WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points -

WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points

Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers
Buch | Softcover
XI, 166 Seiten
2002 | 2002
Springer Berlin (Verlag)
978-3-540-43969-1 (ISBN)
53,49 inkl. MwSt
WorkshopTheme The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth of electronic commerce. In addition, customer interactions, including personalized content, e-mail c- paigns, and online feedback provide new channels of communication that were not previously available or were very ine?cient. The Web presents a key driving force in the rapid growth of electronic c- merceandanewchannelforcontentproviders.Knowledgeaboutthecustomeris fundamental for the establishment of viable e-commerce solutions. Rich web logs provide companies with data about their customers and prospective customers, allowing micro-segmentation and personalized interactions. Customer acqui- tion costs in the hundreds of dollars per customer are common, justifying heavy emphasis on correct targeting. Once customers are acquired, customer retention becomes the target. Retention through customer satisfaction and loyalty can be greatly improved by acquiring and exploiting knowledge about these customers and their needs. Althoughweblogsarethesourceforvaluableknowledgepatterns,oneshould keep in mind that the Web is only one of the interaction channels between a company and its customers. Data obtained from conventional channels provide invaluable knowledge on existing market segments, while mobile communication adds further customer groups. In response, companies are beginning to integrate multiple sources of data including web, wireless, call centers, and brick-a- mortar store data into a single data warehouse that provides a multifaceted view of their customers, their preferences, interests, and expectations.

Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences.- A Customer Purchase Incidence Model Applied to Recommender Services.- A Cube Model and Cluster Analysis for Web Access Sessions.- Exploiting Web Log Mining for Web Cache Enhancement.- LOGML: Log Markup Language for Web Usage Mining.- A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking.- Mining Indirect Associations in Web Data.

Erscheint lt. Verlag 19.7.2002
Reihe/Serie Lecture Notes in Artificial Intelligence
Lecture Notes in Computer Science
Zusatzinfo XI, 166 p.
Verlagsort Berlin
Sprache englisch
Maße 155 x 235 mm
Gewicht 268 g
Themenwelt Sachbuch/Ratgeber Natur / Technik Naturwissenschaft
Mathematik / Informatik Informatik Theorie / Studium
Schlagworte Association Rule Mining • Cisco • cluster analysis • Customer Management • Data Mining • E-Commerce • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Internet Data Mining • Modeling • Navigation Patterns • Recommender Services • Recommender System • Web Site Personalization • Web Usage Analysis • Web User Profiling
ISBN-10 3-540-43969-2 / 3540439692
ISBN-13 978-3-540-43969-1 / 9783540439691
Zustand Neuware
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
Die glänzenden und die dunklen Jahre der Physik 1895-1945

von Tobias Hürter

Buch | Softcover (2023)
Klett-Cotta (Verlag)
14,00
Kaleidoskop der Mathematik

von Ehrhard Behrends; Peter Gritzmann; Günter M. Ziegler

Buch | Hardcover (2024)
Springer (Verlag)
32,99
Die größte Geschichte aller Zeiten

von Josef M. Gaßner; Jörn Müller

Buch | Hardcover (2022)
S. Fischer (Verlag)
33,00