AI-DRIVEN PREDICTIVE ANALYTICS IN RETAIL: A REVIEW OF EMERGING TRENDS AND CUSTOMER ENGAGEMENT STRATEGIES

Authors

  • David Iyanuoluwa Ajiga Independent Researcher, Chicago, Illinois, USA
  • Ndubuisi Leonard Ndubuisi Spacepointe Limited Rivers State, Nigeria
  • Onyeka Franca Asuzu Dangote Sugar Refinery Plc, Lagos, Nigeria.
  • Oluwaseyi Rita Owolabi Independent Researcher, Indianapolis Indiana, USA
  • Tula Sunday Tubokirifuruar Department of Accounting, Ignition Ajuru University of Education, Rivers State, Nigeria
  • Rhoda Adura Adeleye Information Technology & Management, University of Texas, Dallas, USA

DOI:

https://doi.org/10.51594/ijmer.v6i2.772

Abstract

As the retail landscape undergoes a profound transformation in the era of digitalization, the integration of Artificial Intelligence (AI) and predictive analytics has emerged as a pivotal force reshaping the industry. This paper provides a comprehensive review of the latest trends in AI-driven predictive analytics within the retail sector and explores innovative customer engagement strategies that leverage these advanced technologies. The review begins by elucidating the foundational concepts of AI and predictive analytics, highlighting their synergistic role in forecasting consumer behavior, demand patterns, and market trends. The paper then delves into the emerging trends, such as machine learning algorithms, natural language processing, and computer vision, that are revolutionizing the way retailers harness data for strategic decision-making. In addition to outlining technological advancements, the paper emphasizes the crucial role of data quality and ethical considerations in the implementation of AI-driven predictive analytics. It examines the challenges associated with privacy concerns, algorithmic bias, and the need for transparent AI models to ensure responsible and fair use of customer data. Furthermore, the paper explores a spectrum of customer engagement strategies enabled by AI-driven predictive analytics. From personalized shopping experiences and targeted marketing campaigns to dynamic pricing and inventory optimization, retailers are deploying innovative approaches to enhance customer satisfaction and loyalty. The review also discusses case studies of successful AI implementations in leading retail enterprises, showcasing tangible benefits such as improved operational efficiency, increased sales, and enhanced customer retention. These real-world examples illustrate the transformative impact of AI-driven predictive analytics on diverse aspects of the retail value chain. By examining emerging trends and customer engagement strategies, it serves as a valuable resource for industry professionals, researchers, and policymakers seeking to navigate the evolving landscape of AI in the retail sector.

Keywords: AI-driven Predictive Analytics, Retail Industry, Customer Engagement Strategies, Machine Learning Algorithms, Natural Language Processing.

Published

2024-02-13

Issue

Section

Articles