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dc.contributor.authorGarcía, David L.
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.authorNebot Castells, M. Àngela
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2016-04-26T09:44:49Z
dc.date.available2016-04-26T09:44:49Z
dc.date.issued2007-01
dc.identifier.citationGarcía, D., Vellido, A., Nebot, M. "Predictive models in churn data mining: a review". 2007.
dc.identifier.urihttp://hdl.handle.net/2117/86182
dc.description.abstractThe development of predictive models of customer abandonment plays a central role in any churn management strategy. These models can be developed using either qualitative approaches or can take a data-centred point of view. In the latter case, the use of Data Mining procedures and techniques can provide useful and actionable insights into the processes leading to abandonment. In this report, we provide a brief and structured review of some of the Data Mining approaches that have been put forward in recent academic literature for customer abandonment prediction.
dc.format.extent12 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-07-4-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherChurn
dc.subject.otherData mining
dc.subject.otherPredictive models
dc.titlePredictive models in churn data mining: a review
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.rights.accessOpen Access
drac.iddocument1841879
dc.description.versionPostprint (published version)
upcommons.citation.authorGarcía, D., Vellido, A., Nebot, M.
upcommons.citation.publishedtrue


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