Continuous Data-driven Personas Generation: An LLM-based Knowledge Graph Approach
Ryota Sugiyama, Hironori Washizaki, Naoyasu Ubayashi, Ryoko Tanahashi, Mai Hirabayashi, Satoshi Okuda, Ken Toriumi · 2025
Business-to-business software systems are inherently specialized and operationally intricate, which make them crucial to develop accurate personas that reflect real end-user requirements throughout the development lifecycle. As user needs continuously evolve over time, it becomes imperative to establish a data-driven framework capable of persistently updating these personas and promptly integrating those changes into the development process to maintain long-term value delivery. Conventional persona generation techniques typically depend on clustering approaches applied to qualitative and quantitative data—a process that is time-intensive, expensive, and requires considerable domain expertise. This study introduces an automated method for continuous persona generation, extracting user requirements from an ongoing stream of user data. The approach utilizes large language models to interpret qualitative inputs and dynamically generate knowledge graphs, enabling real-time insights into shifting user needs. A case study involving inquiry call logs from a customer support center was conducted to validate the method. Results demonstrated that the proposed approach outperformed a traditional clustering-based baseline in approximately 86% of the cases in a question–answering task aimed at evaluating the structuring and retrieval of user requirements. Furthermore, clear insights into user pain points and requiring improvements were provided, reinforcing the effectiveness and practical utility of continuous, data-driven persona generation.