Naavik Gaming Podcast Naavik Digest: LLMs for Games Consumer Research
Mar 29, 2026
A tour of how large language models can mirror player behavior for consumer research. They describe studies that clone people into generative agents and methods to rate purchase intent from free text. Practical tools and reproducible code are highlighted. Real-world tests on icons and screenshots show synthetic rankings aligning with actual performance.
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Interviews Create Highly Accurate AI Human Simulations
- LLMs can emulate human attitudes and behaviors by using rich interview transcripts as memory rather than simple demographic prompts.
- The 1,052-agent study achieved ~85% survey accuracy and ~80% Big Five correlation by using two-hour life-story interviews and expert reflections.
Build Interview-Based Agent Banks For Early Validation
- Use interview-derived agent banks to validate early design, marketing, and monetization choices before expensive tests.
- Interview gamers about play styles, likes, and spending to build a repository that flags likely problems fast.
SSR Converts Free Text Into Humanlike Purchase Scores
- Semantic Similarity Rating (SSR) maps free-text AI consumer responses to Likert anchors via text embedding distances to avoid neutral 'three' responses.
- SSR matched human survey rankings at over 90% of theoretical accuracy and produced richer rationales.