Agricultural & Food Consumer Research

Synthetic consumer research uses AI-powered digital twins—statistically accurate simulations of real consumer populations—to answer research questions. Unlike traditional panels that require recruiting and surveying real people over weeks, synthetic research delivers validated insights in hours. The approach is built on peer-reviewed methodology from leading universities including Harvard, Cambridge, and Stanford.

Commodity groups and agricultural trade associations use consumer research to understand perception of their commodity versus imports or substitutes, test marketing campaign messaging before launch, build compelling cases for retail buyers, track consumer attitudes over time, and measure the effectiveness of checkoff-funded programs. Our platform makes this research accessible and affordable for organizations of all sizes.

Our digital twin methodology achieves high correlation with traditional panel research at a fraction of the time and cost. Each population is built on official census data, validated against real-world market outcomes, and continuously updated. We provide transparent methodology documentation and can run parallel validation studies against traditional research when required.

Traditional consumer panels typically take 4-8 weeks and cost $50,000+ for a single study. Digital twin research delivers comparable insights in days at significantly lower cost. The key advantages: faster iteration (test multiple concepts in the time it takes to field one traditional study), global reach (same methodology across 14 countries), and always-on access (no need to re-recruit for follow-up questions).

We help with consumer perception studies for commodities (rice, dairy, beef, produce), message testing for agricultural marketing campaigns, competitive positioning analysis, price sensitivity research, sustainability claim validation, farmer adoption studies for agribusiness, and retail buyer influence research. If it involves understanding how people make decisions about food and agriculture, we can help.

Every digital twin population starts with official census and statistical data to mirror real demographics. We use versioned population bases with transparent weighting, test for out-of-sample prediction accuracy, and validate against actual market outcomes where data exists. This means your research reflects how real consumers actually behave, not just what a model predicts.

We use multiple bias-reduction techniques: debiased question wording, randomized prompt variants, diverse personality distributions across the synthetic population, and 'skeptic checks' that challenge conformity. We also offer optional human validation workflows for regulated decisions or when stakeholder assurance is required.

Yes. We can run parallel studies using both synthetic and traditional panels to demonstrate correlation for your specific use case. Many clients start with a validation study before moving to always-on synthetic research. We're transparent about methodology and happy to discuss the peer-reviewed science behind digital twin research.