Customer Transactional Analysis along with Customer Segmentation and Feedback Analysis

All customers come from diverse backgrounds and do not have the same requirements. Customer transactional analysis is important for customer-oriented industries. Customers can be treated differently according to their requirements and companies can win preference with a wider range of customers. In order to accomplish the goal, several machine learning approach can be used. The most efficient method to accomplish Customer Transactional Analysis is to separate customers into different segments. The proposed customer segmentation model can be used by companies and firms to develop strategies for marketing; Kmeans clustering algorithm is used for customer segmentation based on the attributes such as products purchased, cost of orders etc. This paper also proposes a methodology for customer feedback analysis which gives a summary of total feedbacks for a particular product so that the retailers can have a better understanding of how their products feature in the market. This will help retailers to know which product needs improvement and how the most popular products can be optimized even more.