AUTOMATED ANALYSIS OF CUSTOMER CONTACTS AND FEEDBACK AS A TOOL FOR OPTIMIZING SOCIAL SERVICES

Abstract

The purpose of this article is to examine and substantiate the necessity of implementing automated systems for the analysis of citizen requests and feedback within social service institutions, as well as to define the characteristics of such a model and its interoperability with existing information systems and client portals. The article proposes an integrated model for the automated processing of requests and client feedback based on the conceptual foundations of social service digitalization, principles of automated feedback management, and the analysis of AI-driven customer support systems. The study explored international experience of automating feedback mechanisms in social services in Estonia, the United Kingdom, and Canada. A step-by-step implementation for integrating an automated request and feedback analysis system into the national social protection framework is proposed, taking into account legal and regulatory requirements. The study demonstrates the application of artificial intelligence–based analytics, natural language processing (NLP), SLA monitoring mechanisms, and predictive modeling to enhance managerial decision-making and service personalization, while simultaneously reducing operational costs and response times. The advantages of automated analysis of citizen requests and client feedback are identified in terms of: detecting systemic deficiencies in social service provision; forecasting demand for specific social benefits and client needs; preventing corruption risks through transparency and systematic feedback analysis; improving compliance with service level agreements; and strengthening personalized support for vulnerable population groups. Ethical and organizational considerations, as well as the implications of implementing this approach in the context of social work and counseling, are also discussed