Exploration of customer churn routes using machine learning probabilistic models

The 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 s...

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Detalles Bibliográficos
Autor: Garcia Gomez, David
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:CBUC, CESCA
Repositorio:TDR. Tesis Doctorales en Red
OAI Identifier:oai:www.tdx.cat:10803/144660
Acceso en línea:http://hdl.handle.net/10803/144660
https://dx.doi.org/10.5821/dissertation-2117-95309
Access Level:acceso abierto
Palabra clave:004
65
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spelling Exploration of customer churn routes using machine learning probabilistic modelsGarcia Gomez, David00465The 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.DOCTORAT EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 1998)Universitat Politècnica de CatalunyaGavaldà Mestre, RicardUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics201420142014info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion159 p.application/pdfapplication/pdfhttp://hdl.handle.net/10803/144660https://dx.doi.org/10.5821/dissertation-2117-95309TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésL'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/http://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/1446602026-06-14T12:46:07Z
dc.title.none.fl_str_mv Exploration of customer churn routes using machine learning probabilistic models
title Exploration of customer churn routes using machine learning probabilistic models
spellingShingle Exploration of customer churn routes using machine learning probabilistic models
Garcia Gomez, David
004
65
title_short Exploration of customer churn routes using machine learning probabilistic models
title_full Exploration of customer churn routes using machine learning probabilistic models
title_fullStr Exploration of customer churn routes using machine learning probabilistic models
title_full_unstemmed Exploration of customer churn routes using machine learning probabilistic models
title_sort Exploration of customer churn routes using machine learning probabilistic models
dc.creator.none.fl_str_mv Garcia Gomez, David
author Garcia Gomez, David
author_facet Garcia Gomez, David
author_role author
dc.contributor.none.fl_str_mv Gavaldà Mestre, Ricard
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.subject.none.fl_str_mv 004
65
topic 004
65
description The 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.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014
2014
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10803/144660
https://dx.doi.org/10.5821/dissertation-2117-95309
url http://hdl.handle.net/10803/144660
https://dx.doi.org/10.5821/dissertation-2117-95309
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 159 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
reponame:TDR. Tesis Doctorales en Red
instname:CBUC, CESCA
instname_str CBUC, CESCA
reponame_str TDR. Tesis Doctorales en Red
collection TDR. Tesis Doctorales en Red
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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