Tree-based methods for analytics of online shoppers' purchasing intentions Online publication date: Fri, 05-Jul-2024
by Lu Xiong; Xi Chen; Jingsai Liang; Xingtong Cao; Pengyu Zhu; Mingyuan Zhao
International Journal of Data Science (IJDS), Vol. 9, No. 2, 2024
Abstract: The recent speedy growth of e-commerce and big data has accumulated vast amounts of data about online shopping behaviour. Analysing this data can help online retailers gain competitive advantages. We propose four tree-based methods for analytics of online shoppers' purchasing intentions. After exploring data through various visualisation techniques, we conduct feature engineering to improve the model's accuracy. AUC is the primary measurement used to evaluate models. To make the conclusion more statistically robust, k-fold cross-validation is applied to obtain the statistics of AUCs, such as the average and standard deviation. By analysing the global and local feature importance of each model, the most critical predictor, PageValues is found. Furthermore, we do sensitivity analysis for PageValues concerning the target variable Revenue to examine the relationship. Our findings support the decision on how to improve sales. The interpretation of the models and the explanation of their business implications make this paper unique.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook