AI-driven synthetic user interviews can uncover deep emotional insights that real users might not share with a stranger. However, they fail to capture unique, real-life situational problems (e.g. a parent escaping a toddler), making a hybrid research approach essential for a complete picture.

Just as one human interview can go off-track, a single AI-generated interview can produce anomalous results. Running a larger batch of synthetic interviews allows you to identify outliers and focus on the "center of gravity" of the responses, increasing the reliability of the overall findings.

Synthetic users, like a stranger at a bar, can provide unfiltered, emotionally rich feedback during simulated interviews. This happens because there's no social barrier or fear of judgment, leading to the discovery of edge cases and deeper motivations that real users might not share with a human interviewer.

Unlike traditional desk research which finds existing data, generative AI can infer responses for novel scenarios not present in training data. It builds an internal "model of human nature," allowing it to generate plausible answers to new questions, effectively creating research that was never done.

A UK startup has found that LLMs can generate accurate, simulated focus group discussions. By creating diverse digital personas, the AI reproduces the nuanced and often surprising feedback that typically requires expensive and slow in-person research, especially in politics.

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.

To test complex AI prompts for tasks like customer persona generation without exposing sensitive company data, first ask the AI to create realistic, synthetic data (e.g., fake sales call notes). This allows you to safely develop and refine prompts before applying them to real, proprietary information, overcoming data privacy hurdles in experimentation.

Instead of manually sifting through overwhelming survey responses, input the raw data into an AI model. You can prompt it to identify distinct customer segments and generate detailed avatars—complete with pain points and desires—for each of your specific offers.

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'.