Investigating the Role of Multiple Channels in Predicting Website Browsing Patterns and Purchase

In today's online environment, consumers and sellers interact through multiple channels such as email, search engines, banner ads, affiliate websites and comparison-shopping websites. In this paper, we investigate whether knowing what channels a consumer used on their path until a point of time is predictive of their future browsing patterns and purchase conversion on the website. We propose a model in which the browsing patterns and conversion are stochastically dependent on the sequence of channels the consumer has used up to this point. Important features of our model are that we allow for clustered visits by consumers, which enables separation of their browsing patterns into intra- and inter-session components, and that channels can be evaluated using multiple metrics such as visit frequency and conversion. We estimate the model on a dataset obtained from an online retailer selling a durable good and find that information on both the identities and the frequencies of the channels through which consumers visited has significant predictive power for future conversion behavior. We find that channels may act as “closers” or “engagers” — consumers arriving through the former are more likely to make a purchase, while consumers arriving through the latter, even if they do not make a purchase, are more likely to visit again in the future or extend the current session. We discuss several managerial implications of the model including model based visualizations, landing page design, multi-dimensional propensity scoring, and the use of the model to evaluate a policy change regarding the encryption of channel information