Smart Retailing Using IoT and Real-Time Analytics

  • Authors

    • Chloe King Senior Consultant, PwC, UK Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2022PII6U6S

    Published 11-04-2022

  • Smart Retailing, Internet of Things (IoT), Real-Time Analytics, Artificial Intelligence, Edge Computing, Cloud Computing, RFID, Customer Experience, Predictive Analytics, Retail Automation

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    King, C. (2022). Smart Retailing Using IoT and Real-Time Analytics. International Journal of Commerce, Finance and Digital Economy, 5(2), 01-16. https://doi.org/10.67228/3071642X/IJCFDE-2022PII6U6S

  • Abstract

    The Internet of Things (IoT) and real-time data analytics have revolutionized the retail sector, creating a smart, customer-centric environment that enables personalized shopping experiences, operational efficiency, and data-driven decision-making. Retail systems can have issues with over- or understocking, ineffective supply chain management, slow service times to customers, and lack of information regarding customer behaviors. Smart retailing is a solution that overcomes all these problems by combining the power of real-time analytics platforms with IoT sensors, Radio Frequency Identification (RFID), smart shelves, computer vision, cloud computing, and edge computing, as well as AI. These technologies allow for real-time stock tracking, self-service desks, demand prediction, individualised suggestions, real-time pricing and intelligent customer interaction. Real-time analytics also allows retailers to process large volumes of data in real-time to make more accurate decisions and minimize the costs of their operations. This paper introduces a comprehensive study and development of smart retail systems powered by IoT and real-time analytics, covering the technological evolution, system architecture, applications, challenges as well as research motivations. The research underscores the need for comprehensive, interdependent smart devices and robust analytical tools to create smart retail spaces, which can adapt to market dynamics and consumer choices in real time. In addition, the paper highlights the existing challenges, such as data privacy, cybersecurity threats, interoperability concerns, scalability, and implementation cost, which inspire future research on creating secure, scalable, and AI-integrated smart retail ecosystems for the next generation of digital commerce.

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