Testing the performance of feature selection methods for customer churn analysis: Case study in B2B business
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Churn analysis has recently become one of the favorite topics of marketing teams with the development of machine learning models. This study aims to discover the most suitable feature selection (FS) model for churn analysis by using the databases of BiletBank, a business-to-business (B2B) company. It was found that some categorical data such as agency type and currency used by customers, along with periodic flight sales data, are also meaningful features for churn analysis in the BiletBank customer portfolio. This feature selection study in the database will be a source for future churn analysis studies.
Açıklama
Anahtar Kelimeler
Customer Churn Analysis, Feature Selection, B2B, Sequential Forward Selection, Sequential Backward Selection, Classification, Logistic Regression, Support Vector Machines, Random Forest Classifier, Extra Tress Classifier
Kaynak
International Conference on Computing, Intelligence and Data Analytics
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
Sayı
Künye
Sancar, S., & Uzun-Per, M. (2023). Testing the performance of feature selection methods for customer churn analysis: Case study in B2B business. F.P. García Márquez, A. Jamil, S. Eken, A.A. Hameed, (Eds.), International Conference on Computing, Intelligence and Data Analytics (pp 509-519). Springer: Cham. https://doi.org/10.1007/978-3-031-27099-4_39
