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Ontology-supported database marketing

2009-06Journal article Open access
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dc.contributor.authorPinto, Filipe Mota
dc.contributor.authorMarques, Alzira
dc.contributor.authorSantos, Manuel Filipe
dc.contributor.authorMota Pinto, Filipe
dc.contributor.authorMarques, Alzira
dc.date.accessioned2025-06-05T18:46:58Z
dc.date.available2025-06-05T18:46:58Z
dc.date.issued2009-06
dc.descriptionPalgrave Macmillan
dc.description.abstractDatabase marketing (DBM) provides in -depth analysis of marketing databases. Knowledge discovery in database techniques is one of the most prominent approaches to supporting some of the DBM process phases. However, in many cases, the benefits of these tools are not fully exploited by marketers. Complexity and amount of data constitute two major factors limiting the application of knowledge discovery techniques in marketing activities. Currently, ontologies may here play an important role in the marketing discipline. Motivated by its success in the area of artificial intelligence, we propose an ontology-supported DBM approach. The approach aims to enhance DBM with ontology by providing detailed step-phase specific information. Our research work has its foundations in a double methodological approach using the Delphi and Action Research methodologies. First, we use Delphi to structure related DBM knowledge, and then we align our work to the Action Research methodology in order to systematise the knowledge extraction process and knowledge base creation. The issues raised in this paper both respond and contribute to calls for a DBM process improvement. Our work was evaluated in the relationship marketing domain focusing on a relational marketing programme database. The findings of this study not only advance the state of DBM research, but also shed light on future research directions.eng
dc.identifier.citationPinto, F., Marques, A. & Santos, M. Ontology-supported database marketing. J Database Mark Cust Strategy Manag 16, 76–91 (2009). https://doi.org/10.1057/dbm.2009.9.
dc.identifier.doi10.1057/dbm.2009.9
dc.identifier.eissn1741-2447
dc.identifier.issn1741-2439
dc.identifier.urihttp://hdl.handle.net/10400.8/13144
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/article/10.1057/dbm.2009.9?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=scopus
dc.relation.ispartofJournal of Database Marketing & Customer Strategy Management
dc.rights.uriN/A
dc.subjectdatabase marketing
dc.subjectknowledge extraction
dc.subjectontologies
dc.subjectmarketing databases
dc.subjectknowledge base
dc.titleOntology-supported database marketingeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage91
oaire.citation.issue2
oaire.citation.startPage76
oaire.citation.titleJournal of Database Marketing and Customer Strategy Management
oaire.citation.volume16
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMota Pinto
person.familyNameMarques
person.givenNameFilipe
person.givenNameAlzira
person.identifier.ciencia-id1519-309E-BC47
person.identifier.orcid0000-0001-7634-0296
person.identifier.orcid0000-0001-6607-852X
relation.isAuthorOfPublication908ef8f4-823a-41fd-8184-287f872b943c
relation.isAuthorOfPublicationd558615c-d75f-42b4-8b6c-c160e48304cc
relation.isAuthorOfPublication.latestForDiscovery908ef8f4-823a-41fd-8184-287f872b943c

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Database marketing (DBM) provides in -depth analysis of marketing databases. Knowledge discovery in database techniques is one of the most prominent approaches to supporting some of the DBM process phases. However, in many cases, the benefits of these tools are not fully exploited by marketers. Complexity and amount of data constitute two major factors limiting the application of knowledge discovery techniques in marketing activities. Currently, ontologies may here play an important role in the marketing discipline. Motivated by its success in the area of artificial intelligence, we propose an ontology-supported DBM approach. The approach aims to enhance DBM with ontology by providing detailed step-phase specific information. Our research work has its foundations in a double methodological approach using the Delphi and Action Research methodologies. First, we use Delphi to structure related DBM knowledge, and then we align our work to the Action Research methodology in order to systematise the knowledge extraction process and knowledge base creation. The issues raised in this paper both respond and contribute to calls for a DBM process improvement. Our work was evaluated in the relationship marketing domain focusing on a relational marketing programme database. The findings of this study not only advance the state of DBM research, but also shed light on future research directions.
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