Choice and the Internet: From Clickstream to Research Stream

Abstract

The authors discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data (the electronic records of Internet usage recorded by company web servers and syndicated data services). The authors compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data), highlighting the differences relevant to choice modelers. Though the application of choice models to clickstream data is relatively new, the authors review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The paper offers directions for further research in these areas and discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation. Notwithstanding the numerous challenges associated with clickstream data research, the authors conclude that the detailed nature of the information tracked about Internet usage and e-commerce transactions presents an enormous opportunity for empirical modelers to enhance the understanding and prediction of choice behavior.

Goal attainment on long tail web sites: An information foraging approach

“…When a user visits a Web site, his or her main objective for visiting has been traditionally classified as either browsing or purchasing [8]. A browsing objective reflects how a visitor may navigate within a site [9], across multiple sites [31], or how site visits evolve over time [26].…”

Consumers' decision-making process and their online shopping behavior: a clickstream analysis

“…Clickstream can be defined as the path a consumer takes trough one or more websites (Bucklin et al, 2002). It can include within-site information such as the pages visited, time spent on each page and between-site information such as the websites visited.…”

Traffic-based feedback on the web

“…But it would also be valuable to look at more extensive representations of user behavior, by tracking longer user paths through the site; this offers the chance to make richer inferences about both group and individual user intentions (10)(11)(12)(17)(18)(19)(20), although it becomes correspondingly harder to interpret the usage data. Ultimately, by considering an increasing level of detail in the dynamics of traffic at an active web site, we can hope to achieve more detailed insight into the collective behavior of the crowds that congregate there.…”