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A clickstream is the recording of the parts of the screen 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, router, proxy server or ad server. Clickstream analysis is useful for web activity analysis, software testing, market research, and for analyzing employee productivity.
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. 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.
Analyzing the data of clients that visit a company website can be important 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. This can generate a business advantage, and be used to assess the effectiveness of advertising on a web page or site.
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.
- WW Moe, PS Fader (2004),“Capturing Evolving Visit Behavior in Clickstream Data” Journal of Interactive Marketing (2004)
- Ting, I. H.; Kimble C; Kudenko. D (September 2005). "UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design." (PDF). IEEE/WIC/ACM International Conference on Web Intelligence: 179–185. doi:10.1109/WI.2005.153.
- "Compete CEO: ISPs Sell Clickstreams For $5 A Month". Seeking Alpha. 2007-03-13. Retrieved 2011-09-15.
- Singel, Ryan (2007-04-06). "Some ISPs Still Dodging Data Retention Requests, Help 27B Get the 411 AGAIN | Threat Level | Wired.com". Blog.wired.com. Retrieved 2011-09-15.
- 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
- Nasraoui, Olfa; Cardona, Cesar; Rojas, Carlos; Gonzalez, Fabio (2003). "Mining Evolving User Profiles in NoisyWeb Clickstream Data with a Scalable Immune System Clustering Algorithm". Proc. of KDD Workshop on Web mining as a Premise to…. CiteSeerX: 10
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