DC FieldValueLanguage
dc.contributor.authorDrivas, Ioannis-
dc.contributor.authorGiannakopoulos, Georgios-
dc.contributor.authorSakas, Damianos-
dc.date.accessioned2023-10-13T19:49:02Z-
dc.date.available2023-10-13T19:49:02Z-
dc.date.issued2021-02-01-
dc.identifier.isbn9783030570644-
dc.identifier.issn21987254-
dc.identifier.issn21987246-
dc.identifier.other85126209508-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/329-
dc.description.abstractDisplay advertising constitutes one of the most efficient digital marketing strategies for the development of organizations’ brand awareness. Proper targeting of display ads campaigns potentially leads to the improvement of web users’ consideration and engagement about products and services that organizations offer through their websites. As prior studies indicate, this kind of consideration and engagement, which resulted through display ads, leads web users to type the name of the brand in search engines. The submitted search terms that contain the brand name of the organizations are called branded keywords, and the traffic that comes from them as branded search traffic. In this paper, the authors propose a computational data-driven methodology for the estimation and prediction of display advertising effectiveness in terms of optimizing brand popularity in search engines. One step further, preliminary research efforts of the authors indicate that branded search traffic visitors show higher interaction with the content of the websites regarding the time they spend and the number of pageviews they are browsing. In this respect, if display advertising campaigns increase the number of branded keywords and hence, the volume of branded search traffic, then this raises opportunities to optimize users’ engagement inside websites. Against this research gap, the authors proceed into a data-driven methodological process that is expanded in three major stages. In the first stage, the web mining process of extracting several web behavioral analytics metrics takes place for 125 continuous days at 7 courseware websites. At the second stage, analysis and interpretation of possible intercorrelations between the web analytics metrics take place with the purpose to integrate a computational model that relies on web behavioral data harvesting and their statistical analysis. Subsequently, in the third stage, the authors develop a data-driven computational model based on the agent-based modeling approach for estimating and predicting the optimal interaction rates of branded search traffic visitors of the examined websites. The results of the study constitute a practical toolbox for digital marketing practitioners in order to understand their display advertising effectiveness in terms of brand popularity and branded search traffic improvement for their websites.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofBusiness Intelligence and Modellingen_US
dc.relation.ispartofseriesSpringer Proceedings in Business and Economicsen_US
dc.subjectAgent-based models in marketingen_US
dc.subjectBrand awarenessen_US
dc.subjectBranded keywordsen_US
dc.subjectBranded search trafficen_US
dc.subjectData-driven marketingen_US
dc.subjectDisplay advertisingen_US
dc.subjectWeb analyticsen_US
dc.subjectWebsites trafficen_US
dc.titleDisplay Advertising and Brand Awareness in Search Engines: Predicting the Engagement of Branded Search Traffic Visitorsen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Business Intelligence & Modellingen_US
dc.identifier.doi10.1007/978-3-030-57065-1_1en_US
dc.identifier.scopus2-s2.0-85126209508-
dc.relation.deptDepartment of Archival, Library and Information Studiesen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage3en_US
dc.identifier.epage15en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldSocial Sciencesen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0003-2407-9502-
crisitem.author.orcid0000-0002-1659-3504-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
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