An innovative optimized model to anticipate clients about immigration in telecom industry

Naik, Midde Venkateswarlu; Reddy, Sareddy Shiva

doi:10.1109/icatcct.2017.8389139

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Al-Molhem et al [68] conducted social network analysis to enhance the results of churn prediction models in the telecom domain using call detail records to construct a weighed graph representing the distance between two subscribers to calculate the centrality. The usage of marketing related variables, such as promotions offered to a customer, calls developed in a retention strategy, and helpdesk interactions, were applied by Verbeke et al [50] However, certain studies did not identify the features employed [9,12,18,22,25,42,48,67,70,73,85,87,88,97,98,99,107,110,112,113,116,119,122,127], which in some cases are related to the usage of more than one database. Idris et al [119] used two databases (orange and cell2cell).…”

Section: Rq3 -What Are the Features Used To Predict Dropout?mentioning

confidence: 99%

A SLR on Customer Dropout Prediction

et al. 2022

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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.

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