With the rapid advancement of digital technology and social media, virtual digital humans and hyper-realistic virtual idols have demonstrated immense commercial potential and application value. Taking virtual idol Liu Yexi as a case study sample, this research employs a multi-faceted approach integrating literature review and questionnaire surveys to delve into audience characteristics and interaction preferences. Employing a two-stage clustering algorithm combining Self-Organizing Map (SOM) neural networks with K-means clustering algorithm, the audience is segmented into four distinct groups: the highly educated and rational type prioritizing logic and aesthetics; the young and fervent type exhibiting strong emotional dependency; the mature and pragmatic type with demanding requirements; and the socially driven type seeking recognition. Based on these findings, user personas are constructed centered around four primary markets: the core emotional monetization market, the premium content experience market, the long-term Intellectual Property (IP) oversight market, and the broad social viral market. These markets are further segmented. The research aims to reveal the group characteristics of different market segments, providing guidance for developing refined operational and targeted marketing strategies for virtual digital human IPs. This seeks to advance audience expansion and commercialization within the virtual digital human industry.
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Share and Cite
Lin, Q. (2025) User Profiling and Market Segmentation of Virtual Idols: A Cluster Analysis Based on Virtual Idol Liu Yexi. Journal of Social Development and History, 1(6), 166-172. https://doi.org/10.71052/jsdh/FISI9972