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

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

Editors: Kohavi, R., Masand, B.M., Spiliopoulou, M., Srivastava, J. (Eds.)

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About this book

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.

Table of contents (7 chapters)

  • Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences

    Berendt, Bettina

    Pages 1-24

  • A Customer Purchase Incidence Model Applied to Recommender Services

    Geyer-Schulz, Andreas (et al.)

    Pages 25-47

  • A Cube Model and Cluster Analysis for Web Access Sessions

    Huang, Joshua Zhexue (et al.)

    Pages 48-67

  • Exploiting Web Log Mining for Web Cache Enhancement

    Nanopoulos, Alexandros (et al.)

    Pages 68-87

  • LOGML: Log Markup Language for Web Usage Mining

    Punin, John R. (et al.)

    Pages 88-112

Buy this book

eBook 51,16 €
price for Spain (gross)
  • ISBN 978-3-540-45640-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 62,39 €
price for Spain (gross)
  • ISBN 978-3-540-43969-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points
Book Subtitle
Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers
Editors
  • Ron Kohavi
  • Brij M. Masand
  • Myra Spiliopoulou
  • Jaideep Srivastava
Series Title
Lecture Notes in Artificial Intelligence
Series Volume
2356
Copyright
2002
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-45640-7
DOI
10.1007/3-540-45640-6
Softcover ISBN
978-3-540-43969-1
Edition Number
1
Number of Pages
XI, 166
Topics