Exploring the Application of Machine Learning for Automatic Inbound Email Classification in CRM System at XYZ Company - Lukman Arif Sanjani; Bimo Mandala Putra, Raden; Laili Yuhana, Umi (Journal of Technology and Informatics (JoTI), 2024)
Customer service has become increasingly crucial in today's business landscape, necessitating companies to provide fast, responsive, and personalized assistance to their clientele. However, amidst the challenges posed by surges in email volume, manual categorization and response strategies often lead to performance declines. To address this, we propose a system leveraging Machine Learning techniques for automated email classification. Our evaluation reveals promising results, with SVM achieving the highest accuracy of 96.59%, followed by XGB (96.02%) and RF (95.27%). These models exhibit commendable precision, recall, F1 scores, and Matthews Correlation Coefficient (MCC), showcasing their effectiveness in improving customer service efficiency and responsiveness. This integration of technology not only enhances operational efficiency but also fosters harmonious customer relationships, ultimately leading to increased loyalty and profitability for companies.