# AI-Moderated Interviews for Market Research and Digital Twins Calibration

arXiv: https://arxiv.org/abs/2609.29143 · HTML: https://mlsanity.com/paper/2609.29143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [Clio: Privacy-Preserving Insights into Real-World AI Use](https://mlsanity.com/paper/2412.13678/) (2024)
- [Digital Twins as Funhouse Mirrors: Five Key Distortions](https://mlsanity.com/paper/2509.19088/) (2025)
- [SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery](https://mlsanity.com/paper/2602.21136/) (2026)
- [Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs](https://mlsanity.com/paper/2503.01870/) (2025)
- [Codebook Reduction and Saturation: Novel observations on Inductive Thematic Saturation for Large Language Models and initial coding in Thematic Analysis](https://mlsanity.com/paper/2503.04859/) (2025)

## Abstract (arXiv)

AI-moderated interviews are emerging as a scalable market-research method for generating consumer insights and building consumer "digital twins." Yet it remains unclear whether they match human-moderated interviews or improve on simpler, static data collection methods. In a pre-registered, between-subjects study (N = 317) with three industry partners, we compare AI-moderated (N = 139), human-moderated (N = 24), and static interviews (N = 154). AI moderation matches human moderation in depth, covers more themes, and, holding budget constant, recovers significantly more customer needs than human moderation or static interviews. However, participants sound more emotionally engaged when speaking to a live human. We then create digital twins using interview data and evaluate each twin against the participant's own held-out responses to six real-world marketing stimuli. We find that digital twins created from AI-moderated interviews predict consumer responses better than demographics-only personas. However, the additional richness from AI moderation does not translate into better quantitative predictions compared to static interviews. By analyzing open-ended thoughts generated from humans versus their twins, we find that prediction errors are connected both to differences in (self-reported) thinking styles between twins and humans, and to gaps between training and validation data (i.e., asking questions that are too far out of distribution).