Value segmentation of remotely acquired customers in banking: a model-based approach

Innovations in information technologies lead to significant changes in the banking sector. While consumers' adaptation to digital services is accelerating, there are developments in the areas of customer experience and expectations. The integration of banks with high technology to meet customer expectations ensures that new products and services are emerging in the digital banking. Remote customer acquisition has become one of the most important developments in digital banking, enabling banks to acquire new customers by overcoming geographical restrictions and without any physical interaction. The possibility that digital channels will be the most important customer acquisition channels for banks soon makes remotely acquired customers have a strategic importance for banks. It is important for banks to get to know these customers better acquired through digital channels without any physical interaction. Efforts to bring these customers to a value segment that will create more value for the bank are increasing significantly. In this study, it has been tried to emphasize the strategic importance of remote customer acquisition and online account opening process within the scope of digital transformation in the banking sector. Using data obtained from a private bank operating in Turkey, various machine learning models were applied to estimate the value segment of customers opening remote accounts and the results of the models were compared. Random Forest was the best performing machine learning model, which predicted customers' value segment with 76% accuracy.

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Eser Adı
(dc.title)
Value segmentation of remotely acquired customers in banking: a model-based approach
Yazar [Asıl]
(dc.creator.author)
Mumcu, Recep
Yazar Departmanı
(dc.creator.department)
Yeditepe University Graduate School of Social Sciences
Yazar Departmanı
(dc.creator.department)
Yeditepe University Graduate School of Social Sciences Master’s Program in Management Information Systems
Yayın Tarihi
(dc.date.issued)
2023
Yayın Turu [Akademik]
(dc.type)
preprint
Yayın Türü [Ortam]
(dc.format)
application/pdf
Konu Başlıkları [Genel]
(dc.subject)
Customer value segmentation
Konu Başlıkları [Genel]
(dc.subject)
Digital banking
Konu Başlıkları [Genel]
(dc.subject)
Remote customer acquisition
Konu Başlıkları [Genel]
(dc.subject)
Müşteri değeri bölümlendirmesi
Konu Başlıkları [Genel]
(dc.subject)
Dijital bankacılık
Konu Başlıkları [Genel]
(dc.subject)
Uzaktan müşteri edinme
Yayıncı
(dc.publisher)
Yeditepe University Academic and Open Access Information System
Dil
(dc.language.iso)
eng
Özet Bilgisi
(dc.description.abstract)
Innovations in information technologies lead to significant changes in the banking sector. While consumers' adaptation to digital services is accelerating, there are developments in the areas of customer experience and expectations. The integration of banks with high technology to meet customer expectations ensures that new products and services are emerging in the digital banking. Remote customer acquisition has become one of the most important developments in digital banking, enabling banks to acquire new customers by overcoming geographical restrictions and without any physical interaction. The possibility that digital channels will be the most important customer acquisition channels for banks soon makes remotely acquired customers have a strategic importance for banks. It is important for banks to get to know these customers better acquired through digital channels without any physical interaction. Efforts to bring these customers to a value segment that will create more value for the bank are increasing significantly. In this study, it has been tried to emphasize the strategic importance of remote customer acquisition and online account opening process within the scope of digital transformation in the banking sector. Using data obtained from a private bank operating in Turkey, various machine learning models were applied to estimate the value segment of customers opening remote accounts and the results of the models were compared. Random Forest was the best performing machine learning model, which predicted customers' value segment with 76% accuracy.
Kayıt Giriş Tarihi
(dc.date.accessioned)
2024-01-19
Açık Erişim Tarihi
(dc.date.available)
2024-01-19
Haklar
(dc.rights)
Yeditepe University Academic and Open Access Information System
Erişim Hakkı
(dc.rights.access)
Open Access
Telif Hakkı
(dc.rights.holder)
Unless otherwise stated, copyrights belong to Yeditepe University. Usage permissions are specified in the Open Access System, and "InC-NC/1.0" and "by-nc-nd/4.0" are as stated.
Telif Hakkı Url
(dc.rights.uri)
http://creativecommons.org/licenses/by-nc-nd/4.0
Telif Hakkı Url
(dc.rights.uri)
https://rightsstatements.org/page/InC-NC/1.0/?language=en
Açıklama [Genel]
(dc.description)
Final published version
Açıklama [Not]
(dc.description.note)
Note: This preprint reports new research that has not been certified by peer review and should not be used as established information without consulting multiple experts in the field.
Tanım Koleksiyon Bilgisi
(dc.description.collectioninformation)
This item is part of the preprint collection made available through Yeditepe University library. For your questions, our contact address is openaccess@yeditepe.edu.tr
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.11831/8219
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