Optimising customer retention: An AI-driven personalised pricing approach

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dc.authoridORTAKCI, Yasin/0000-0002-0683-2049
dc.contributor.authorOrtakci, Yasin
dc.contributor.authorSeker, Huseyin
dc.date.accessioned2024-09-29T15:55:08Z
dc.date.available2024-09-29T15:55:08Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractCustomer churn has become one of the most important challenges that telecom companies have to deal with. Churn cases not only cause revenue losses but also impose extra costs of finding new customers. To overcome this issue, they develop various strategies to retain their customers. In this regard, this study presents an integrated artificial intelligence -based model that can meet the expectations of these companies not only to profile the customer churn, but also to predict a service fee that is likely to be accepted by the customers. The model first identifies the customers who are likely to churn and then offers the customers a personalised service fee that is likely to be acceptable. In this study, the K -nearest neighbours, Decision Tree, Random Forest, and Support Vector Machine methods are adapted as classifiers for churn prediction, and regression models of the same methods are utilised to predict the most optimum personalisedservice fee for potential customer churns. Additionally, to reduce the cost of data collection for companies, the feature selection method is used to determine the most optimal feature subset in churn analysis and service fee prediction. The results show that the Random Forest method is superior to other methods in both churn and price predictions and has resulted in as much as a predictive accuracy of 94% and AUC of 98%. The outcome of this comprehensive analysis using four artificial intelligence methods over three diverse telecom datasets, suggests that the proposed personalisedpricing model in the telecom sector could prevent the churn and increase the profitability by up to 36%. In addition, the model based on SVM suggests that it is possible to reduce the number of required data to be collected by as much as 20%. As the robustness and generalisation ability of the models has been demonstrated over three diverse data sets, it can be further adapted in different sectors.en_US
dc.description.sponsorshipScientific and Technological Research Council of Tuerkiye (TUEBIdot;TAK); [1059B192101063]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Tuerkiye (TUEB & Idot;TAK) under the BIDEB-2219 International Postdoctoral Research Fellowship Programme grant number 1059B192101063.en_US
dc.identifier.doi10.1016/j.cie.2024.109920
dc.identifier.issn0360-8352
dc.identifier.issn1879-0550
dc.identifier.scopus2-s2.0-85184135427en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cie.2024.109920
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4491
dc.identifier.volume188en_US
dc.identifier.wosWOS:001170760700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Industrial Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCustomer churnen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectPersonalised pricingen_US
dc.subjectFeature selectionen_US
dc.titleOptimising customer retention: An AI-driven personalised pricing approachen_US
dc.typeArticleen_US

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