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

Kohavi, Ron

ISBN 10: 3540439692 / ISBN 13: 9783540439691

Editore: Springer 2002-07, 2002

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Codice articolo 6666-IUK-9783540439691

Dati bibliografici

• Titolo: WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points : Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised P
• Editore: Springer 2002-07
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 3540439692
• ISBN 13: 9783540439691
• Rilegatura: PF
• Condizione: New

Descrizione articolo

Riassunto

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.

Contenuti

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.