Customer segmentation and behavioral systems through influential effective elements: An E-satisfaction analysis using machine learning.

In the digitalization of the present world, customer-based E-services have made a lot of progress because of thefusion of the E-commerce sector with the machine learning paradigm. It presents an appropriate, flexible and easy-to-use environment for the customers to purchase the products and give them a variety of products through the Internet. Today's industrial scenario moves toward the client-centric market. So, this market requires the effective partitioning of customers using influential effective elements. This paper presents a detailed study of customer behavioral and segmentation models for E-satisfaction using K-Means, modified K-means, and other variations of K-based clustering techniques. It provides the comparison of the statistical and analytical results of various existing models along with the consideration of their data attributes and effective elements. Further, it provides suggestions and extensions to improve their results in the E-market. It also signifies the need of K-prototype algorithm. Therefore, such a review provides an analysis of the E-Satisfaction, and of the behavior and loyalty of the customers in the ML-based paradigm. [ABSTRACT FROM AUTHOR]