Churn analysis: Predicting churners

Forhad, Navid; Hussain, Md. Shahriar; Rahman, Rashedur M.

doi:10.1109/icdim.2014.6991433

Cited by 8 publications

(7 citation statements)

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“…Using customer demographics (age and gender) as churn predictors in the churn prediction model is common in the literature [6,7,25,[29][30][31][32][33][34][35][36] found that young people below forty-five years of age are more likely to churn. The similar results were found by [33,36]: customers between forty-five and forty-eight years old are more likely to churn.…”

Section: New Customer Churn Model Variablesmentioning

confidence: 99%

“…The attributes included phone and call details. Reference [31] applied rule-based classification to predict whether a customer is likely to churn or not. Their dataset contained customer information such as call details (billing information and length of calls).…”

Section: Used Influence Maximisationmentioning

confidence: 99%

“…They used customer information, such as age, sex and call details. Numerous studies recognised the importance of including customer complaints as an attribute in their churn prediction model [6,7,25,31,32,34,35,39,41,43,45,46,50].…”

Section: Used Influence Maximisationmentioning

confidence: 99%

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An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021

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With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.

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Section: New Customer Churn Model Variablesmentioning

confidence: 99%

Section: Used Influence Maximisationmentioning

confidence: 99%

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An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021

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“…In general, the accuracy rate of all models reached over 60%. Forhad et al (2014) proposed the use of a rule-based classifier. According to their paper, some facts that could be used to create the rules are that if a customer doesn't pay his bill on time, he could become a churner and if there is a rapid decrease in the bill amount, the customer could churn.…”

Section: Literature Reviewmentioning

confidence: 99%

Building a proper churn prediction model for Vietnam's mobile banking service

Thành¹,

Vy²

2022

Int. j. adv. appl. sci.

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This study aims to build a model predicting the churn rate of customers using mobile banking services in Vietnam by applying data mining techniques. Customer churn is an issue that any service provider must pay attention to because it is decisive to the development of the business. The competition between banks is getting tougher, hence customer churn prediction has become of great concern to banking service companies. It is necessary for banks to collect colossal data and establish a valued model for classifying types of customers. In this study, three supervised statistical learning methods which are KNN, Random Forest, and Gradient Boosting are applied to the churn prediction model using the data source of VIB’s customers. In addition to selecting models belonging to the group of weak single learners such as Neural Networks, Naïve Bayes Classifier, and K-nearest Neighbor..., this paper utilizes Random Forest and Gradient Boosting which are assessed as better models because they can combine weak learners for improving model efficiency and capable of classification. The results exhibited that Gradient Boosting is the best performance in the three above classifiers with a 79.71% of accuracy rate, and 86.23% of ROC (Receiver Operating Characteristic) curve graph. Moreover, the decision tree algorithm generates readable rules for churner and non-churner classification which are potentially helpful to managers. Finally, this study suggests a proper model that can be used to forecast churners of mobile banking services in Vietnam.

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“…In the demographic analysis, age and gender [21,40,41], income [34], child info and customer profession [21], place of residence (rural / city) [42], education level [39], marital status [40] have been investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis

DONAT

USTA

2022

Sakarya University Journal of Science

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Today's rising cutting-edge technology requirements and competitive environment in telecommunication industry has gained a remarkable importance due to the COVID-19 pandemics in terms of high need of information sharing and remote communication necessity. Telecommunication companies conduct significant analyses by highlighting that the customer data is the most valuable information. Besides, they obtain results emphasizing that acquiring new customers is costlier than retaining the existing ones. Therefore, the companies are willing to determine the important customer features in order to understand why they shift to the other telecommunication service providers. Hence, this study aims to conduct a churn analysis by feature selection approach with large volumes of telecommunication customer data in order to present what kind of customer behaviors and qualifications exist. Since there is a huge amount of data in this field, data mining is a vital requirement. The performance outputs were observed, and the features carrying these outputs to the highest value were identified. The data collection and analysis were carried out in mid-2019, and the same data collection and analysis were carried out again at the beginning of 2021, and these before and after results were compared. In addition, a comparison was made with the results obtained by the other churn analysis studies. This paper contributes to the practitioners by presenting the most important customer features in telecom customer churn, and a new approach in performance evaluation have been proposed specific to the telecommunication market with the industry experts’ guidance as a theoretical contribution.

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Churn analysis: Predicting churners

Cited by 8 publications

References 0 publications

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

Building a proper churn prediction model for Vietnam's mobile banking service

The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis

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