AI Chatbot with Emotional Recognition for an Improved Customer Service Experience
Jeremy Jared Alcala Ocxas, Fabian Kevin Chagua Martin, Alejandrina Nelly Huarcaya Junes · 2024
In the telecommunications sector, customer dissatisfaction with automated service systems is a significant issue, as these systems often fail to effectively respond to users' emotional needs. This study presents the development and implementation of an intelligent chatbot designed to address this challenge by improving customer service through emotional recognition using advanced text mining and artificial intelligence techniques. The chatbot provides fast, accurate, and personalized responses, reducing wait times and enhancing the customer experience. To assess its effectiveness, a case study was conducted at SOE INDUSTRIAL E.I.R.L., collecting qualitative and quantitative data through post-conversation surveys and interaction logs. The objective was to understand the chatbot's impact in a real-world customer service environment. During the initial trial period, a significant improvement in customer satisfaction was observed, with an average customer experience score of 4.66 out of 5. The average response time was recorded at 2993.98 milliseconds, and the first-contact resolution rate was 68.97%. Considering reinteractions on the same issue, a total effectiveness rate of 86.21% was achieved. These results demonstrate that the chatbot is effective in improving service and consultation processes in the telecommunications sector.