A Novel Machine Learning-Based Early Warning Detection System for Business Customer Churn

This study presents an Early Warning Detection System (ML-EWDS) based on machine learning for reducing commercial customer turnover. Companies in all sectors need to be especially concerned about client churn in today's environment of volatile markets and plenty of consumer options. The ML-EWDS overcomes this difficulty by analyzing past customer data that has been augmented with Regency, Frequency, and Monetary (RFM) attributes using an ensemble learning model that includes a Random Forest classifier. The system's high accuracy (87.5%) and ROC-AUC (0.92) are the result of meticulous data preparation, model training, and cross-validation. Next, the ML-EWDS is put into action in a live business setting, where it alerts potentially churned consumers ahead of time. The solution significantly reduces turnover by 70% during a three-month assessment period, which has a beneficial effect on income. The ethical implementation of this technology is emphasized, with a focus on data protection and bias reduction. This study demonstrates the revolutionary potential of ML-EWDS in CRM, with implications for both academics and business and many possible avenues for further development.