4,504 research outputs found
Customer Churn Prediction in Telecom Sector: A Survey and way a head
© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe
A SLR on Customer Dropout Prediction
Dropout prediction is a problem that is being addressed with machine learning algorithms;
thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict
the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as
which features should be selected and how to measure accuracy while considering whether the features are
appropriate according to the business context in which they are employed. To solve these questions, the
goal of this paper is to develop a systematic literature review to evaluate the development of existing studies
and to predict the dropout rate in contractual settings using machine learning to identify current trends and
research opportunities. The results of this study identify trends in the use of machine learning algorithms
in different business areas and in the adoption of machine learning algorithms, including which metrics are
being adopted and what features are being applied. Finally, some research opportunities and gaps that could
be explored in future research are presented.info:eu-repo/semantics/publishedVersio
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