Optimizing Customer Relationship Management (CRM) Systems Using Advanced Machine Learning Algorithms

• Published in: 2025 Seventh International Conference on Computational Intelligence andCommunication Technologies (CCICT) pp. 215 - 220
• Main Authors: Ganesh, C.Naga, Rani, M.Swaroopa, Pradeep, S, Dahiya, Ritu, Gupta, Ankur, Veeraiah, Vivek
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.04.2025
• Subjects: CRM; Customer data; Customer relationship management; Data privacy; Machine learning; Machine learning algorithms; Pattern identification; predictive analysis; Predictive analytics; Predictive models; Real-time systems; Recommendation system; Recommender systems; Solids; Visualization
• DOI: 10.1109/CCICT65753.2025.00042

Using machine learning approaches, this effort aims to enhance CRM systems. Controlling client interactions, increasing client retention, and strengthening sales strategy are all greatly facilitated by customer relationship management systems. This study discusses how machine learning models might improve CRM skills via pattern recognition in consumer data, behaviour prediction, personalised marketing, and natural language processing. Increasing consumer engagement and enjoyment while enhancing data privacy is the goal of research into data integration, algorithm selection, and data privacy protection, all of which contribute to resolving the challenges of using machine learning in CRM. Using state-of-the-art machine learning methods, this research aims to optimise CRM systems. We demonstrate that the suggested machine learning model significantly improves overall performance, accuracy, and error rate in real-time decision-making and predictive analytics by comparing it with more conventional methods. The findings demonstrate that machine learning enhances customer satisfaction and loyalty, leading to better business outcomes. This research provides solid groundwork for future developments in the field, as machine learning has the potential to radically alter CRM systems.