CRM-системи

The article proposes a state-dynamic approach to the analysis of phase transitions in complex CRM systems (customer relationship management systems), aimed at modeling and predicting mass customer behavior. Based on the analogy between socio-economic processes in interaction with customers and physical phase transitions, a mathematical model is proposed that takes into account critical points of change in behavioral states. Numerical modeling using the Monte Carlo method is used to analyze critical phenomena and determine stability conditions.

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.