Author(s)

Yulin Cai^1*, Fengqing Chen¹, Jingyang Zhang²

¹Department of Mathematics, Hanshan Normal University, Chaozhou, China.
²School of Business, Macau University of Science and Technology, Taipa, Macau, China.

With the rapid growth of e-commerce and online transactions, e-commerce platforms face a critical challenge: predicting consumer behavior after purchase. This study aimed to forecast such after-sales behavior within the digital retail environment. We utilized four machine learning models: logistic regression, decision tree, random forest, and XGBoost, employing SMOTE oversampling and class weighting techniques to address class imbalance. To bolster the models’ predictive capabilities, we executed pivotal data processing steps, including feature derivation and one-hot encoding. Upon rigorous evaluation of the models’ performance through the 5-fold cross-validation method, the random forest model was identified as the superior performer, excelling in accuracy, F1 score, and AUC value, and was thus deemed the most effective model for anticipating consumer after-sales behavior. The findings from this research offer actionable strategies for e-commerce platforms to refine their after-sales services and enhance customer satisfaction.

E-Commerce, Consumer Behavior, Machine Learning Algorithms, Random Forest

Cai, Y. , Chen, F. and Zhang, J. (2024) Prediction of After-Sales Behavior in E-Commerce Using Machine Learning Models. Open Journal of Statistics, 14, 757-774. doi: 10.4236/ojs.2024.146035.