Analysis and prediction of e-customers' behavior by mining clickstream data

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dc.authorid0000-0001-8863-8348
dc.contributor.authorSilahtaroğlu, Gökhan
dc.contributor.authorDönertaşlı, Hale
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T20:02:39Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T20:02:39Z
dc.date.issued2015
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.descriptionIEEE International Conference on Big Data -- OCT 29-November 01, 2015 -- Santa Clara, California
dc.descriptionWOS: 000380404600176
dc.description.abstractIn a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion.
dc.description.sponsorshipIEEE, IEEE Comp Soc, Natl Sci Fdn, CCF, HUAWEI, Springer, ELSEVIER, CISCO, Intelen_US
dc.identifier.citationSilahtaroğlu, G. ve Dönertaşlı, H. (2015). Analysis and prediction of e-customers' behavior by mining clickstream data. IEEE International Conference on Big Data içinde (1466-1472. ss.). Santa Clara, California, November 01, 2015. https://doi.org/10.1109/BigData.2015.7363908
dc.identifier.endpage1472
dc.identifier.isbn978-1-4799-9925-5
dc.identifier.scopusqualityN/A
dc.identifier.startpage1466
dc.identifier.urihttps://hdl.handle.net/20.500.12511/3702
dc.identifier.urihttps://doi.org/10.1109/BigData.2015.7363908
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofIEEE International Conference on Big Dataen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectData Mining
dc.subjectClickstream
dc.subjectE- Customer
dc.subjectCustomer Behavior
dc.subjectDigital Market
dc.subjectPrediction
dc.titleAnalysis and prediction of e-customers' behavior by mining clickstream data
dc.typeConference Object

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