Enhancing Personalized Email Generation and Engagement Using Machine Learning

By customizing email content to each user's preferences, this study investigates the implementation of a personalized email communication system with the goal of improving customer engagement. Due mostly to generic and irrelevant content, traditional email marketing frequently encounters issues like low open rates and little user interaction. In order to address this, the study makes use of user response data to comprehend particular preferences and provide personalized, context-aware email content. A variety of machine learning techniques were applied, such as Random Forest for engagement prediction, Decision Trees for user segmentation, Support Vector Machines (SVM) for user preference classification, and sophisticated deep learning models like Transformers (BERT, GPT) for creating customized content. The system was created to dynamically modify email content according to demographic data, interaction patterns, and user behavior. The effectiveness of data-driven customisation was demonstrated by the results, which indicated a significant improvement in user engagement with higher open and response rates. This study provides insights into increasing customer happiness through tailored communication and shows how machine learning models may be used to optimize email marketing campaigns.