PREDICTIVE MODELLING FOR CUSTOMER CHURN IN A TELECOMMUNICATIONS COMPANY (A CASE STUDY OF MTN)
Abstract:
Customer churn, the phenomenon of customers switching from one provider to another, poses significant challenges for telecommunications companies in terms of revenue generation and customer retention. To mitigate churn and improve business performance, telecom companies have increasingly turned to predictive modelling techniques to identify factors that contribute to customer attrition and develop strategies to reduce it. This study presents a case study of MTN, a leading telecommunications company, focusing on the application of predictive modelling for customer churn prediction.
The objective of this research is to develop an effective predictive model that can accurately forecast customer churn in the context of MTN. The study utilizes historical customer data, including demographic information, usage patterns, service complaints, billing details, and customer interactions, to build a predictive model using advanced machine learning algorithms. The dataset is preprocessed to handle missing values, and outliers, and feature engineering is performed to extract relevant features that have a significant impact on churn.
Several machine learning algorithms, such as logistic regression, decision trees, random forests, and gradient boosting, are employed to build and compare predictive models. Feature selection techniques are also applied to identify the most influential variables for churn prediction. The models are evaluated using appropriate evaluation metrics, such as accuracy, precision, recall, and F1-score, to assess their performance and identify the best-performing model.
The results of this study provide insights into the factors driving customer churn in the telecommunications industry and offer a practical framework for predicting and managing customer churn. By accurately identifying customers at a high risk of churn, MTN can implement targeted retention strategies, such as personalised offers, proactive customer support, and service improvements, to reduce churn rates and enhance customer loyalty.
The findings of this research contribute to the growing body of knowledge on customer churn prediction in the telecommunications industry and demonstrate the efficacy of predictive modelling techniques in addressing this critical business challenge. The study also highlights the importance of leveraging data-driven approaches to gain a competitive advantage in the telecommunications market and optimise customer retention efforts.
