An Intelligent Machine Learning Framework for Customer Churn Prediction in CRM Systems
TL;DR
A CRM system that relies on clever machine learning techniques and outperforms state-of-the-art machine learning and deep learning models for predicting customer attrition so that businesses may pinpoint consumers who are likely to churn, which allows for more proactive retention measures, happier customers, and more profits in the long run.
Abstract
In today's highly competitive market environment, organizations face significant challenges in retaining customers due to increasing competition, evolving customer expectations, and unpredictable churn behavior. In order to address these concerns and predict customer turnover using the Telco Customer turnover dataset, this article suggests a CRM system that relies on clever machine learning techniques. To ensure high-quality data for model training, the proposed system incorporates thorough data pretreatment stages such ADASYN class balancing, missing value management, label encoding, and z-score normalisation. Build and test two supervised ML models, XGBoost and Random Forest (RF), to see how well they perform. The trials showed that the Proposed RF Model achieved 97.1% accuracy (ACC), 97.6% precision (PRE), 97.4% recall (REC), and 97.2% F1-score (F1), whereas the Model Proposed XGBoost Model generated 96.9% ACC, 96.7% PRE, 96.6% REC, and 96.3% F1-score. The suggested method outperforms state-of-the-art machine learning and deep learning models for predicting customer attrition. With the help of the suggested framework, businesses may pinpoint consumers who are likely to churn, which in turn allows for more proactive retention measures, happier customers, and more profits in the long run.