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

1. Front Matter

   Pages I-XI

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2. Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences

   • Bettina Berendt

   Pages 1-24

3. A Customer Purchase Incidence Model Applied to Recommender Services

   • Andreas Geyer-Schulz, Michael Hahsler, Maximillian Jahn

   Pages 25-47

4. A Cube Model and Cluster Analysis for Web Access Sessions

   • Joshua Zhexue Huang, Michael Ng, Wai-Ki Ching, Joe Ng, David Cheung

   Pages 48-67

5. Exploiting Web Log Mining for Web Cache Enhancement

   • Alexandros Nanopoulos, Dimitrios Katsaros, Yannis Manolopoulos

   Pages 68-87

6. LOGML: Log Markup Language for Web Usage Mining

   • John R. Punin, Mukkai S. Krishnamoorthy, Mohammed J. Zaki

   Pages 88-112

7. A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking

   • Cyrus Shahabi, Farnoush Banaei-Kashani

   Pages 113-144

8. Mining Indirect Associations in Web Data

   • Pang-Ning Tan, Vipin Kumar

   Pages 145-166

9. Back Matter

   Pages 167-167

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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

• Blue Martini Software, San Mateo, USA

  Ron Kohavi

• Data Miners Inc., Boston, USA

  Brij M. Masand

• Leipzig Graduate School of Management, Leipzig, Germany

  Myra Spiliopoulou

• University of Minnesota, Minneapolis

  Jaideep Srivastava

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