Ebook: WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points: Third International Workshop San Francisco, CA, USA, August 26, 2001 Revised Papers

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.

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This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Mining Web Data, WEBKDD 2001 held in San Francisco, CA, USA in August 2001.
The seven revised full papers went through two rounds of reviewing an improvement. The book addresses key issues in mining Web log data for e-commerce. The papers are devoted to predicting user access, recommender systems and access modeling, and acquiring and modeling data and patterns.

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This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Mining Web Data, WEBKDD 2001 held in San Francisco, CA, USA in August 2001.
The seven revised full papers went through two rounds of reviewing an improvement. The book addresses key issues in mining Web log data for e-commerce. The papers are devoted to predicting user access, recommender systems and access modeling, and acquiring and modeling data and patterns.
Content:
Front Matter....Pages I-XI
Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences....Pages 1-24
A Customer Purchase Incidence Model Applied to Recommender Services....Pages 25-47
A Cube Model and Cluster Analysis for Web Access Sessions....Pages 48-67
Exploiting Web Log Mining for Web Cache Enhancement....Pages 68-87
LOGML: Log Markup Language for Web Usage Mining....Pages 88-112
A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking....Pages 113-144
Mining Indirect Associations in Web Data....Pages 145-166
Back Matter....Pages 167-167

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This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Mining Web Data, WEBKDD 2001 held in San Francisco, CA, USA in August 2001.
The seven revised full papers went through two rounds of reviewing an improvement. The book addresses key issues in mining Web log data for e-commerce. The papers are devoted to predicting user access, recommender systems and access modeling, and acquiring and modeling data and patterns.
Content:
Front Matter....Pages I-XI
Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences....Pages 1-24
A Customer Purchase Incidence Model Applied to Recommender Services....Pages 25-47
A Cube Model and Cluster Analysis for Web Access Sessions....Pages 48-67
Exploiting Web Log Mining for Web Cache Enhancement....Pages 68-87
LOGML: Log Markup Language for Web Usage Mining....Pages 88-112
A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking....Pages 113-144
Mining Indirect Associations in Web Data....Pages 145-166
Back Matter....Pages 167-167
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