Synthetic data is quickly becoming one of the most discussed topics in market research. At the same time, it is one of the most misunderstood.
In a recent webinar, we walked through how synthetic data is used inside QuestionPro and opened the floor to questions from research, CX, healthcare, and enterprise teams. What stood out was not skepticism about the idea itself, but a desire for clarity:
- How accurate is synthetic data?
- When does it work well?
- Where are its limits?
- And how do you know it is not just “made up”?
Below, we answer the most common questions from that session, using the same principles we demonstrated live: transparency, grounding in real data, and responsible application.
How can synthetic data be valid if it is not collected from real people?
This is often the first concern, and rightly so.
Synthetic data should not be treated as a replacement for real human responses. Its validity comes from how it is generated.
In QuestionPro, synthetic data is grounded in:
- Real communities
- Real survey responses
- Known respondent profiles and attributes
The model does not invent opinions or create fictional people. Instead, it learns patterns from existing data and generates plausible responses that reflect those patterns.
Teams validate synthetic output by:
- Comparing it to known findings
- Using it to form hypotheses
- Following up with qualitative or quantitative research
Synthetic data is most powerful when used as a directional tool, not a final answer.
How do you account for changes in consumer behavior?
Synthetic data reflects the data it is built on.
If consumer behavior changes, your synthetic output should change too. That is why continuously refreshed data matters.
Teams using communities or ongoing survey programs are especially well positioned. As new responses are collected, synthetic output updates to reflect current behavior rather than historical assumptions.
How is quality measured in synthetic data?
Quality is not measured by perfection, but by usefulness.
High quality synthetic data:
- Aligns with known patterns
- Makes logical sense to domain experts
- Helps researchers ask better questions
- Improves the design of real studies
If synthetic data leads to clearer hypotheses, stronger qual guides, or more focused surveys, it is doing its job.
How does synthetic data work in healthcare research?
Healthcare is one of the strongest use cases for synthetic data because access to respondents is often limited and privacy is critical.
When grounded in vetted healthcare communities and compliant survey data, synthetic data can support:
- Early exploration of care pathways
- Messaging and education testing
- Understanding decision drivers
All while minimizing exposure to sensitive individual information.
How does AI “get inside respondents’ heads”? Isn’t that unrealistic?
It can sound almost psychic, but the reality is much more practical.
AI does not read minds. It identifies patterns.
By analyzing structured data, past responses, and respondent attributes, the model generates responses that are statistically and contextually likely. It is closer to pattern recognition than prediction.
How realistic is synthetic data?
When built responsibly, synthetic data often feels realistic because it is anchored in real experience.
However, realism does not equal truth. Synthetic responses are plausible, not confirmed.
That distinction is important, and it is why synthetic data should inform research, not replace it.
How do you address skepticism about authenticity?
Skepticism is healthy.
The most effective way to address it is transparency. Inside QuestionPro, teams can see:
- Where the data comes from
- How respondents are matched to topics
- How interviews and reports are generated
This removes the “black box” concern that often surrounds synthetic data.
Can synthetic data uncover completely new insights?
Synthetic data reflects patterns present in the source data. It cannot invent phenomena that do not exist.
This limitation is not a flaw, but a boundary.
Synthetic data is best used to:
- Explore existing themes
- Surface likely perspectives
- Accelerate early discovery
Truly new insights still require real-world data collection.
Is synthetic data a black box?
It does not have to be.
In QuestionPro, the workflow is explicit:
- Select a community
- Define the research purpose
- Match respondents
- Generate interviews
- Review outputs
As synthetic capabilities evolve, transparency remains a priority so teams understand not just the output, but the process behind it.
We already have surveys and a community. How do we use that data?
You do not upload raw files into a generic model.
The platform uses:
- Existing survey responses
- Community profiles
- Behavioral and demographic attributes
All within the QuestionPro environment, ensuring continuity and context.
What business problems are best suited for synthetic data?
Synthetic data works especially well for:
- Early-stage exploration
- Concept framing
- Messaging discovery
- Persona development
- Research planning
It is less suited for final measurement or regulatory decisions.
Which research methods benefit most from synthetic data?
Synthetic data is most accurate for:
- Concept exploration
- Idea screening
- Early prioritization
- Qual guide development
Final statistical methods still require real respondent data.
How confident can teams be in synthetic output?
Confidence depends on intent.
When used to guide direction, confidence is high. When treated as final truth, confidence should drop.
This balance is intentional and responsible.
Final thoughts
Synthetic data is not about replacing researchers. It is about giving them better starting points, faster learning cycles, and more informed decisions. If you are already collecting data or managing a community, synthetic data can help you extract more value from what you have today.
If you would like to explore how this could work in your organization, our team is happy to continue the conversation.
