Development of an Information System for Analyzing Customer Feedback for Effective Reputation Management

Abstract

In the digital era, customer feedback plays a crucial role in quality management and reputation enhancement. This study focuses on the development of an information system for analyzing customer feedback to help organizations make data-driven decisions. The proposed system integrates Natural Language Processing (NLP), machine learning, and sentiment analysis to process large volumes of unstructured data from multiple sources. By leveraging trend analysis, automated recommendations, and visualization techniques, businesses can identify key areas for improvement and respond to customer needs more effectively. A survey conducted with 52 companies revealed that while most organizations collect customer feedback, only a few systematically analyze and utilize it for strategic decision-making. The findings underscore the necessity of an advanced customer feedback analysis system to improve business operations, customer satisfaction, and market competitiveness. The proposed model offers a comprehensive solution by incorporating automated categorization, sentiment analysis, and an interactive chatbot to streamline feedback management.