IT Management Strategies for Implementing Personalized Marketing with Machine Learning in the U.S. Retail Sector - Hossain, Md Azhad; Hosen, Arif; Ahmed, Arifa; Ahmed, Fahad; Imran, MD Ahsan Ullah; Aziz, Mustakim Bin; Noor, Syeda Kamari; Sizan, Arif Ahmed (Journal of Posthumanism, 2023)
In the dynamic landscape of the U.S. retail industry, personalized marketing has become a vital approach to strengthening customer engagement and driving sales. This paper investigates the application of machine learning (ML) techniques to create customized marketing strategies that align with individual consumer preferences and behaviors. By leveraging extensive datasets from both online and in-store transactions, the study utilizes predictive analytics to segment customers, deliver targeted product recommendations, and design personalized promotional campaigns. The integration of ML enables retailers to better understand their customers, leading to more meaningful interactions and improved shopping experiences. The results demonstrate that businesses employing ML-driven personalization report significant improvements in engagement metrics, increased brand loyalty, and higher conversion rates. This research highlights the transformative impact of data-driven insights on retail marketing strategies and offers practical guidance for retail managers seeking to stay competitive by enhancing the customer journey through technology-enabled personalization.