AI-Driven Personalized Shopping: Enhancing Retail Experiences through Body Type Analysis and Real-Time Inventory Integration
Year: 2024
Type: article
Abstract: : This article introduces an innovative AI-driven personalized shopping system integrating body type analysis, real-time inventory management, and smart recommendations to transform the retail experience. The system significantly improves fit satisfaction, shopping efficiency, and inventory optimization by leveraging advanced computer vision, machine learning, and deep learning technologies. User trials involving 5,000 participants show a 40.3% increase in fit and style satisfaction, 37% reduction in shopping time, and 28% increase in conversion rates compared to traditional methods. The system's ability to provide highly accurate, personalized recommendations at scale addresses key challenges in e-commerce, potentially revolutionizing the retail industry by enhancing customer satisfaction, reducing returns, and optimizing inventory management.
Institutions: Towson University, Texas A&M University, Dexcom (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402986237