Machine Learning's Impact on Customer Behavior Analytics: Current Trends, Challenges, and Future Directions
Mohd Rahman bin Abdullah
Department of Computer Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia
Khurshed Iqbal
Department of Management sciences, UCoZ Campus, BUITEMS
Abstract
The field of customer behavior analytics has undergone a significant transformation with the advent of machine learning (ML) technologies. This paper explores the role of machine learning in the evolution of customer behavior analytics, highlighting current trends, challenges, and future directions. The integration of ML algorithms has enabled businesses to gain deeper insights into customer preferences, purchasing patterns, and overall behavior. Key trends include the use of predictive analytics, personalized customer experiences, and real-time data processing. However, several challenges impede the full potential of ML in this domain, such as data privacy concerns, algorithmic bias, and the need for large datasets. This paper aims to provide a an overview of how machine learning has transforming customer behavior analytics and to propose strategies to address existing challenges, ensuring sustainable and ethical implementation. Future directions point towards more sophisticated ML models, enhanced data integration, and improved regulatory frameworks to foster innovation while safeguarding customer interests.
