Detection of Customer Satisfaction in In-Person Telecommunication Services Using Automated Facial Image Analysis with Artificial Intelligence

In the present era, improving customer experience and enhancing services in the telecommunications industry are of paramount importance. One of the fundamental criteria for evaluating customer experience is their level of satisfaction with services at physical sales points. This paper focuses on the automated analysis of facial images using artificial intelligence to determine the satisfaction level of inperson visitors to telecommunications service booths. Advanced technologies such as transfer learning and the use of the EfficientNet network architecture have been employed to provide a more accurate and efficient method for facial emotion analysis. The results of this study demonstrate that the proposed method significantly improves the accuracy and efficiency of emotion detection and, consequently, the satisfaction or dissatisfaction of visitors in both implementation stages. This research not only contributes to the improvement of service processes and the enhancement of customer experience but also confirms the superiority and reliability of this method by comparing the results with previous approaches. These findings can assist telecommunications businesses in improving services and increasing customer satisfaction, guiding them towards optimized performance and success. This study is introduced as a solution for improving the customer experience process during in-person visits to telecommunications service offices, which has extensive applicability in enhancing communication systems, increasing employee productivity, improving service delivery supervision and management, and enhancing responsiveness and service quality at telecommunications service counters. Additionally, it provides for increased customer satisfaction and loyalty, optimization of internal company processes, improved marketing strategies, and more accurate assessment of customer experience status.