Churn prediction in telecommunication industry using kernel Support Vector Machines

Y, Nguyen Nhu; Tran, Ly Van; Son, Dao Vu Truong

doi:10.1371/journal.pone.0267935

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…Fareniuk et al [11] is 2022 used different data science and machine elarning models like k-nearest neighbor (KNN), NNs, and RF to classify the customers to loyal and likely to be churn for telecom companies using Ukrainian telecom company dataset and they achieved classification accuracy rate of 90%. Nguyen et al [12] in the same year, used the kernel SVM to predict customer's churn for telecom companies and they achieved a classification accuracy rate of 98.9%. Shrestha and Shakya [13] in the same year also used XGBoost to address the same problem of telecom customer's churn prediction and they achieved an accuracy rate of 96%.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Hussien,

Mahgoub,

Youssef

et al. 2024

IJEECS

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<p><span>Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes place). Furthermore, an extensive data exploratory analysis is performed to gain more insights from the data to identify the important features. Transactional data and static profile data are integrated to feed a deep learning model which is implemented using two methodologies: time-series approach and statistical approach. It is found that the time-series approach gives the best performance and fulfills all the requirements. The experiments show that the proposed framework introduces a good overall performance in comparison to existing approaches based on standard metrics like accuracy and mean absolute error (MAE) values. What makes the proposed work novel and special is that it is the first approach that addresses the telecom customer’s next future interaction not just churn prediction like the other approaches in literature.</span></p>

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Section: Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Hussien,

Mahgoub,

Youssef

et al. 2024

IJEECS

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show abstract

“…Literature [7] proposes the use of Kernel SVM to construct a churn prediction model for a telecom company, in addition, resampling techniques such as Tomek Link and ENN are used on the dataset to deal with unbalanced data. Literature [8] analyzes the sales of a retail store on Friday using a classification model and proposes a machine learning based technique to predict the age group of Friday shoppers for comparison.…”

Section: Customer Churn Predictionmentioning

confidence: 99%

Research on customer churn prediction and model interpretability analysis

Peng,

2023

PLoS ONE

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In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

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“…Churn rate modeling and analysis play a vital role in the telecommunications industry [1][2]. Creating a predictive churn model involves multiple steps, such as data collection, understanding, pre-processing, learning, model design, development, validation, and evaluation [3,4]. The effective use of training and testing data simplifies the process, ensuring the accuracy and effectiveness of the designed model.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have examined predictive models using comparable datasets [3,7,8,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. These studies indicate that the SVM algorithm often achieves the highest accuracy value.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate

Akbar

Apriono

2023

Green Intell. Syst. Appl.

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Customer churn frequently occurs in the telecommunications industry, which provides services and can be detrimental to companies. A predictive model can be useful in determining and analyzing the causes of churn actions taken by customers. This paper aims to analyze and implement machine learning models to predict churn actions using Kaggle data on customer churn. The models considered for this research include the XG Boost Classifier algorithm, Bernoulli Naïve Bayes, and Decision Tree algorithms. The research covers the steps of data preparation, cleaning, and transformation, exploratory data analysis (EDA), prediction model design, and analysis of accuracy, F1 Score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. The EDA results indicate that the contract type, length of tenure, monthly invoice, and total bill are the most influential features affecting churn actions. Among the models considered, the XG Boost Classifier algorithm achieved the highest accuracy and F1 score of 81.59% and 74.76%, respectively. However, in terms of efficiency, the Bernoulli Naïve Bayes and Decision Tree algorithms outperformed XG Boost, with AUC scores of 0.7469 and 0.7468, respectively.

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Churn prediction in telecommunication industry using kernel Support Vector Machines

Cited by 10 publications

References 16 publications

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Research on customer churn prediction and model interpretability analysis

Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate

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