AI and Netnography for Effective Customer Research: Apple Vision Pro Case

This study presents the development and empirical validation of a novel methodology — AI-Augmented Netnography — designed to address the key limitations of traditional netnographic research, such as subjectivity, labor intensity, and limited scalability. The methodology combines classical netnographic procedures with the capabilities of large language models (LLM), enabling automated thematic and sentiment analysis of user-generated content (UGC) and the structured extraction of user needs in the framework of Jobs-to-be-Done (JTBD). The research is grounded in the analysis of online user discussions surrounding Apple Vision Pro, a cutting-edge Extended Rrality (XR) device. These discussions serve as a rich empirical field for testing the pipeline’s ability to capture latent user motivations and barriers to adoption. The proposed method introduces a formal procedure for prioritizing identified JTBDs based on lexical specificity and emotional intensity — encapsulated in a metric termed Resonance Score. The practical significance of the work lies in its contribution to digital product development: by producing standardized, reproducible, and quantitatively grounded insights, this method supports faster and more accurate alignment of product decisions with real user expectations. The results demonstrate that AI-augmented netnography not only overcomes the analytical bottlenecks of manual ethnography but also enables scalable integration of user voice into product strategy.