To use ROC AUC in email marketing, follow these steps:
Data Collection: Gather data on your past email campaigns, including whether the emails were opened or not, and other relevant features like subject lines, send times, and recipient demographics. Model Training: Use this data to train a predictive model. Various algorithms like logistic regression, decision trees, or neural networks can be employed. ROC Curve Creation: Plot the ROC Curve by calculating the True Positive Rate (TPR) and False Positive Rate (FPR) at various threshold settings of your model. Area Under Curve (AUC): Compute the AUC to summarize the performance. A score close to 1 indicates a highly effective model.