DC FieldValueLanguage
dc.contributor.authorPapakyriakopoulos, Dimitrios-
dc.contributor.authorGriva, Anastasia-
dc.contributor.authorZampou, Eleni-
dc.contributor.authorStavrou, Vasilis-
dc.contributor.authorDoukidis, Georgios-
dc.date.accessioned2024-04-19T09:37:56Z-
dc.date.available2024-04-19T09:37:56Z-
dc.date.issued2024-01-01-
dc.identifierscopus-85143537724-
dc.identifier.issn21167052-
dc.identifier.issn12460125-
dc.identifier.other85143537724-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2151-
dc.description.abstractCustomer segmentation is considered the cornerstone for personalisation, target advertising, and promotion assisting both researchers and practitioners to enhance customers’ buying behaviour understanding. Pertinent literature mainly exploits one distinct segmentation type such as behavioural to segment customers solely under one lens. We develop a two-stage business analytics approach that introduces a combination of geographic and behavioural customer segmentation. Our approach is based on data mining and machine learning techniques. We evaluate the suggested approach using e-commerce home delivery data. First, we segment customers based on the products ordered to identify behavioural customer segments with similar product preferences. Then, we perform geographic segmentation. By applying the approach developed we also identify challenges that affect the segmentation process and results. The suggested approach can serve as a guide to business analysts to understand which are the steps that they should perform when analysing similar datasets. Whereas its results may assist third-party logistics (3PL) companies, retailers, and brands in supporting decision making.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Decision Systemsen_US
dc.subjectBusiness analyticsen_US
dc.subjectCustomer segmentationen_US
dc.subjectData miningen_US
dc.subjectE-commerceen_US
dc.subjectGeographic segmentationen_US
dc.subjectHome deliveryen_US
dc.titleA two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/12460125.2022.2151071en_US
dc.identifier.scopus2-s2.0-85143537724-
dcterms.accessRights1en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume33en_US
dc.relation.issue1en_US
dc.identifier.spage1en_US
dc.identifier.epage29en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldEngineering and Technologyen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0001-7033-1890-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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