Dynamic behavior based churn prediction in mobile telecom

Alboukaey, Nadia; Joukhadar, Ammar; Ghneim, Nada

doi:10.1016/j.eswa.2020.113779

Cited by 72 publications

(52 citation statements)

References 22 publications

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“…Zhao et al [6] state that customer churn management is the need for the survival and development of the telecom industry. Alboukaey et al [7] believe that customer churn is one of the most challenging problems which affects revenue and customer base in mobile telecom operators. For the telecom industry in the era of big data, the growth bonus gradually disappears, the transformation continues to deepen, and the pressure from investment and construction costs for future is huge.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Zhao

Qing-jun

Chang

et al. 2021

Discrete Dynamics in Nature and Society

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Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

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Section: Introductionmentioning

confidence: 99%

A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Zhao

Qing-jun

Chang

et al. 2021

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…Earlier the prediction, the better it is for the organizations. As per Alboukaey et al (2020), the monthly churn prediction is partly inefficient; hence daily churn prediction models are required. Thus, churn management includes the identification of valuable customers who might exhibit churning behavior and being proactive to prevent the churn.…”

Section: Literature Reviewmentioning

confidence: 99%

Is price a significant predictor of the churn behavior during the global pandemic? A predictive modeling on the telecom industry

Shukla

Prashar

Pandiya

2021

J Revenue Pricing Manag

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“…According to Glady et al [12], churn is a marketing-related term that represents a consumer who is switching from one company to a competitor in the near future, and, according to Verbeke et al [13], is a management science problem that adopts a data mining approach to try to solve the problem related to the lower costs for retaining customers versus the costs of attracting new customers [14]. This approach requires determining which customers have a higher propensity to attrite [15], but the reasons for dropping out could be attributed to different events, Berry and Linoff [2] thus divide customer dropout in voluntary and involuntary categories; voluntary dropout represents a decision by the customer to end the relationship, while involuntary dropout occurs when the company ends the customer relationship VOLUME 4, 2016 because the customer does not fulfil their obligations, via breaches such as lack of payment or abuse of service. This method creates two scenarios leading to an end event that is referred to as dropout.…”

Section: Introductionmentioning

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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Dynamic behavior based churn prediction in mobile telecom

Cited by 72 publications

References 22 publications

A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Is price a significant predictor of the churn behavior during the global pandemic? A predictive modeling on the telecom industry

A SLR on Customer Dropout Prediction

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