Sam, Glory and Asuquo, Philip and Stephen, Bliss (2024) Customer Churn Prediction using Machine Learning Models. Journal of Engineering Research and Reports, 26 (2). pp. 181-193. ISSN 2582-2926
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Abstract
Customer churn is a critical concern for the telecommunication industry. Understanding and predicting customer churn can lead to more effective retention strategies and an increase in profitability. Predicting customer churn allows telecommunication companies to identify potentially dissatisfied customers early on and take proactive measures to retain them. Due to a large client base, the telecom industry generates a large volume of data on a daily basis. Decision makers and business analysts stressed that acquiring new customers is more expensive than retaining existing ones. Business analysts and customer relationship management (CRM) analysts must understand the reasons for customer churn as well as behaviour patterns from existing churn data. This paper proposes a churn prediction model that uses classication and clustering techniques to identify churn customers and provides the factors that contribute to customer churning in the telecom sector. The results presented shows that XBoost and Random Forest achieved higher prediction accuracy when compared to K- Nearest Neighbours, Support Vector Machines and Decision Trees in terms of accuracy, precision, F1-Score and recall.
Item Type: | Article |
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Subjects: | Euro Archives > Engineering |
Depositing User: | Managing Editor |
Date Deposited: | 07 Feb 2024 08:43 |
Last Modified: | 07 Feb 2024 08:43 |
URI: | http://publish7promo.com/id/eprint/4434 |