Synthetic data—artificially generated data that mirrors real-world patterns—is gaining traction as a tool for enhancing market research while addressing privacy and compliance challenges. It is already being used in concept testing, product development, packaging and pricing studies, market sizing, and consumer trend research.  

For market researchers, its value lies in delivering scalable, rapid, and privacy-compliant insights, particularly when engaging with niche or hard-to-reach segments. It also supports continuous, always-on research through digital twins and real-time simulations.  

In forecasting, synthetic data helps to mitigate issues arising from data sparsity and signal loss due to privacy restrictions and cold start challenges. However, its ability to model complex human decisions and emotional nuances remains uncertain. Challenges persist regarding data quality, ethical use, bias reinforcement, and regulatory inconsistency. There is also a critical need for industry-wide standards and validation frameworks. Significant questions remain: Can synthetic data replicate the richness and unpredictability of real human behavior sufficiently to support high-stakes forecasting and decision-making? Where is the line between synthetic realism and synthetic bias, and how do we manage it? Is synthetic data a bridge—or a barrier—to more effective and reliable forecasting models?  

MSI and the ARF are excited to co-produce the ANALYTICS & FORECASTING conference for the first time. It will be held at Columbia University’s Business School on September 29-30. This is an opportunity to engage in discussions about the opportunities and limitations of modeling in market research applications and to showcase innovative work from academia and the business sector.

This is a complimentary event exclusively for ARF & MSI Members.   Click here to learn more about membership.