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

Third International Workshop San Francisco, CA, USA, August 26, 2001 Revised Papers

  • Ron Kohavi
  • Brij M. Masand
  • Myra Spiliopoulou
  • Jaideep Srivastava
Conference proceedings WebKDD 2001

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2356)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2356)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Andreas Geyer-Schulz, Michael Hahsler, Maximillian Jahn
    Pages 25-47
  3. Joshua Zhexue Huang, Michael Ng, Wai-Ki Ching, Joe Ng, David Cheung
    Pages 48-67
  4. Alexandros Nanopoulos, Dimitrios Katsaros, Yannis Manolopoulos
    Pages 68-87
  5. John R. Punin, Mukkai S. Krishnamoorthy, Mohammed J. Zaki
    Pages 88-112
  6. Pang-Ning Tan, Vipin Kumar
    Pages 145-166
  7. Back Matter
    Pages 167-167

About these proceedings


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.


Association Rule Mining Cisco Cluster Analysis Customer Management Data Mining E-Commerce Internet Data Mining Navigation Patterns Recommender Services Web Site Personalization Web Usage Analysis Web User Profiling modeling recommender system

Editors and affiliations

  • Ron Kohavi
    • 1
  • Brij M. Masand
    • 2
  • Myra Spiliopoulou
    • 3
  • Jaideep Srivastava
    • 4
  1. 1.Blue Martini SoftwareSan MateoUSA
  2. 2.Data Miners Inc.BostonUSA
  3. 3.Leipzig Graduate School of ManagementLeipzigGermany
  4. 4.University of MinnesotaMinneapolis

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-43969-1
  • Online ISBN 978-3-540-45640-7
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
Industry Sectors
IT & Software