Persona Generation from User Logs and Inquiries: Identifying Pain Points Beyond Actual Inquirers

Past methods for generating personas have primarily relied on manual assumptions, which have made the automated identification of system-level pain points challenging and have often limited the scope to actual inquirers who provided feedback. To address these limitations, we introduce a framework that combines user logs and inquiry data to generate personas in a more automated manner by integrating behavioral patterns and pain points. The framework consists of three phases: clustering users with k-means, analyzing those clusters with PrefixSpan, random forests, and BiLSTM models to identify pain points for both actual and potential inquirers (that is, actual inquirers—users who submitted feedback—and potential inquirers—users who did not reach out to developers regarding issues), and developing personas through large language models. A case study conducted on Japanese B2B SaaS software, involving 574 users and 3.5 million logs, showed that the generated personas were typically easy to conceptualize, and the identified pain points were compelling and actionable, underscoring their significance in guiding software enhancements.