THE IMPACT OF AUTOMATION AND DATA PROCESSING ON THE EFFICIENCY OF CUSTOMER INTERACTION IN CRM

The rapid development of digital technologies and the exponential growth of data volumes are transforming approaches to customer relationship management, requiring the implementation of comprehensive automated solutions. The main problem remains the fragmentation of information flows between organizational departments and insufficient integration of analytical tools to create a unified customer profile. The lack of effective mechanisms for processing unstructured data limits the possibilities for proactive response to changes in consumer behavior. In this regard, the implementation of intelligent automation systems that optimize customer interaction at all stages of the life cycle is becoming particularly relevant. The research focuses on the integration of machine learning algorithms into CRM systems, in particular the use of LSTM neural networks and gradient boosting for analyzing customer behavioral patterns. The architectural evolution from monolithic structures to distributed microservice models is considered, which ensures adaptability and scalability in the context of business environment fluctuations. Based on the analysis of technological solutions, a list of promising approaches has been formulated, including federated learning, Process Mining and Continuous Intelligence technologies, which ensure a balance between analytical power and maintaining the confidentiality of personal data. Global digital transformation initiatives have been analyzed, which form the regulatory foundation for the implementation of innovative mechanisms for processing customer data. The integration of quantum computing and neuromorphic processors into CRM systems significantly increases the speed of analytical operations, providing complex query processing in real time. The conclusion is made about the feasibility of creating adaptive data processing architectures, optimized for balancing between performance and energy efficiency in accordance with the contextual requirements of the business environment