Chatbots are increasingly used to provide customer service. However, despite technological advances, customer service chatbots frequently reach their limits in customer interactions. This is not immediately apparent to both chatbot operators (e.g., customer service managers) and chatbot developers because analyzing conversational data is difficult and labor-intensive. To address this problem, our ongoing design science research project aims to develop a conversation mining system for the automated analysis of customer-chatbot conversations. Based on the exploration of large dataset (N= 91,678 conversations) and six interviews with industry experts, we developed the backend of the system. Specifically, we identified and operationalized important criteria for evaluating conversations. Our next step will be the evaluation with industry experts. Ultimately, we aim to contribute to research and practice by providing design knowledge for conversation mining systems that leverage the treasure trove of data from customerchatbot conversations to generate valuable insights for managers and developers.