Authors : Dr. Umesh Akare; Girish Umaratkar; Mukesh P. Giri; Megha N. Tagade; Ekta N. Chopde

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/4fft6uxb

Scribd : https://tinyurl.com/5ds6wffc

DOI : https://doi.org/10.38124/ijisrt/25mar017

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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.

Keywords : Retail Analytics, Customer Segmentation, Artificial Neural Networks, RFM Segmentation, Churn Prediction.

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