Results 91 to 100 of about 639,400 (197)

Customer churn prediction using machine learning and customer lifetime value analysis at Eurotel

, 2021
Every CFO should invest in getting to know the organisation’s customers. After all, building long-term and valuable customer relationships is an important driver of value creation.
Stouthuysen, Kristof, Verdonck, Tim, Decorte, Thomas, Van Der Schraelen, Lennert, Brié, Bjarne   +4 more
core  

Customer churn prediction in the telecommunication sector using a rough set approach

, 2017
Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitioners,
Anwar, S., Hussain, A., Amin, Adnan, Anwar, Sajid, Adnan, Awais, Nawaz, Muhammad, Hussain, Amir, Amin, A., Nawaz, M., Adnan, A., Alawfi, Khalid, Huang, Kaizhu, Alawfi, K., Huang, K.   +13 more
core   +1 more source

CUSTOMER CHURN PREDICTION

IARJSET, 2021
Senthilnayaki B, Swetha M, Nivedha D
openaire   +1 more source

Research on customer churn prediction and model interpretability analysis. [PDF]

PLoS One, 2023
Peng K, Peng Y, Li W.
europepmc   +1 more source

Factors, Predictability, and Explainability of Mobile Telephony Customer Departure in Telecommunications Companies: A Systematic Review of the Literature

IEEE Access
The telecommunications sector has experienced exponential growth since the year 2000, reaching 5.31 trillion users by 2022, generating 1.07 trillion in revenue for telecommunications companies.
David Freire, David Santos Mauricio Sanchez, Jose Luis Castillo Sequera, Daniel Fiallo Moncayo   +3 more
doaj   +1 more source

Early warning of telecom enterprise customer churn based on ensemble learning. [PDF]

PLoS One, 2023
Zhou Y, Chen W, Sun X, Yang D.
europepmc   +1 more source

CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction

Journal of Big Data
Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes.
I Nyoman Mahayasa Adiputra, Paweena Wanchai   +1 more
doaj   +1 more source

Banking customer churn prediction using random forest based on Smote and Adasyn approach

Tạp chí Phát triển và Hội nhập
Customer Churn is now becoming a significant problem in the banking sector. It is necessary to seek solutions to predict the rate of customer churn in banks; however, the dataset for customer churn prediction in banks is imbalanced.
Cong Thanh Tran
doaj   +1 more source

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets. [PDF]

Comput Intell Neurosci, 2022
Faritha Banu J, Neelakandan S, Geetha BT, Selvalakshmi V, Umadevi A, Martinson EO.   +5 more
europepmc   +1 more source

Propension to customer churn in a financial institution: a machine learning approach. [PDF]

Neural Comput Appl, 2022
de Lima Lemos RA, Silva TC, Tabak BM.
europepmc   +1 more source