Inter-Purchase Time Model for Associated Products

Abstract

The continued rise of e-commerce is the main driver of the rapid growth of global online purchase. Consumers can nearly buy everything they want at one occasion through online shopping. The purchase behavior models which focus on single product category are insufficient to describe online shopping behavior. Therefore, analysis of multi-category purchase gets more and more popular. For example, market basket analysis explores customers' buying tendency of the association between product categories. The information derived from market basket analysis facilitates to make cross-selling strategies and product recommendation system. However, traditional market basket analysis overlook the possibility that consumers postpone the purchase of associated or captive products. Therefore, firms would not only recommend the right products to consumers, but also recommend them at the right time which matches purchase cycles of the associated products. This study considers purchase occasions of multi-category products and builds an inter-purchase time model for associated products. We classify inter-purchase time behaviors of multi-category products into nine types, and use a mixture regression model to integrate those behaviors under our assumptions of purchase sequences. Our sample data is from Comscore which is a panelist-label database that captures detailed browsing and buying behavior of internet users across the United States. Finding the interpurchase time from books to movie is shorter than the inter-purchase time from movies to books. According to the model analysis and empirical results, this research finally propose the applications and recommendations in the management.