Abstract
In this paper, we propose a simple framework to characterize the switching behavior between search engines based on click streams. We segment users into a number of categories based on their search engine usage 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 click streams focusing on two search engines: one with a large market share and the other 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
