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dc.contributorGavaldà Mestre, Ricard
dc.contributor.authorGarcia Gomez, David
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2014-06-06T11:08:35Z
dc.date.available2014-06-06T11:08:35Z
dc.date.issued2014-04-10
dc.identifier.citationGarcia Gomez, D. Exploration of customer churn routes using machine learning probabilistic models. Tesi doctoral, UPC, Departament de Llenguatges i Sistemes Informàtics, 2014. DOI 10.5821/dissertation-2117-95309.
dc.identifier.urihttp://hdl.handle.net/2117/95309
dc.description.abstractThe ongoing processes of globalization and deregulation are changing the competitive framework in the majority of economic sectors. The appearance of new competitors and technologies entails a sharp increase in competition and a growing preoccupation among service providing companies with creating stronger bonds with customers. Many of these companies are shifting resources away from the goal of capturing new customers and are instead focusing on retaining existing ones. In this context, anticipating the customer¿s intention to abandon, a phenomenon also known as churn, and facilitating the launch of retention-focused actions represent clear elements of competitive advantage. Data mining, as applied to market surveyed information, can provide assistance to churn management processes. In this thesis, we mine real market data for churn analysis, placing a strong emphasis on the applicability and interpretability of the results. Statistical Machine Learning models for simultaneous data clustering and visualization lay the foundations for the analyses, which yield an interpretable segmentation of the surveyed markets. To achieve interpretability, much attention is paid to the intuitive visualization of the experimental results. Given that the modelling techniques under consideration are nonlinear in nature, this represents a non-trivial challenge. Newly developed techniques for data visualization in nonlinear latent models are presented. They are inspired in geographical representation methods and suited to both static and dynamic data representation.
dc.format.extent159 p.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsL'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc/3.0/es/
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/
dc.sourceTDX (Tesis Doctorals en Xarxa)
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.titleExploration of customer churn routes using machine learning probabilistic models
dc.typeDoctoral thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacInformàtica -- Matemàtica
dc.subject.lemacMineria de dades
dc.subject.lemacFidelitat a una marca
dc.subject.lemacFidelització
dc.identifier.doi10.5821/dissertation-2117-95309
dc.identifier.dlB 15988-2014
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.identifier.tdxhttp://hdl.handle.net/10803/144660


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