Analysis of Different Predicting Model for Online Shoppers’ Purchase Intention from Empirical Data

Abstract

The recent outburst of online shopping, commonly known as e-shopping, has added a new dimension in the business sector. In these days, people tend to explore online for finding the items they need and buy through online transaction. It has made their life more easy and comfortable. At the same time, it has become a great need for sellers to know the patterns and intentions of different types of online customers. The customer’s purchase intention can be predicted by analyzing the history of the customers. In this study, we have analyzed the empirical data of online shoppers for building a better prediction model to predict their purchase intention. We have analyzed different classification algorithm such as Decision Tree, Random Forest, Naive Bayes, SVM to predict whether a customer, visiting the webpages of an online shop, will end up with a purchase or not. We have also performed some ensemble methods to boost up the performance of these algorithms. Our study has shown that Random Forest is most suited to predict the customer’s purchase intention. Moreover, if we choose to do gradient boosting using this algorithm, it can predict with the highest accuracy, which is 90.34%. Considering the application of ensemble methods to this dataset makes this study unique.