Forecasting Customer Churn Using Machine Learning: A Marketing Analytics Approach

The modern business environment demands equal importance for customer retention as it does for customer acquisition. The practice of customers ending their relationships with brands represents a major business concern which affects all industries. Organizations now use big data and advanced analytics together with machine learning techniques to actively forecast and minimize customer churn. This research demonstrates a marketing analytics strategy for predicting customer churn through supervised machine learning algorithms which includes Logistic Regression and Decision Trees and Random Forests and Gradient Boosting Machines. The analysis uses a synthetic dataset which represents realistic customer data through variables that include age and tenure and monthly spend and support interaction frequency. The most important predictors of churn are determined through SHAP (SHapley Additive explanations) values and correlation matrices. The evaluation of model performance relies on accuracy and precision and recall and F1-score and AUC-ROC metrics. Ensemble methods demonstrate superior performance than basic models when analyzing complex nonlinear customer behavior patterns because they achieve an AUC-ROC score of 0.89.