Methodology for Implementing Self-Learning Feedback Models in CRM Systems: Comparative Analysis of Effectiveness

The article proposes a methodology for implementing self-learning feedback models in Customer Relationship Management (CRM) systems. It examines the key issues of existing CRM systems, including insufficient adaptability to changes in customer behavior and limited capabilities for automatic data analysis. Based on an analysis of modern machine learning approaches, a comprehensive model for implementing self-learning algorithms has been developed, built on a three-tier architecture: data collection and processing, analytical processing, and adaptive interaction. It has been established that the proposed methodology increases the accuracy of customer behavior prediction by 27.8% compared to traditional static models. Experimental implementation in three companies of different scales demonstrated a sales conversion growth of 15-23% and an increase in customer retention rates by 18-32%. A comparative analysis of the effectiveness of different types of self-learning models (deep learning, gradient boosting, ensemble methods) in the context of CRM systems was conducted. Optimal model configurations for various business scenarios and company sizes were identified. The study found that hybrid models, combining the advantages of different machine learning approaches, are the most effective. The scientific novelty of this research lies in the development of a unified implementation methodology that considers the specifics of CRM systems and ensures their evolutionary development with minimal disruptions to business processes. The practical significance of the study is confirmed by the developed recommendations and implementation templates for various business sectors.