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Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.

This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.

The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.

The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.