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A Clickstream is the recording of what a computer user clicks on while Web browsing or using another software application. As the user clicks anywhere in the webpage or application, the action is logged on a client or inside the Web server, as well as possibly the Web browser, routers, proxy servers, and ad servers. Clickstream analysis is useful for Web activity analysis[1], software testing, market research, and for analyzing employee productivity.

A small observation on the evolution of clickstream tracking: Initial clickstream or click path data had to be gleaned from server log files. Because human and machine traffic were not differentiated, the study of human clicks took a substantial effort. Subsequently javascript technologies were developed which use a tracking cookie to generate a series of signals from browsers. In other words, information was only collected from "real humans" clicking on sites through browsers.

A clickstream is a series of page requests, every page requested generates a signal. These signals can be graphically represented for clickstream reporting. The main point of clickstream tracking is to give webmasters insight into what visitors on their site are doing.

This data itself is "neutral" in the sense that any dataset is neutral. The data can be used in various scenarios, one of which is marketing. Additionally, any webmaster, researcher, blogger or person with a website can learn about how to improve their site.

Use of clickstream data can raise privacy concerns, especially since some Internet service providers have resorted to selling users' clickstream data as a way to enhance revenue. There are 10-12 companies that purchase this data, typically for about $0.40/month per user.[1] While this practice may not directly identify individual users, it is often possible to indirectly identify specific users, an example being the AOL search data scandal. Most consumers are unaware of this practice, and its potential for compromising their privacy. In addition, few ISPs publicly admit to this practice.[2]

Since the business world is quickly evolving into a state of e-commerce, analyzing the data of clients that visit a company website is becoming a necessity in order to remain competitive. This analysis can be used to generate two findings for the company, the first being an analysis of a user’s clickstream while using a website to reveal usage patterns, which in turn gives a heightened understanding of customer behaviour. This use of the analysis creates a user profile that aids in understanding the types of people that visit a company’s website. As discussed in Van den Poel & Buckinx (2005), clickstream analysis can be used to predict whether a customer is likely to purchase from an e-commerce website. Clickstream analysis can also be used to improve customer satisfaction with the website and with the company itself. Both of these uses generate a huge business advantage. It can also be used to assess the effectiveness of advertising on a web page or site.[2]

With the growing corporate knowledge of the importance of clickstreams, the way that they are being monitored and used to build Business Intelligence is evolving. Data mining [3], column-oriented DBMS, and integrated OLAP systems are being used in conjunction with clickstreams to better record and analyze this data.

Clickstreams can also be used to allow the user to see where they have been and allow them to easily return to a page they have already visited, a function that is already incorporated in most browsers.

Unauthorized clickstream data collection is considered to be spyware. However, authorized clickstream data collection comes from organizations that use opt-in panels to generate market research using panelists who agree to share their clickstream data with other companies by downloading and installing specialized clickstream collection agents.


  1. WW Moe, PS Fader (2004),“Capturing Evolving Visit Behavior in Clickstream DataJournal of Interactive Marketing (2004)
  2. Patrali Chatterjee, Donna L. Hoffman and Thomas P. Novak (2003),“Modeling the Clickstream: Implications for Web-Based Advertising Efforts”, Marketing Science22(4), (Autumn, 2003), 520-541
  3. Olfa Nasraoui, Cesar Cardona, Carlos Rojas, Fabio Gonzalez (2003),“Mining Evolving User Profiles in NoisyWeb Clickstream Data with a Scalable Immune System Clustering AlgorithmProc. of KDD Workshop on Web mining as a Premise to... (2003)

See also


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