IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations
navleft navright Research IRM Open Access IRMA Journals IRM Books Proceedings Membership

The IRMA Community

Calls for Papers
Online Symposium
Newsletters

Research IRM

Click a keyword to search titles using our InfoSci-OnDemand powered search:

Web Analytics with Fuzziness

Web Analytics with Fuzziness
View Sample PDF
Author(s): Darius Zumstein (University of Fribourg, Switzerland)
 Copyright: 2012
 Pages: 23
 Source title: Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications
Source Author(s)/Editor(s): Andreas Meier (University of Fribourg, Switzerland)and Laurent Donzé (University of Fribourg, Switzerland)
 DOI: 10.4018/978-1-4666-0095-9.ch009

Purchase

View Web Analytics with Fuzziness on the publisher's website for pricing and purchasing information.

Abstract

In the Internet economy and information society, it has become an essential task of electronic business to analyze, to monitor, and to optimize websites and Web offers. Therefore, this chapter addresses the issues of Web analytics, which is defined as the measurement, collection, analysis, and reporting of Internet data for the purposes of understanding and optimizing website usage. After a short introduction, the second section defines Web analytics, describes benefits and problems of Web analytics, as well as different software architectures and products. Third, a controlling loop is proposed for Web content and Web user controlling in order to analyze Key Performance Indicators (KPIs) and to take website- and e-business-related actions. Fourth, different Web metrics and KPIs of information, transaction and communication are defined. Fifth, a fuzzy Web analytics approach is proposed, which makes it possible to classify Web metrics precisely into more than one class at the same time. Considering real Web data of the Web metrics page views and bounce rate, it is shown that fuzzy classification allows exact and flexible segmentation of Web pages or other objects and gradual rankings within fuzzy sets. In addition, the fuzzy logic approach enables Computing with Words (CWW), i.e. the perception-based, linguistic consideration of Web data and Web metrics instead of measurement-based, numerical ones. Web usage mining with inductive fuzzy classification and Web Analytics with Words (WAW) allows intuitive, human-oriented analyses, description, and reporting of Web metrics values in natural language.

Related Content

Aynetu Terefe, Shashi Kant, Metasebia Adula, Tafese Niguse. © 2026. 26 pages.
Tanya, Nitin Pathak, Priyanka Chugh. © 2026. 32 pages.
Nitika Sharma, Paras Sarjolta. © 2026. 18 pages.
Manoj Govindaraj, Ravishankar Krishnan, L. Anitha, G. M. Shaju, Chandramowleeswaran Gnanasekaran, Jenifer Lawrence. © 2026. 30 pages.
Ravishankar Krishnan, Navaneetha Krishnan Rajagopal. © 2026. 28 pages.
Kriti Kishor, Sanjeev Kumar Bansal, Stefano Bresciani. © 2026. 14 pages.
Shashi Kant, Tamire Ashuro, Metasebia Adula. © 2026. 30 pages.
IRMA Offers Over 2,500 Full Text Open Access Research Papers for Free Download Click to Start Searching Free IRM Research!

IRMA Sponsors

Sponsor IGI Global

Sponsor eContentPro

Encyclopedia of Information Science and Technology, Fourth Edition
Body Bottom
About Us | Contact | Sitemap | Legal | Policies
Copyright ©2026, Information Resources Management Association. 701 East Chocolate Avenue, Hershey, PA 17033.