Exploring Innovative Approaches in Buyer Differentiation: A Detailed Examination of AI- Powered Methods and RFM-Centric Strategies for Practical Intelligence

In today’s competitive retail landscape, understanding and landscape, understanding and predicting customer behaviour is essential for business success. However, traditional data analysis methods can be costly and resource intensive. To overcome these challenges, an innovative system has been introduced that utilizes advanced analytical methods to streamline retail analytics. This framework is engineered to construct a sturdy model for interpreting and predicting user tendencies. It applies techniques such as multi-criteria classification, visual representation of information, and evaluation of purchasing behavior to segment buyers, investigate their spending trends, and anticipate possible client departure. Additionally, it utilizes market basket analysis to predict produce purchases and artificial neural networks (ANN) to segment customers and predict churn. Integrating these methods enables businesses to derive meaningful insights into customer groups, buying patterns, and anticipated behaviours, fostering enhanced customer retention and informed strategic decisions.