MACHINE LEARNING AND DATA-DRIVEN ARCHITECTURE FOR REAL-TIME ENHANCEMENT OF CUSTOMER ENGAGEMENT AND EXPERIENCE IN PHYSICAL RETAIL ENVIRONMENTS

Authors

  • Jinwoo Park

    Department of Artificial Intelligence, Seoul National University Institute of Technology, Seoul, South Korea

    Author

  • Minseo Lee

    Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Author

Keywords:
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Abstract

The rapid evolution of machine learning (ML) and artificial intelligence (AI)-driven architectures has significantly transformed physical retail environments by enabling real-time personalization, predictive analytics, and adaptive customer engagement systems. This study examines a machine learning and data-driven architectural framework designed to enhance customer experience and engagement within brick-and-mortar retail settings. Unlike traditional e-commerce-centric personalization systems, the proposed framework integrates multimodal data streams from in-store sensors, customer behavior tracking systems, conversational agents, and recommendation engines to create a unified real-time decision-support ecosystem.
Drawing upon prior research in recommender systems (Amatriain & Basilico, 2015), AI-enabled marketing transformation (Davenport et al., 2020), and conversational retail systems (Budnik et al., 2019), this paper synthesizes existing knowledge while proposing an integrated architectural model tailored for physical retail environments. The study identifies how AI chatbots, voice assistants, computer vision systems, and real-time recommendation engines collectively influence customer satisfaction, engagement duration, and purchase conversion rates.
The findings suggest that machine learning-based retail ecosystems significantly improve contextual personalization, reduce cognitive load during decision-making, and enhance emotional engagement. However, challenges such as data privacy concerns, infrastructural complexity, and algorithmic bias remain critical limitations. The paper contributes a structured analytical framework that bridges theoretical advancements and practical deployment strategies for AI-driven retail transformation.

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Published
2026-04-05
Issue
Vol. 14 No. 04 (2026): Volume 14 Issue 04
Section
Articles
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Copyright (c) 2026 Jinwoo Park, Minseo Lee (Author)

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How to Cite

Jinwoo Park, & Minseo Lee. (2026). MACHINE LEARNING AND DATA-DRIVEN ARCHITECTURE FOR REAL-TIME ENHANCEMENT OF CUSTOMER ENGAGEMENT AND EXPERIENCE IN PHYSICAL RETAIL ENVIRONMENTS. IFSMRC African International Journal of Research in Management, 14(04), 1-12. https://ifsmrc.org/index.php/aijrm/article/view/75

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