Data preparation for marketing database creation: A case study

Bibliographic Details
Main Author: Pinto, Filipe
Publication Date: 2005
Other Authors: Santos, Manuel Filipe, Cortez, Paulo, Quintela, Hélder
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/18442
Summary: To increase effectiveness in their marketing and CRM activities many organizations are adopting strategies of Database Marketing (DBM). DBM faces today new challenges in business knowledge. Currently DBM strategies are mainly approached by classical statistical inference, which may fail when complex, multi-dimensional, and incomplete data is available. An alternative is to use Knowledge Discovery from Databases (KDD), which aims at automatic pattern extraction using Data Mining (DM) techniques. The patterns identified can be applied to the efficient characterization of the customers and to the database filtering process. This paper focus the problems commonly encountered in the data pre-processing, necessary to the success of the DM in a DBM project, through a case study.
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spelling Data preparation for marketing database creation: A case studyTo increase effectiveness in their marketing and CRM activities many organizations are adopting strategies of Database Marketing (DBM). DBM faces today new challenges in business knowledge. Currently DBM strategies are mainly approached by classical statistical inference, which may fail when complex, multi-dimensional, and incomplete data is available. An alternative is to use Knowledge Discovery from Databases (KDD), which aims at automatic pattern extraction using Data Mining (DM) techniques. The patterns identified can be applied to the efficient characterization of the customers and to the database filtering process. This paper focus the problems commonly encountered in the data pre-processing, necessary to the success of the DM in a DBM project, through a case study.Ega - Empresa Gráfica Açoreana, Lda.Universidade do MinhoPinto, FilipeSantos, Manuel FilipeCortez, PauloQuintela, Hélder2005-042005-04-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/18442enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T05:34:44Zoai:repositorium.sdum.uminho.pt:1822/18442Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:22:50.550391Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Data preparation for marketing database creation: A case study
title Data preparation for marketing database creation: A case study
spellingShingle Data preparation for marketing database creation: A case study
Pinto, Filipe
title_short Data preparation for marketing database creation: A case study
title_full Data preparation for marketing database creation: A case study
title_fullStr Data preparation for marketing database creation: A case study
title_full_unstemmed Data preparation for marketing database creation: A case study
title_sort Data preparation for marketing database creation: A case study
author Pinto, Filipe
author_facet Pinto, Filipe
Santos, Manuel Filipe
Cortez, Paulo
Quintela, Hélder
author_role author
author2 Santos, Manuel Filipe
Cortez, Paulo
Quintela, Hélder
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Pinto, Filipe
Santos, Manuel Filipe
Cortez, Paulo
Quintela, Hélder
description To increase effectiveness in their marketing and CRM activities many organizations are adopting strategies of Database Marketing (DBM). DBM faces today new challenges in business knowledge. Currently DBM strategies are mainly approached by classical statistical inference, which may fail when complex, multi-dimensional, and incomplete data is available. An alternative is to use Knowledge Discovery from Databases (KDD), which aims at automatic pattern extraction using Data Mining (DM) techniques. The patterns identified can be applied to the efficient characterization of the customers and to the database filtering process. This paper focus the problems commonly encountered in the data pre-processing, necessary to the success of the DM in a DBM project, through a case study.
publishDate 2005
dc.date.none.fl_str_mv 2005-04
2005-04-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/18442
url http://hdl.handle.net/1822/18442
dc.language.iso.fl_str_mv eng
language eng
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dc.publisher.none.fl_str_mv Ega - Empresa Gráfica Açoreana, Lda.
publisher.none.fl_str_mv Ega - Empresa Gráfica Açoreana, Lda.
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