부산대학교 도서관

CRM automation, is analytic as well as very important aspect of modern businesses, Effectual CRM can help organizations improve customer satisfaction, retention, and sales. However, managing customer relationships manually can be time-consuming and vulnerable. This has led to the development of CRM automation tools that can rationalize and optimize CRM processes. Machine learning algorithms have emerged as spring up tools for automating CRM processes. Machine learning algorithms have the tendency to survey large datasets of customer information and learn from patterns to make predictions which can also lead to developing alterations for the problem that gets highlighted with the help of these machine learning algorithms. This research paper provides an overview of the use of machine learning algorithms in CRM automation. The paper encompasses and discusses various types of variations in CRM automation, including customer attributes, purchase history, and social media activity. Machine learning algorithms can examine these data sources to predict customer behaviour, identify upselling and cross-selling opportunities, and improve customer segmentation. One of the key advantages of using machine learning algorithms in CRM automation is their ability to capability to improve over time. Machine learning algorithms can learn from customer interactions and feedback to improve their accuracy and effectiveness. One of the major challenges is the quality and availability of data. Inconsistent data quality, missing data, and biased data can significantly impact the accuracy of machine learning models. Machine Learning algorithms used in CRM automation Regression algorithms, classification, K – means clustering, Support Vector Machines (SVM). In conclusion, this research paper highlights the potential of machine learning algorithms for automating CRM processes. The use of machine learning algorithms can help organizations enhance their CRM efforts, improving customer relationship.