Modeling Purchase Behavior at an E-Commerce Web Site: A Task-Completion Approach

Abstract

The authors develop and estimate a model of online buying using clickstream data from a Web site that sells cars. The model predicts online buying by linking the purchase decision to what visitors do and to what they are exposed to while at the site. To overcome the challenges of predicting Internet buying, the authors decompose the purchase process into the completion of sequential nominal user tasks and account for heterogeneity across visitors at the county level. Using a sequence of binary probits, the authors model the visitor's decision of whether to complete each task for the first time, given that the visitor has completed the previous tasks at least once. The results indicate that visitors' browsing experiences and navigational behavior predict task completion for all decision levels. The results also indicate that the number of repeat visits per se is not diagnostic of buying propensity and that a site's offering of sophisticated decision aids does not guarantee increased conversion rates. The authors also compare the predictive performance of the task-completion approach with single-stage benchmark models in a holdout sample. The proposed approach provides superior prediction and better identifies likely buyers, especially early in the task sequence. The authors also discuss implications for Web site managers.