An analysis of search engine switching behavior using click streams

In this paper, we propose a simple framework to characterize the switching behavior between search engines based on user click stream data. We cluster users into a number of categories based on their search engine usage pattern during two adjacent time periods and construct the transition probability matrix across these usage categories. The principal eigenvector of the transposed transition probability matrix represents the limiting probabilities, which are proportions of users in each usage category at steady state. We experiment with this framework using real click stream data focusing on two search engines: one with a large market share and another with a small market share. The results offer interesting insights into search engine switching. The limiting probabilities provide empirical evidence that small engines can still retain its fair share of users over time.