A study with Colgate-Palmolive found that large language models can accurately mimic real consumer behavior and purchase intent. This validates the use of "synthetic consumers" for market research, enabling companies to replace costly, slow human surveys with scalable AI personas for faster, richer product feedback.

Create a business that runs ad tournaments for D2C brands. Use an AI to ingest a brand's actual customer reviews, build detailed customer personas from that language, and then have those personas "judge" dozens of ad concepts overnight. This offers rapid, data-driven feedback at a fraction of traditional costs.

Shopify's new SimGym tool, which uses AI agents to simulate how customers interact with a store, points to a new standard in marketing. Soon, launching a campaign, redesign, or product without first running it through a sophisticated AI simulation will be considered archaic and reckless.

Instead of competing with traditional methods, synthetic research addresses the vast number of decisions made without data due to time or budget constraints. It quantifies the risk of acting on intuition alone, filling a critical gap where research was previously unfeasible, thus lowering the 'cost of doing nothing'.

It's impossible to generate human data at the scale of in silico experiments. The key is to create highly accurate simulations of human physiology (digital twins) and then validate their predictions with limited, strategic human data. If the model proves reliable, it could drastically accelerate R&D.

Modern physician segmentation in the pharmaceutical industry has moved far beyond potential and product adoption. Leading US companies now use up to 79 parameters—including beliefs, motivators, and barriers—to build complex personas. This enables hyper-personalized engagement strategies tailored to each physician's unique context.

Conquer's Farsight Twin can predict a patient's response to a novel drug, standard of care, and the combination therapy separately. This allows pharma companies to determine if a positive response in an early-phase trial is truly driven by their new asset or just the background therapy, providing crucial efficacy data.

A key application for synthetic research is exploring questions that arise after a traditional, human-powered study is complete. Instead of launching a new project, researchers can quickly run a few follow-up questions with a synthetic audience. This provides directional answers to stakeholder queries without the cost and delay of re-fielding a survey.

Expect 2026 to be the breakout year for synthetic data. Companies in highly regulated sectors like healthcare and finance are realizing it offers a compliant and low-risk method to test and train AI models without compromising sensitive customer information, enabling innovation in marketing, research, and CX.