Classification of customer churn prediction model for telecommunication industry using analysis of variance

Abstract

Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has been discovered. A support vector machine is employed as the foundational learner, and a churn prediction model is constructed based on each analysis of variance. The separation of churn data revealed by experimental assessment is recommended for churn prediction analysis. Customer attrition is high, but an instantaneous support can ensure that customer needs are addressed and assess an employee's capacity to achieve customer satisfaction. This study uses an analysis of variance (ANOVA) with a support vector machine (SVM), classification in analyzing risks in telecom systems It may be determined that SVM provides the most accurate forecast of customer turnover (95%). The projected outcomes will allow other organizations to assess possible client turnover and collect customer feedback.

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

“…Authors confirmed that they achieved an accuracy rate of 80% in telecom customer churn prediction. Babatunde et al [20] used a hybrid approach which combined analysis of variance (ANOVA) with SVM to predict customer's churn. The authors confirmed that they successfully predicted future churns with an accuracy rate that reached 95%.…”