 Tree-based methods for analytics of online shoppers' purchasing intentionsLu Xiong, Xi Chen, Jingsai Liang, Xingtong Cao, Pengyu Zhu and Mingyuan Zhao International Journal of Data Science, 2024, vol. 9, issue 2, 99-122 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. Keywords: online shopping data analytics; feature engineering; decision tree; random forest; SGB; stochastic gradient boosting; XGBoost; feature importance; sensitivity analysis. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:9:y:2024:i:2:p:99-122 Access Statistics for this article More articles in International Journal of Data Science from Inderscience Enterprises Ltd |
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