Customer churn prediction in telecom sector using machine learning techniques
In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Tel...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720723001443 |
| _version_ | 1797259645767647232 |
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| author | Sharmila K. Wagh Aishwarya A. Andhale Kishor S. Wagh Jayshree R. Pansare Sarita P. Ambadekar S.H. Gawande |
| author_facet | Sharmila K. Wagh Aishwarya A. Andhale Kishor S. Wagh Jayshree R. Pansare Sarita P. Ambadekar S.H. Gawande |
| author_sort | Sharmila K. Wagh |
| collection | DOAJ |
| description | In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Telecom management and analysts are finding the explanations behind customers leaving subscriptions and behavior activities of the holding churn customers’ data. This system uses classification techniques to find out the leave subscriptions and collects the reasons behind the leave subscription of customers in the telecom industry. The major goal of this system is to analyze the diversified machine learning algorithms which are required to develop customer churn prediction models and identify churn reasons in order to give them with retention strategies and plans. In this system, leave subscriptions collects customers' data by applying classification algorithms such as Random Forest (RF), machine learning techniques such as KNN and decision tree Classifier. It offers an efficient business model that analyzes customer churn data and gives accurate predictions of churn customers so that business management may take action within the churn period to stop churn as well as loss in profit. System achieves an accuracy of 99 % using the random forest classifier for churn predicts, the classifier matrix has achieved a precision of 99 % with a recall factor of 99 % alongwith received overall accuracy of 99.09 %. Likewise, our research work improves churn prediction, scope other business fields, and provide prediction models to hold their existing customers customer service, and avoid churn effectively. |
| first_indexed | 2024-03-09T15:39:06Z |
| format | Article |
| id | doaj.art-8c567f19a8fe4b52aed9747c9c4e1d96 |
| institution | Directory Open Access Journal |
| issn | 2666-7207 |
| language | English |
| last_indexed | 2024-04-24T23:12:44Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj.art-8c567f19a8fe4b52aed9747c9c4e1d962024-03-17T07:58:53ZengElsevierResults in Control and Optimization2666-72072024-03-0114100342Customer churn prediction in telecom sector using machine learning techniquesSharmila K. Wagh0Aishwarya A. Andhale1Kishor S. Wagh2Jayshree R. Pansare3Sarita P. Ambadekar4S.H. Gawande5Department of Computer Engineering, M.E.S. College of Engineering, S.P. Pune University, Pune, Maharashtra 411001, India; Corresponding author.Department of Information Technology, MKSSS's Cummins College of Engineering for Women, Pune 411052, IndiaDepartment of Computer Engineering, AISSMS Institute of Information Technology, S.P. Pune University, Pune, Maharashtra 411001, IndiaDepartment of Computer Engineering, M.E.S. College of Engineering, S.P. Pune University, Pune, Maharashtra 411001, IndiaDepartment of Computer Engineering, K. J. Somaiya Institute of Technology, Sion, Mumbai, IndiaIndustrial Tribology Laboratory, Department of Mechanical Engineering, M.E.S. College of Engineering, S.P. Pune University, Pune, Maharashtra, 411001, IndiaIn the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Telecom management and analysts are finding the explanations behind customers leaving subscriptions and behavior activities of the holding churn customers’ data. This system uses classification techniques to find out the leave subscriptions and collects the reasons behind the leave subscription of customers in the telecom industry. The major goal of this system is to analyze the diversified machine learning algorithms which are required to develop customer churn prediction models and identify churn reasons in order to give them with retention strategies and plans. In this system, leave subscriptions collects customers' data by applying classification algorithms such as Random Forest (RF), machine learning techniques such as KNN and decision tree Classifier. It offers an efficient business model that analyzes customer churn data and gives accurate predictions of churn customers so that business management may take action within the churn period to stop churn as well as loss in profit. System achieves an accuracy of 99 % using the random forest classifier for churn predicts, the classifier matrix has achieved a precision of 99 % with a recall factor of 99 % alongwith received overall accuracy of 99.09 %. Likewise, our research work improves churn prediction, scope other business fields, and provide prediction models to hold their existing customers customer service, and avoid churn effectively.http://www.sciencedirect.com/science/article/pii/S2666720723001443ChurnersCustomer churn predictionUp-samplingClassifiersSurvival analysis |
| spellingShingle | Sharmila K. Wagh Aishwarya A. Andhale Kishor S. Wagh Jayshree R. Pansare Sarita P. Ambadekar S.H. Gawande Customer churn prediction in telecom sector using machine learning techniques Results in Control and Optimization Churners Customer churn prediction Up-sampling Classifiers Survival analysis |
| title | Customer churn prediction in telecom sector using machine learning techniques |
| title_full | Customer churn prediction in telecom sector using machine learning techniques |
| title_fullStr | Customer churn prediction in telecom sector using machine learning techniques |
| title_full_unstemmed | Customer churn prediction in telecom sector using machine learning techniques |
| title_short | Customer churn prediction in telecom sector using machine learning techniques |
| title_sort | customer churn prediction in telecom sector using machine learning techniques |
| topic | Churners Customer churn prediction Up-sampling Classifiers Survival analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666720723001443 |
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