Customer Segmentation and Purchase Prediction System

In the fast-paced world of retail analytics, using data to make important decisions has become a key part of doing well in business. To do data analytics, a lot of expenses are encountered. This paper explains a system that helps with customer behaviour analysis and prediction. This paper’s principal goal is to create a system that can enhance retail analytics by developing a comprehensive framework for understanding and predicting customer behaviour. This involves leveraging advanced analytical techniques to segment customers, analyse their purchasing patterns, and predict churn, to drive strategic decision-making and improve customer satisfaction. The paper proposes a multi-faceted approach, integrating multi-attribute segmentation, visualization of customer data, and customer behaviour analysis. Market basket analysis is used for product prediction, while artificial neural networks (ANNs) are applied for RFM segmentation and churn prediction that can predict the outcome with an accuracy of 83%. These approaches are made to offer a thorough comprehension of customer segments and their purchasing behaviours, as well as to predict future customer actions and retention.