Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data

Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.

Automated summary

This paper aims to develop a data-driven method to achieve accurate purchase forecasting and analyze the influence of behavior variables on online tourism purchases. Based on real-world data, the proposed method successfully predicts purchases in online tourism using machine learning algorithms, contributing significantly to the forecasting literature and practical implications.