A Markovian mixture model for Web data

In this paper we carry out a site-centric clickstream analysis by fitting a probabilistic model to the click sequences of surfers browsing an e-commerce Web site. In particular, surfers’ paths are modeled as observations originating from a finite mixture of Markov chains which takes values in the site’s page-space. We preliminarily tackle the problem of making inference on the number of distinct visits in which each surfers’ sequence of clicks can be divided. We then deal with goodness-of-fit testing of the model to real data, exploring the heterogeneity of surfers. Finally, by using a simple model with only two components, we shed light on the relationship between surfing behavior and tendency to on-line purchasing.