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, current research on chatbot evaluation and feedback lacks both practical and theoretical clarity. In this paper, we adress this gap by introducing a chatbot feedback taxonomy derived from existing research and a sample of N = 72 real world cus-tomer service chatbots. Furthermore, based on a cluster analysis, we identify four archetypes of feedback collection designs and provide strategic guidelines for the informed use of each feedback collection archetype.