Survey data suggests that 40% of retail consumers in the US to have experiences with chatbots. According to a recent Gartner report, 31% of interviewed organizations already had, or were in the short-term planning for introducing conversational platforms. On the basis of the findings, we discuss the strengths and limitations of the framework, its theoretical and practical implications, and directions for future work.Ĭhatbots are increasingly seen as a valuable complement to customer service. Through the case findings, we show how the framework may provide insight into key drivers of user experience, including response relevance and dialogue helpfulness (Case 1), insight to drive chatbot improvement in practice (Case 2), and insight of theoretical and practical relevance for understanding chatbot user types and interaction patterns (Case 3). We present the framework and illustrate its application with insights from three case examples. The framework has been developed across several studies involving two chatbots for customer service, in collaboration with the chatbot hosts. Motivated by this, we present a framework for qualitative analysis of chatbot dialogues in the customer service domain.
#Customer service chatbot for retail how to#
However, there is a need for knowledge of how to make use of these data. Dialogue data from interactions between users and chatbots represents a potentially valuable source of insight into user experience. For such chatbots, user experience in particular concerns whether the user is provided relevant answers to their queries and the chatbot interaction brings them closer to resolving their problem. The uptake of chatbots for customer service depends on the user experience.
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