The market research industry keeps evolving through technology. Advances in respondent verification and panel quality have strengthened our ability to gather authentic insights. Meanwhile, synthetic research has emerged as an intriguing new option, and understanding the types of synthetic data is the first step to using it well.
But in an industry built on understanding real human behaviour, what role should synthetic data play? Let’s look at the reality behind the buzz, starting with the different types of synthetic data.
The Three Types of Synthetic Data
Not all synthetic data is created equal. Three distinct approaches are emerging in market research. To understand the opportunities and pitfalls, we first need to make sure we’re all talking about the same thing.
1. Statistical Modeling
Statistical weighting of respondent data isn’t new. The industry has used it for decades to make survey data more representative. Statistical modeling takes that concept further.
Instead of only adjusting existing responses, statistical modeling can expand your dataset, turning 500 responses into 1,000, for example. In effect, it creates synthetic respondents based on real ones. AI-powered modeling takes this a step beyond traditional weighting.
2. Digital Personas
Digital personas are AI-powered audience profiles built from large language models (LLMs). These synthetic respondents aren’t random. Instead, they’re carefully built representations of your target audience, based on millions of known data points.
Within seconds, these personas can surface likely themes and points of interest around products, services, and more. That makes them especially useful for early-stage ideation and questionnaire development. You can test concepts before investing in full-scale research.
Personas don’t replace conversations with real people. But they can point to promising themes to explore and help shape new research projects.
3. The Hybrid Approach
The hybrid approach blends statistical modeling with insights from digital personas. It combines quantitative rigor with qualitative depth. Statistical modeling expands the dataset, while digital personas add context and nuance.
This approach can be especially powerful for companies with large proprietary and primary datasets. Incorporating that data into the model can improve accuracy even further.
The Power of Statistically Modeled Synthetic Data
Of the types of synthetic data, statistical modeling is the most relevant for quantitative research. It’s quite different from the AI-generated “digital twins” used in qualitative work.
Consider a real-world challenge. You’re researching Hispanic Americans and need representative data across countries of origin. Mexican-American respondents may be easy to find, since they make up a larger share of the U.S. population. But reaching enough Colombian-American respondents is often much harder.
Synthetic respondents built through statistical modeling can thoughtfully amplify these underrepresented voices while maintaining mathematical validity and research integrity.
Synthetic Data Guidelines for Reliable Results
Before you explore these possibilities, it’s important to understand what success requires. Here are the synthetic data guidelines we recommend:
- Start with a strong foundation. You need real respondents for about 60% of your target sample size before adding synthetic respondents.
- Follow statistical principles. The traditional minimum of 30 respondents per segment still applies.
- Pay attention to precision. Margin of error matters even more, especially in B2B research, where populations are often smaller.
- Remember that quality is connected. “Garbage in, garbage out” applies here. Modeling amplifies both the strengths and the weaknesses of your source data.
- Keep your methods rigorous. Good survey design and data collection still matter most. No model can fix flawed research design.
Choosing the Right Type of Synthetic Data
The right approach depends on your research needs:
- Statistical modeling works best for quantitative research. Use it to amplify hard-to-reach demographics or boost sample sizes for stronger statistical significance.
- Digital personas suit early-stage concept testing and questionnaire development, when you want quick, cost-effective feedback.
- A hybrid approach fits when you need to expand your dataset and add qualitative depth.
Whichever you choose, success in synthetic research takes experience and a solid grasp of research fundamentals. It follows the same quality rules and statistical validation as traditional research. Like cooking, you need both quality ingredients and expertise to get the best result.
Our Approach: Test First
At Quest Mindshare, we take a measured approach to synthetic research. Rather than rushing to market with a new offering, we’re testing it independently to understand its strengths and limits. We’re one of the few companies independently testing synthetic data in market research.
Our testing explores key questions:
- How accurate is it across different audiences?
- Does it work equally well across countries and cultures?
- What are its applications in B2B research?
- How does it perform with emerging groups like Gen Z?
The Bottom Line
This technology isn’t a magic solution. But it is an important step forward in research methodology. The key is following clear synthetic data guidelines and knowing when to use it. Like any tool, its value depends on how you apply it and the expertise behind it.
Have more questions about synthetic data? Download our Real Potential of Synthetic Data in Market Research FAQ for answers to the questions researchers ask most.
