Impact of AI-Driven Customer Analytics on Retail Marketing Effectiveness and Revenue Growth

Abstract

The retail industry has undergone a significant transformation with the integration of artificial intelligence (AI)-driven customer analytics, enabling more targeted and personalized marketing strategies. This paper explores the impact of AI-driven customer analytics on retail marketing effectiveness and revenue growth, emphasizing how the utilization of customer data helps in understanding consumer behavior, segmenting markets, and optimizing marketing campaigns. AI technologies, including machine learning and predictive analytics, allow retailers to extract valuable insights from vast amounts of consumer data, leading to enhanced decision-making, improved customer experiences, and ultimately, increased sales. The study examines the theoretical and empirical relationship between AI-driven customer analytics and key retail outcomes such as customer acquisition, retention, and overall revenue growth. By analyzing case studies and current trends in AI applications, the paper provides evidence of how AI-powered marketing strategies outperform traditional methods and contribute to long-term business success. The findings suggest that the effective use of AI-driven analytics can significantly enhance retail marketing efforts, driving substantial revenue growth through improved customer targeting and personalized experiences.