Development of Data-driven Persona Including User Behavior and Pain Point through Clustering with User Log of B2B Software
Rie Sera, Hironori Washizaki, Junyan Chen, Yoshiaki Fukazawa, Masahiro Taga, Kazuyuki Nakagawa, Yusuke Sakai, Kiyoshi Honda · 2024
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.