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

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.

More from arXiv cs.AI

Towards Understanding the Expressive Power of GNNs with Global Readout

arXiv cs.AI·2

Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark

arXiv cs.AI·3

Probing Visual Planning in Image Editing Models

arXiv cs.AI·4

IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review

arXiv cs.AI·3

MAE-Based Self-Supervised Pretraining for Data-Efficient Medical Image Segmentation Using nnFormer

arXiv cs.AI·4

SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving

arXiv cs.AI·2