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Chatbot technology has rapidly spread, especially in digital customer service. However, the automation potential of chatbots can only be realized if customers are satisfied with their service. Collecting explicit feedback is a promising technique for assessing customer satisfaction and identifying issues with the chatbot. It enables chatbot managers and developers to enhance performance and design of operational chatbots on an informed basis. The evident significance of explicit customer feedback comes with a multitude of design options available. However, there is a lack of research on chatbot feedback mechanisms and practical as well as theoretical clarity. In this paper, we address this gap by introducing a chatbot feedback taxonomy derived from existing research and a sample of N = 72 real world customer service chatbots. Furthermore, based on a cluster analysis, we identify four archetypes of feedback mechanisms and provide strategic guidelines for the informed use of each of those feedback design variants.

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“Was This Answer Helpful?”—A Taxonomy for Feedback Mechanisms in Customer Service Chatbots

  • 会议论文

  • 发表日期：

    2026年

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  • Daniel Schloss,
  • Saskia Haug,
  • Alexander Maedche

摘要

Chatbot technology has rapidly spread, especially in digital customer service. However, the automation potential of chatbots can only be realized if customers are satisfied with their service. Collecting explicit feedback is a promising technique for assessing customer satisfaction and identifying issues with the chatbot. It enables chatbot managers and developers to enhance performance and design of operational chatbots on an informed basis. The evident significance of explicit customer feedback comes with a multitude of design options available. However, there is a lack of research on chatbot feedback mechanisms and practical as well as theoretical clarity. In this paper, we address this gap by introducing a chatbot feedback taxonomy derived from existing research and a sample of N = 72 real world customer service chatbots. Furthermore, based on a cluster analysis, we identify four archetypes of feedback mechanisms and provide strategic guidelines for the informed use of each of those feedback design variants.