An Industrial Application of Soft Computing for the Design of Personalized Call Centers

In service industries such as telecommunications, hotels, insurance, banking, retail, or medical services, companies are increasingly paying more attention to human-computer communication systems that are in direct contact with customers, and focused on achieving the desired profit and market share goals. For this reason, chatbots are increasingly used in service industries starting with simple chat conversation up to more complex functionalities based on soft computing methodologies. Evaluation methodologies for chatbots try to provide an efficient means of assessing the quality of the system and/or predicting the user satisfaction. In this paper we present a clustering approach to provide insight on whether user profiles can be automatically detected from the interaction parameters and overall quality predictions, providing a way of corroborating the most representative features for defining user profiles. We have carried out different experiments for a practical dialog system, from which the clustering approach provided an efficient way of easily distinguishing between different user groups and complete a more significant evaluation of the system.