A Hybrid Churn Prediction Model in Mobile Telecommunication Industry

Olle, Georges Olle

doi:10.7763/ijeeee.2014.v4.302

Cited by 29 publications

(11 citation statements)

References 8 publications

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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%

“…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%

“…On the contrary, reference [34] stated that the four months is needed to predict a customer churning based on his/her dissatisfaction. However, reference [3] increased this to a five-month collection of tweets as dataset to predict their customer growth model.…”

Section: Data Set Constructionmentioning

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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“…Logistic Regression (LR) for classification, and Voted Perceptron (VP) for estimation has been used in the model. According to the results obtained, the predictive power of the hybrid model has been reported to be better than of individual methods (Olle 2014).…”

Section: Studies Of Churn Analysis In Telecommunication Industrymentioning

confidence: 97%

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Kayaalp

2017

KSEJ

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companies are provided by the payments made by these customers periodically. It is very important to be able to keep customers satisfied in order to be able to sustain this revenue with the least expenditure cost. The objectives of this study are: • Reviewing the relevant studies about churn analysis on telecommunications industry presented in the last five years, particularly in the last two years, and introducing these up-to-date studies in the literature, • Determining the data mining methods frequently used in churn implementations, • Shedding a light on methods that can be used in further studies.

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“…The cost of acquiring new customers is 5 to 10 times higher than the cost of retaining existing customers [26], and the churn rate of new customers is often higher than existing customers [27]. For telco operators, the adoption of a retention strategy can obtain greater profits by regaining trust from customers who have already enjoyed their services and products.…”

Section: Research Motivation and Contributionsmentioning

confidence: 99%

Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

et al. 2021

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In the telco industry, attracting new customers is no longer a good strategy since the cost of retaining existing customers is much lower. Churn management becomes instrumental in the telco industry. As there is limited study combining churn prediction and customer segmentation, this paper aims to propose an integrated customer analytics framework for churn management. There are six components in the framework, including data pre-processing, exploratory data analysis (EDA), churn prediction, factor analysis, customer segmentation, and customer behaviour analytics. This framework integrates churn prediction and customer segmentation process to provide telco operators with a complete churn analysis to better manage customer churn. Three datasets are used in the experiments with six machine learning classifiers. First, the churn status of the customers is predicted using multiple machine learning classifiers. Synthetic Minority Oversampling Technique (SMOTE) is applied to the training set to deal with the problems with imbalanced datasets. The 10-fold cross-validation is used to assess the models. Accuracy and F1-score are used for model evaluation. F1-score is considered to be an important metric to measure the models for imbalanced datasets since the premise of churn management is to be able to identify customers who will churn. Experimental analysis indicates that AdaBoost performed the best in Dataset 1, with accuracy of 77.19% and F1-score of 63.11%. Random Forest performed the best in Dataset 2, with accuracy of 93.6% and F1-score of 77.20%. Random Forest performed the best in Dataset 3 in terms of accuracy, at 63.09%, while Multi-layer Perceptron performed the best in terms of F1-score, at 42.84%. After implementing churn prediction, Bayesian Logistic Regression is used to conduct the factor analysis and to figure out some important features for churn customer segmentation. Churn customer segmentation is then carried out using K-means clustering. Customers are segmented into different groups, which allows marketers and decision makers to adopt retention strategies more precisely.

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A Hybrid Churn Prediction Model in Mobile Telecommunication Industry

Cited by 29 publications

References 8 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

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Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

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