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Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion. / Alizadeh, Masoud; Zadeh, Danial Sadrian; Moshiri, Behzad et al.
In: IEEE Access, 15.03.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Alizadeh, M, Zadeh, DS, Moshiri, B & Montazeri, A 2023, 'Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion', IEEE Access. https://doi.org/10.1109/access.2023.3257352

APA

Alizadeh, M., Zadeh, D. S., Moshiri, B., & Montazeri, A. (2023). Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion. IEEE Access. Advance online publication. https://doi.org/10.1109/access.2023.3257352

Vancouver

Alizadeh M, Zadeh DS, Moshiri B, Montazeri A. Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion. IEEE Access. 2023 Mar 15. Epub 2023 Mar 15. doi: 10.1109/access.2023.3257352

Author

Alizadeh, Masoud ; Zadeh, Danial Sadrian ; Moshiri, Behzad et al. / Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion. In: IEEE Access. 2023.

Bibtex

@article{6f50da41c1a74af0b826131aff14bdf6,
title = "Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion",
abstract = "There has been an increase in customer churn over the past few years—customers decide not to continue purchasing products or services from an organization. Customers{\textquoteright} data lie in two categories: soft and hard. The term “hard data” refers to the records generated by various devices and programs, including but not limited to smartphones, computers, sensors, smart meters, fleet management systems, call detail records (CDRs), and consumer bank transaction data. On the other hand, information that is subject to interpretation and viewpoint is known as “soft data.” Fusing these two types of data leads to better customer behaviour analysis. This paper uses a supervised machine learning algorithm, namely a decision tree (DT), and the change mining method to model hard data. K-means clustering, an unsupervised machine learning algorithm, is also used along with the data preprocessing techniques. This paper also considers the Dempster-Shafer theory and other steps for soft data modelling. By fusing soft and hard data, the churn rate of customers compared with each other can be calculated. Besides, the customers{\textquoteright} banking data are leveraged for data modelling. The results show that the banking industry will gain a more dynamic and efficient customer relationship management system by using this model.",
keywords = "General Engineering, General Materials Science, General Computer Science, Electrical and Electronic Engineering",
author = "Masoud Alizadeh and Zadeh, {Danial Sadrian} and Behzad Moshiri and Allahyar Montazeri",
year = "2023",
month = mar,
day = "15",
doi = "10.1109/access.2023.3257352",
language = "English",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Development of a Customer Churn Model for Banking Industry Based on Hard and Soft Data Fusion

AU - Alizadeh, Masoud

AU - Zadeh, Danial Sadrian

AU - Moshiri, Behzad

AU - Montazeri, Allahyar

PY - 2023/3/15

Y1 - 2023/3/15

N2 - There has been an increase in customer churn over the past few years—customers decide not to continue purchasing products or services from an organization. Customers’ data lie in two categories: soft and hard. The term “hard data” refers to the records generated by various devices and programs, including but not limited to smartphones, computers, sensors, smart meters, fleet management systems, call detail records (CDRs), and consumer bank transaction data. On the other hand, information that is subject to interpretation and viewpoint is known as “soft data.” Fusing these two types of data leads to better customer behaviour analysis. This paper uses a supervised machine learning algorithm, namely a decision tree (DT), and the change mining method to model hard data. K-means clustering, an unsupervised machine learning algorithm, is also used along with the data preprocessing techniques. This paper also considers the Dempster-Shafer theory and other steps for soft data modelling. By fusing soft and hard data, the churn rate of customers compared with each other can be calculated. Besides, the customers’ banking data are leveraged for data modelling. The results show that the banking industry will gain a more dynamic and efficient customer relationship management system by using this model.

AB - There has been an increase in customer churn over the past few years—customers decide not to continue purchasing products or services from an organization. Customers’ data lie in two categories: soft and hard. The term “hard data” refers to the records generated by various devices and programs, including but not limited to smartphones, computers, sensors, smart meters, fleet management systems, call detail records (CDRs), and consumer bank transaction data. On the other hand, information that is subject to interpretation and viewpoint is known as “soft data.” Fusing these two types of data leads to better customer behaviour analysis. This paper uses a supervised machine learning algorithm, namely a decision tree (DT), and the change mining method to model hard data. K-means clustering, an unsupervised machine learning algorithm, is also used along with the data preprocessing techniques. This paper also considers the Dempster-Shafer theory and other steps for soft data modelling. By fusing soft and hard data, the churn rate of customers compared with each other can be calculated. Besides, the customers’ banking data are leveraged for data modelling. The results show that the banking industry will gain a more dynamic and efficient customer relationship management system by using this model.

KW - General Engineering

KW - General Materials Science

KW - General Computer Science

KW - Electrical and Electronic Engineering

U2 - 10.1109/access.2023.3257352

DO - 10.1109/access.2023.3257352

M3 - Journal article

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -