Emotional Artificial Intelligence: Detecting and Managing Customer Emotions in Automated Customer Service

Among the distinctive features of the human race are the ability to feel emotions and to be empathetic with others. These features are strictly related to the concept of emotional intelligence (EI). In this thesis, the skills of EI have been explored in the context of automated customer service, to achieve effective customer engagement through the emotional reading of their needs and moods. Contact center operators are often trained to detect different emotional states and connect empathically with customers, to engage them in new commercial offers or solve their main problems both in the presales and post-sales processes. Frontline employees (FLEs) use their empathetic skills to prevent negative emotions and transform complex issues into positive solutions for the customer. Emotional awareness and empathy are important assets in customer relationship management (CRM) to establish the customer’s loyalty and advocacy towards the firm in a logic of value co-creation. Customer service automated systems see artificial intelligence (AI) become part of this scenario with a consequent loss of empathic capacity in the interaction between customers and firms due to an incorrect reading and managing of customer emotions. The aim of this thesis is to evaluate how a customer service AI technology called chatbots affect this interaction and detect customer emotions, expectations, and service quality perceptions effectively. This work develops a new conceptual framework that combines the skills of emotional intelligence (EI) with those of current AI-powered chatbots already operating in many customer service systems. The emotional artificial intelligence (EAI) framework represents a possible way for a chatbot to know when a human agent must intervene to handle a complicated conversation with the customer without a loss of empathic capacity of the firm. .. [edited by Author]