, performance, and maintainability. This approach enhances user engagement, boosts conversion rates, and provides a seamless shopping experience. The study also explores the system's architecture, implementation challenges, and potential impact on customer satisfaction and business outcomes in modern eCommerce platforms. The challenge of product overload on ecommerce platforms can lead to decision fatigue for users. Persona-based catalogs address this by aligning product recommendations with specific user needs and characteristics. This white paper discusses the concept of implementation using AI with the Spring framework, and its direct impact on company profitability.

Keywords: Personalization, Persona-Based Commerce, Artificial Intelligence (AI), Spring Framework, Recommendation Engine, User Engagemen, eCommerce Optimization