Clicking Through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by Ecommerce Entities

Abstract

Abstract Success or failure of an ecommerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicates the choice of what features of a web system solves the conversion maximization problem. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental. In this research clickstream data from an online retailer was examined to provide answers to the previous questions with statistical modeling. A conceptual model was developed from which several hypotheses on the nature of clickstream relationships were posited. A discrete choice logit model was developed which showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited by visitors was associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but were in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is open to statistical modeling with interpretable and comprehensible models.