9th International Conference on ICT for Sustainable Development

Authors - A.V. Dehankar, Suraj Bangade, Tanay Tijare, Ayush Wankhede, Akash Tidke, Samyak Meshram
Abstract - 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 expense is encountered. This paper explains a system that helps with customer behavior 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 behavior. This involves leveraging advanced analytical techniques to segment customers, analyze 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 behavior analysis. Market basket analysis is used for product prediction, while Artificial Neural Networks (ANN) 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 behaviors, as well as to predict future customer actions and retention.