A Model of Web Site Browsing Behavior Estimated on Clickstream Data

TLDR

The authors aim to develop and estimate a model of web‑site browsing behavior that captures visitors’ decisions to continue browsing or exit and the time spent on each page. They fit a type‑II Tobit model to clickstream data from 5,000 visitors to an automotive company’s site, using server log files to estimate individual browsing decisions. The model shows that the likelihood of continuing to browse varies with site depth and repeat visits—repeat visits lower page‑view propensities but not durations—and that aggregate metrics differ markedly from individual‑level results, implying the need to account for heterogeneity.

Abstract

Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor's decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors' propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with “within-site lock-in” or site “stickiness” and with learning that carries over repeat visits. In particular, repeat visits lead to reduced page-view propensities but not to reduced page-view durations. The results also reveal browsing patterns that may reflect visitors' time-saving strategies. Finally, the authors report that simple site metrics computed at the aggregate level diverge substantially from individual-level modeling results, which indicates the need for Web site analyses to control for cross-sectional heterogeneity.