It is commonly acknowledged that a positive customer experience is essential to maintaining a competitive advantage. Delivering relevance to each customer at the right time through personalising interactions, information, and the customer experience is a fundamental com- ponent of an excellent customer experience. However, it is becoming more and more difficult to personalise interactions and enable a personal touch due to increased digitalisation and a decline in human contact in customer service. When companies automate their customer service through the use of technologies such as conversational artificial intelligence (AI), the lack of a human touch and conversational care often gets exacerbated.

This research aims to examine how a personal touch can be facilitated in text-based com- munication by enabling interpersonal communication and addressing a customer’s affective experience. Therefore, the research draws upon the advancements of conversational AI and Natural Language Processing (NLP). First, the research provides a thorough background of the core concepts of customer experience, personalisation and conversational AI in customer service to reveal the research gap and derive the research questions. To answer the research questions, the exploratory sequential design of the mixed-methods approach is applied, utilising both qualitative and quantitative studies.

In a first qualitative study, a conceptual framework is developed to better understand the relationship between personalised affective communication and the affective customer experience in digital customer service. Therefore, the theory of relational personalisation is adapted with the social information processing theory for the context of text-based commu- nication. Building on a systematic literature review, the conceptual framework delineates propositions which suggest that conversation styles such as empathy, small talk and lexical diversity need to be personalised based on psychological and individual customer knowledge such as emotions and relational history. The developed propositions are subsequently tested with two quantitative studies. The first study uses a large-scale dataset of real-life customer service interactions from social media and tests how relational conversation styles impact the affective customer experience in human-to-human interactions. The second quantitative study builds upon the findings of the previous studies and carries out an experiment with a generative AI chatbot to investigate how relational conversation styles impact the affective customer experience in AI-performed interactions.

The results show that cognitive empathy considerably improves the affective customer experience in both chatbot and human interactions, across different industries. However, affective empathy has been found to have a negative effect on the affective customer experi- ence. Similarly, regardless of the interaction type (human or AI), expressed small talk and a lack of lexical diversity have a negative effect. The findings were further delineated as it was found that the effect of the expressed relational conversation styles depends on the stage of the conversation they are expressed in as well as on the initial customer emotion and relational history.

The research holds significant theoretical and practical implications. By integrating social information processing theory to extend the application of relational personalisation to text-based communication the research provides a new perspective on conversational AI ap- plication design in customer service. The novel conceptual framework further shifts the focus of personalisation towards language and linguistic styles, filling a gap in existing research. The quantitative studies challenge and refine existing theories, such as the role of small talk and lexical diversity in customer service, adding new dimensions to the understanding of digital interactions. The findings offer actionable insights for practitioners to enhance digital customer service by incorporating personalisation strategies into service scripts and chatbots. It advocates for the training of human agents in recognising and adapting to customers’ emotional states and conversation contexts, promoting the use of diverse lexicons and less scripted, more empathetic responses. For AI, it highlights the benefits of using advanced Natural Language Processing and generative AI technologies to analyse customer data and autonomously adapt conversation styles, enhancing the efficiency and quality of customer service.