Research on Predicting Customers' Next Purchase Based on Shopping Basket Data

Abstract

Thanks to the rapid development of e-commerce and online shopping, a large amount of shopping basket data has been accumulated. How to mine the useful information in shopping cart data to predict customers' next purchase is an important problem in commercial data analysis, which is widely used in the fields of online advertising and product recommendation. In this paper, three prediction methods are proposed, including frequency-based prediction, rule-based prediction and similarity-based prediction. Moreover, evaluations and analysis of these three methods are conducted on the public dataset. It is found that the items that were frequently purchased by consumers in the past are more likely to continue to be purchased due of the higher prediction accuracy of the frequency-based methods. On the other hand, similarity-based item prediction methods also yielded good results because there is also a significant overlap in the items that similar users want to purchase. Therefore, it is concluded that in practical applications, frequency and similarity-based prediction methods can be applied to predict consumers' next purchases.