用大模型模拟用户，高效精准预测消费偏好。
Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis
- 用LLM构建用户数字孪生，基于历史评论生成个性化虚拟用户。
- 虚拟用户预测真实用户偏好准确率达87.73%，可量化属性权衡关系。
- 适合想低成本快速获取消费者偏好的市场研究者使用。
联合分析是市场研究中估算消费者偏好的核心方法，但传统方法存在耗时、成本高和受访者疲劳等问题。本研究提出利用大语言模型（LLM）构建“客户数字孪生（CDT）”作为虚拟受访者。我们选取Reddit社区中的活跃用户，整合其完整的评论历史，构建个性化向量数据库。通过检索增强生成（RAG）与提示工程结合，开发出能动态检索并推理其过往偏好与约束的客户代理。这些客户代理在分数因子设计生成的产品配置上进行成对比较，通过逻辑回归分析其选择数据，估算部分效用值。实证验证表明，这些CDT对真实用户偏好的预测准确率达到87.73%。此外，在电脑显示器品类的案例研究中，成功量化了面板类型与分辨率等属性间的权衡关系，推导出与市场现实一致的偏好结构。本研究为营销研究提供了可扩展的替代方案，显著提升传统方法的敏捷性与成本效率。
原文摘要 · Abstract (English)
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.
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