A Purchasing Prediction Model Considering the Time Consumers Spend on a Site and Consumers Characteristics (Second Report)

With the proliferation of the Internet, retailers are obtaining large amounts of big data such as access logs and customer attributes from their daily online interactions with customers. By using those data, retailers can understand the characteristics of the customers who visit their sites, and can tailor their marketing strategies accordingly. Specifically, by building a purchase prediction model, a model that predicts which customers will visit a site and make a purchase and which will not, it is possible to understand what factors are influencing customer purchases. Traditionally, one such model has been built using data such as POS data and customer attributes, focusing only on the resulting purchases by customers. However, since those models fail to take into account the process by which the customer makes the purchase, they are unable to understand what the customer was thinking when he or she made the purchase. In e-commerce, which is a transaction via the Internet, it is possible to obtain data on the process of a customer's purchase, such as how much time the customer spent on what product, what products the customer browsed before making a purchase, etc. By using these features in the model, it will be possible to gain a more precise understanding of the factors influencing the customer's purchase. The purpose of this study is to construct a purchase prediction model that incorporates variables that indicate the time spent on the site by customers, the time spent browsing products, and the bias of the time spent on the products browsed by customers, and to obtain the contribution of the features to the prediction results to help formulate marketing strategies.