A customer lifetime value model for the banking industry: a guide to marketing actions

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dc.authoridUray, Nimet/0000-0003-2285-1845;
dc.authorwosidUray, Nimet/AAH-5395-2019
dc.authorwosidUlengin, Fusun/AAD-2476-2019
dc.authorwosidUlengin, Fusun/T-2338-2019
dc.contributor.authorEkinci, Yeliz
dc.contributor.authorUray, Nimet
dc.contributor.authorUlengin, Fusun
dc.date.accessioned2024-07-18T20:47:13Z
dc.date.available2024-07-18T20:47:13Z
dc.date.issued2014
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractPurpose - The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry - namely the banking sector. Design/methodology/approach - This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as top 20 per cent and bottom 80 per cent profitable. Findings - Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership-related indicators, are also significant factors. Practical implications - Organisations in the banking sector have to persuade their customers to use certain routine risk-bearing transaction-based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product-related variables directly increase the value of customers. Originality/value - The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry-specific method which is based on easily measurable and objective indicators.en_US
dc.identifier.doi10.1108/EJM-12-2011-0714
dc.identifier.endpage784en_US
dc.identifier.issn0309-0566
dc.identifier.issn1758-7123
dc.identifier.issue3.Nisen_US
dc.identifier.startpage761en_US
dc.identifier.urihttps://doi.org/10.1108/EJM-12-2011-0714
dc.identifier.urihttps://hdl.handle.net/11411/7734
dc.identifier.volume48en_US
dc.identifier.wosWOS:000339628600017en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.ispartofEuropean Journal of Marketingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectLeast Squares Estimationen_US
dc.subjectCustomer Lifetime Valueen_US
dc.subjectLinear Regressionen_US
dc.subjectMarketing Decisionen_US
dc.subjectSegmentationen_US
dc.subjectOptimizationen_US
dc.titleA customer lifetime value model for the banking industry: a guide to marketing actionsen_US
dc.typeArticleen_US

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