Development of Data-driven Persona Including User Behavior and Pain Point through Clustering with User Log of B2B Software

Persona --- fictional user profiles --- are used to identify user requirements in software engineering. However, methods targeting revisions, especially for existing B2B services, remain sparse. This paper proposes a method that integrates several models, including k-means clustering, term frequency-inverse document frequency (TF-IDF), and generative AI. Users' behavior tendencies, pain points, and other attributes are output solely from clickstream log data, bypassing the traditional survey-based approaches of previous studies. Clickstreams are vectorized and categorized, whereas users are further analyzed on the basis of time and content of their clickstreams. A case study was conducted with evaluations carried out both quantitatively and qualitatively. The results suggest that, although some parameters still need improvement, fairly rated persona outcomes were attained.