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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.
Sera et al. (Sun,) studied this question.