Predicting Customer Success in Digital Marketing with Data Mining and Naive Bayes Classifier Using Google Analytics

Abstract-In the era of digital transformation, organizations are increasingly leveraging data analytics to optimize marketing strategies and enhance customer engagement. Predicting customer performance is critical for businesses aiming to tailor marketing efforts, improve customer retention, and maximize revenue. This study presents a comprehensive data mining framework utilizing the Naive Bayes classifier to forecast customer performance based on historical behavior and interaction data. Employing Google Analytics as the primary data collection tool, we evaluate the model’s effectiveness by analyzing metrics such as accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and the area under the Receiver Operating Characteristic (ROC) curve. The results illustrate the framework’s potential to provide actionable insights into customer behavior, thereby facilitating more informed marketing strategies and decision-making processes.