Feature selection in customer churn analysis: Case study in B2B business

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dc.authorid0000-0002-4958-4575en_US
dc.authorscopusid55355863500en_US
dc.authorwosidGHQ-7349-2022en_US
dc.contributor.authorSancar, Semanur
dc.contributor.authorUzun-Per, Meryem
dc.date.accessioned2023-03-02T08:45:03Z
dc.date.available2023-03-02T08:45:03Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractCustomer churn analysis is one of the machine learning applications that has become a hot topic in businesses with the developing technology. Since the performance of fore-casting algorithms is directly affected by the abundance of data, the literature for B2C businesses has been developed faster. In B2B businesses, on the other hand, since customer dynamics are slightly different and the number of customers is not as high as in B2C, data mining studies have been carried out less frequently. Within the scope of BiletBank R&D studies, it is aimed to analyze the customer loss of BiletBank, a B2B company. In line with the customer loss analysis target, the categorical features of BiletBank customers, such as the city they are in, and their periodic interactions with the BiletBank system, have been converted into a data set. In this study, the evaluation of the features in the data set was carried out to create a source for customer loss analysis. Evaluation of features has been implemented by establishing nine different models, including statistical, wrapper, and embedded methods. It is aimed that the feature importance determined as a result of this study will be used in the customer churn analysis studies to be carried out from now on.en_US
dc.identifier.citationSancar, S., & Uzun-Per, M. (2022). Feature selection in customer churn analysis: Case study in B2B business. 2022 IEEE International Conference on e-Business Engineering (ICEBE) (pp. 264-270). Bournemouth: Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICEBE55470.2022.00053en_US
dc.identifier.doi10.1109/ICEBE55470.2022.00053en_US
dc.identifier.endpage270en_US
dc.identifier.isbn9781665492447
dc.identifier.scopus2-s2.0-85148612539en_US
dc.identifier.startpage264en_US
dc.identifier.urihttps://doi.org/10.1109/ICEBE55470.2022.00053
dc.identifier.urihttps://hdl.handle.net/20.500.13055/407
dc.indekslendigikaynakScopusen_US
dc.institutionauthorUzun-Per, Meryem
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 IEEE International Conference on e-Business Engineering (ICEBE)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBusiness-To-Businessen_US
dc.subjectCustomer Churn Analysisen_US
dc.subjectFeature Selectionen_US
dc.titleFeature selection in customer churn analysis: Case study in B2B businessen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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