Telecom churn prediction and used techniques, datasets and performance measures: a review

H Jain, A Khunteta, S Srivastava - Telecommunication Systems, 2021 - Springer
Customer churn prediction in telecommunication industry is a very essential factor to be
achieved and it makes direct impact to customer retention and its revenues. Develo** a …

Intelligent data analysis approaches to churn as a business problem: a survey

DL García, À Nebot, A Vellido - Knowledge and Information Systems, 2017 - Springer
Globalization processes and market deregulation policies are rapidly changing the
competitive environments of many economic sectors. The appearance of new competitors …

A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy

R Sudharsan, EN Ganesh - Connection Science, 2022 - Taylor & Francis
Owing to saturated markets, fierce competition, dynamic criteria, along with introduction of
new attractive offers, the considerable issue of customer churn was faced by the …

Predicting employee attrition using machine learning techniques

F Fallucchi, M Coladangelo, R Giuliano… - Computers, 2020 - mdpi.com
There are several areas in which organisations can adopt technologies that will support
decision-making: artificial intelligence is one of the most innovative technologies that is …

Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction

Z Liu, Y Zhang, MZ Abedin, J Wang, H Yang… - Journal of Retailing and …, 2024 - Elsevier
Accurately identifying potential purchasers (PPers) is pivotal for enhancing an enterprise's
core competitiveness in a competitive market. Although existing research focused on …

[PDF][PDF] Customer churn prediction in telecommunication industry using deep learning

SW Fujo, S Subramanian… - Information Sciences …, 2022 - digitalcommons.aaru.edu.jo
Without proper analysis and forecasting, industries will find themselves repeatedly churning
customers, which the telecom industry in particular cannot afford. A predictable model for …

A data mining-based framework for supply chain risk management

ME Kara, SÜO Fırat, A Ghadge - Computers & Industrial Engineering, 2020 - Elsevier
Increased risk exposure levels, technological developments and the growing information
overload in supply chain networks drive organizations to embrace data-driven approaches …

Why customer satisfaction is important to business?

AA Hamzah, MF Shamsudin - Journal of Undergraduate Social Science …, 2020 - abrn.asia
This paper explores the importance of customer in strategic marketing in the values of
customer satisfaction and loyalty. The role of customer for organizations in the 21st century …

Machine-learning techniques for customer retention: A comparative study

SF Sabbeh - … Journal of advanced computer Science and …, 2018 - search.proquest.com
Nowadays, customers have become more interested in the quality of service (QoS) that
organizations can provide them. Services provided by different vendors are not highly …

An empirical comparison of techniques for the class imbalance problem in churn prediction

B Zhu, B Baesens, SKLM vanden Broucke - Information sciences, 2017 - Elsevier
Class imbalance brings significant challenges to customer churn prediction. Many solutions
have been developed to address this issue. In this paper, we comprehensively compare the …