arXiv AI September 15, 2026 By Oded Netzer, Rajan Sambandam

Synthetic Data in Marketing Research: How to Evaluate and When to Trust

The paper discusses the use of synthetic data in marketing research, arguing that the key question is not whether synthetic respondents work, but when they do. It categorizes synthetic data into three types—ungrounded LLM responses, segment-level personas, and individual-level digital twins—and maps each to the decisions they can support. The authors also propose a taxonomy of accuracy measures, highlight the forgotten question problem, and introduce an ex‑ante answerability diagnostic based on R² to improve twin-human correlation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 4

Synthetic Personalities: How Well Can LLMs Mimic Individual Respondents Using Socio-Economic Microdata?

arXiv:2606. 04592v1 Announce Type: cross Abstract: LLM-based digital twins promise to scale and accelerate market research, but most published twins are either coarse persona bots conditioned on a few demographic questions or detailed individual-level twins built on purpose-collected surveys and interview transcripts.

By Leonard Kinzinger, Jochen Hartmann

llms rag

arXiv AI

Jul 31

When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses

arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.

By Zihan Chen, Di Zhu, Lei Nico Zheng

llms benchmarks

arXiv AI

Sep 10

When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability

arXiv:2609.07987v1 Announce Type: new Abstract: LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide lit...

By Steven Wang, Kyle Hunt, Shaojie Tang, Kenneth Joseph

llms

arXiv Machine Learning

4d ago

When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

arXiv:2609.13148v1 Announce Type: cross Abstract: Large language models are increasingly deployed as synthetic consumer panels, promising $97\%$ cost reductions over traditional surveys. Yet aggregat...

By Robson Tigre, Hugo Gobato Souto

llms safety

arXiv AI

Jun 29

Psychometric Comparability of LLM-Based Digital Twins

arXiv:2601. 14264v2 Announce Type: replace-cross Abstract: Large language models (LLMs) act as digital twins for human respondents, yet their psychometric comparability remains uncertain.

By Yufei Zhang, Zhihao Ma

llms benchmarks safety

arXiv AI

Aug 7

Stochastic Parrots or Singing in Harmony? Testing Five Leading LLMs for their Ability to Replicate a Human Survey with Synthetic Data

arXiv:2603. 00059v3 Announce Type: replace-cross Abstract: How well can AI-derived synthetic research data replicate the responses of human participants?

By Jason Miklian, Kristian Hoelscher, John E. Katsos

llms agents

One email a morning, machine-written

Related stories

Synthetic Personalities: How Well Can LLMs Mimic Individual Respondents Using Socio-Economic Microdata?

When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses

When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability

When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

Psychometric Comparability of LLM-Based Digital Twins

Stochastic Parrots or Singing in Harmony? Testing Five Leading LLMs for their Ability to Replicate a Human Survey with Synthetic Data