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'.
Synthetic data removes limitations imposed by human attention spans. For a Booking.com study, a 30-minute survey with a 75-item question—impossible for human respondents—was used to conduct a novel psychographic segmentation. This allows researchers to explore more variables and territories than traditional methods permit.
AI-powered tools automate the menial tasks of research, like building charts and running cross-tabs. This frees up researchers, even those with PhDs, to focus on higher-value activities: driving strategy, bridging the gap between understanding and action, and making investment recommendations based on insights.
Unlike general-purpose LLMs (e.g., ChatGPT, Gemini) that produce homogenous answers, Qualtrics's specialized model, trained on survey data, replicates the variability and irrationality inherent in human opinion. This results in more realistic data distributions, preventing the false consensus that generic AI models often create.
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.
To integrate AI without sacrificing scientific rigor, teams should categorize research needs into two types. 'Strategic' projects warrant slower, human-powered, multi-phase studies due to the weight of the decision. 'Quick turn' projects, however, are ideal for AI-led methods, enabling rapid insights for less critical but still important use cases.