# Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis

arXiv: https://arxiv.org/abs/2604.22756 · HTML: https://mlsanity.com/paper/2604.22756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

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

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators](https://mlsanity.com/paper/2403.16950/) (2024)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG](https://mlsanity.com/paper/2505.17156/) (2025)

## Abstract (arXiv)

Conjoint analysis is a cornerstone of market research for estimating consumer preferences; however, traditional methods face persistent challenges regarding time, cost, and respondent fatigue. To address these limitations, this study proposes a framework that utilizes large language model (LLM)-based "customer digital twins (CDT)" as virtual respondents. We identified active users within the Reddit community and aggregated their comprehensive review histories to construct individualized vector databases. By integrating retrieval-augmented generation (RAG) with prompt engineering, this study developed customer agents capable of dynamically retrieving and reasoning upon their specific past preferences and constraints. These customer agents, called CDTs, performed pairwise comparison tasks on product profiles generated via fractional factorial design, and the resulting choice data was analyzed to estimate part-worth utilities by logistic regression. Empirical validation demonstrates that these CDTs predict the preferences of actual users with 87.73% accuracy. Furthermore, a case study on the computer monitor category successfully quantified trade-offs between attributes such as panel type and resolution, deriving preference structures consistent with market realities. Ultimately, this study contributes to marketing research by presenting a scalable alternative that significantly improves both agility and cost-efficiency to traditional methods.