As chatbots are gaining popularity in customer service, it is critically important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis approaches are often limited to the question-answer level or produce highly aggregated metrics (e.g., conversations per day) instead of leveraging the full potential of the large volume of conversation data to provide actionable insights for chatbot developers and chatbot managers. To address this challenge, we developed a novel chatbot analytics approach-conversation mining-based on concepts and methods from process mining. We instantiated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The results of four focus group evaluations suggest that conversation mining can help chatbot developers and chatbot managers to extract useful insights for improving customer service chatbots. Our research contributes to research and practice with novel design knowledge for conversation mining systems.