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020 _a9783540456407
_9978-3-540-45640-7
024 7 _a10.1007/3-540-45640-6
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082 0 4 _a004
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245 1 0 _aWEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points
_h[electronic resource] :
_bThird International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers /
_cedited by Ron Kohavi, Brij M. Masand, Myra Spiliopoulou, Jaideep Srivastava.
250 _a1st ed. 2002.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2002.
300 _aXI, 166 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2356
505 0 _aDetail 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.
520 _aWorkshopTheme 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.
650 0 _aComputer science.
650 0 _aData structures (Computer science).
650 0 _aInformation theory.
650 0 _aComputer simulation.
650 0 _aArtificial intelligence.
650 0 _aDatabase management.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aComputer Science.
650 2 4 _aData Structures and Information Theory.
650 2 4 _aComputer Modelling.
650 2 4 _aArtificial Intelligence.
650 2 4 _aDatabase Management.
650 2 4 _aInformation Storage and Retrieval.
700 1 _aKohavi, Ron.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMasand, Brij M.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSpiliopoulou, Myra.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSrivastava, Jaideep.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540439691
776 0 8 _iPrinted edition:
_z9783662177334
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2356
856 4 0 _uhttps://doi.org/10.1007/3-540-45640-6
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