An advanced intelligence system in customer online shopping behavior and satisfaction analysis

Abstract

Online shopping or internet shopping is increasing day by day. With the advancement of modern technology, the online market is growing in a vast way. People nowadays prefer online shopping because it saves time, energy, and money. Because of the blessing of the internet that online shopping has made its debut which also affects the common citizens for online shopping. So, for the emerging growth of the online market, it is necessary to find out the behavior of online shopping and customer satisfaction. Safety, trust, product quality plays an important role in customer satisfaction. In this study, we examined customer online shopping satisfaction its impact. The quality of the product, the price of the product compared to the local market, the policy of return, timely delivery of the product are also essential elements of online shopping. By analyzing all these factors we have tried to find the customer behavior and satisfaction with online shopping. In our study, we used a machine learning method to search for the result. We used 40 thousand data to find out the accuracy of our work and to analyze customer shopping satisfaction. We use Naïve Bayes, Apiorir, Decision Tree, and Random Forest classification algorithms for this analysis. We have got our best result by using Apiorir algorithm (88% accuracy) and Naïve Bayes algorithm (87% accuracy). We have also focused on customer behavior and interest in online shopping. Our study can help to develop the business intelligence and satisfaction enhancement about E-commerce.