TL;DR: Synthetic data generation uses AI to produce artificial data sets that imitate real responses, letting marketers model how an audience might behave without fielding a full survey. It's fast and genuinely useful for early concept testing and hard-to-reach groups, but a simulated answer is only as reliable as the human data beneath it. The best simulated data is grounded in real, directly-asked survey responses.
A client emails on Tuesday. They need to know how eco-conscious millennials in North America will react to three new campaign concepts, and they need an answer by Friday. Fielding a fresh survey would take weeks you don't have. So someone suggests generating the responses synthetically instead, and within minutes, you've got a clean, confident answer for all three.
Before sending the recommendation over to the client, the doubts creep in. Is this actually right? Where did these numbers even come from?
That worry is the right instinct. Synthetic data can give you useful answers to questions you haven't asked before. The quality of those answers, however, depend on the data the simulation was built on.
Synthetic data generation is the process of using AI to create artificial data that imitates real-world data. Instead of collecting fresh responses from actual people, a model produces a data set designed to look and behave like the real thing.
For insights teams, this usually shows up as simulated audiences. Ask a synthetic data model how 25-to-34-year-old runners in Germany feel about a new sports drink, and it answers as if it had actually surveyed them. That means you can quickly get insight on ideas that could shape your commercial strategy without waiting on fieldwork.
But synthetic data itself isn't new to data science. It's long been used to protect privacy, fill gaps when real data is scarce or expensive, and balance data sets so models train properly on rare cases, like fraud.
What's new is how easily it now stands in for consumer research, and how convincing the results can look even when the underlying evidence is disconnected from current attitudes and behaviors of consumers.
Not all synthetic data is generated the same way, and the method decides how far you can trust the result. The most common approaches sit on a spectrum, from loose guesswork on one end to something firmly grounded in real answers on the other.
At the weak end, a general-purpose language model just improvises. Ask it about your audience and it puts together a plausible-sounding persona from whatever it picked up during training, which is mostly the open web. The answer sounds fluent and specific. It's also a guess, shaped by whoever happens to post online on blogs, forums, or social platforms, rather than the people who actually buy from you.
Ask it how retirees feel about a new investment app, and it might confidently describe people who don't look anything like your actual customers.
A step up from that, some synthetic data is built from web scraping or inferred behavior. It stitches together digital traces, like what people clicked, browsed, or posted, into a model of how a group might respond. This captures something real, but it's still an interpretation of behavior, one step removed from what people would actually tell you if you asked them directly. Someone who keeps browsing running shoes can easily be tagged as a keen runner when they might just be buying a gift.
At the strongest end, the simulation is grounded in real survey responses: actual answers real people gave about what they think and do.
The question may be new, but the evidence isn't. The model estimates an answer by combining patterns from what real consumers have already said.
For example, if you want to ask energy drink fans a question that's never been asked before about a new sugar-free launch, a survey-grounded model builds simulated data based on real consumer evidence: GWI data shows 61% enjoy trying drinks with healthy ingredients, while 31% describe themselves as low-sugar.
But how does synthetic data compare with real survey data, and when does the difference begin to matter?
Real survey data captures something no model can invent on its own: what actual people said when asked a direct question.
It's the raw material that real insight is built from. For example, it can tell you that 10,000 people said they prefer blue over red, whether they're more likely to recycle, or whether they'll pay a premium price.
And the further you dig, the more likely you are to find something that genuinely surprises you or points to an opportunity you hadn't thought to look for.
That's why it helps to think of survey data as the ground, and synthetic data as what you build on top of it. Synthetic data takes what you already know from survey data and models answers to new questions in seconds. But it can only do that well if the ground beneath it is solid.
When synthetic data is grounded in real answers, you can trace every result back to something a real person actually said. When it isn't, you get answers with little basis for explaining why they came out that way.
GWI synthetic audiences are built this way: every answer they simulate is grounded in real survey responses, not invented from scratch. Explore GWI synthetic audiences to see what that looks like in practice.
At GWI, we refer to "synthetic data" as simulated data instead, because "synthetic" implies something manufactured from nothing, when really it's a model reproducing patterns from real survey answers. The difference is more than a label, though. Ask one question of any provider, including us: is there a real, identifiable person behind each answer? Synthetic approaches often blend pooled or scraped data into people who never existed, so no one stands behind any single response. Simulation starts from real people who already gave thousands of answers under terms they agreed to, so every result traces back to someone we can point to. So put it to us plainly: show me the real respondent behind this answer.
Used well, simulated data earns its place in an insights toolkit in a few concrete ways.
The first big benefit is speed. A grounded simulation gives you a solid answer fast enough to actually act on, instead of getting the "right" answer after the decision you needed to make has already been made.
You can also test more ideas at once. Say you have five different concepts for a new product. Instead of running a full, expensive study on all five, you can run them through a simulated audience first to see which ones show promise. This leaves you with more time and budget to test and refine the strongest ones properly.
Then there's reach. Some groups of people are hard, slow, or expensive to survey directly because they're too small, specific, or scattered to reach easily. If the simulation is built on real answers from people in that group, you can still learn more about them without having to launch a new study. This is also where simulation is most fragile, though: the thinner the real data on a group, the more caution its answers deserve, so a good provider tells you where its coverage is strong and where it isn't. Some high-stakes or genuinely new questions still belong in primary research.
The common thread that ties them all together is efficiency. Simulation lets you get more value from research you've already done. It helps answer new questions using the same real answers as a base, saving time and money in the early stages of exploring an idea.
But real surveys are still what make this work in the first place. Simulation works best as an extension of real research, not a replacement for it.
This is exactly what GWI built synthetic audiences to do. Explore the use case, or book a GWI demo to put it against a live brief.
GWI runs over 2 million interviews a year across 53 markets, has asked the same core questions for 15 years, and refreshes its data every week. That combination of scale, breadth, history, and freshness gives GWI's simulation a rich foundation of consumer insight to build from.
The scale means the model can pick up on how people in different places actually think rather than flattening the world into one global average. The 15 years of consistent questions show how opinions have moved over time, while weekly refreshes keep the picture current.
You can put all of this to work through Agent Spark, GWI's human insights analyst, or pull the raw responses yourself as Respondent Level Data through Snowflake. Curious how this stacks up against data built from clickstreams or web scraping? Our guide to synthetic personas breaks it down.
Simulated data is a genuinely useful tool for researchers and marketers. When it's fueled by rich, real-world human survey data, it can help you move faster, explore more ideas, and get more value from the research you've already done.
Explore GWI synthetic audiences, or book a demo to try it on a question you're working on right now.
Real data is collected directly from people, for example through a survey. Synthetic data is generated by a model to imitate real data without collecting fresh responses. The thing to check is what the synthetic data was built on, because a simulation grounded in real survey responses is far more reliable than one improvised from the open web.
It can be, and it depends entirely on the source. A simulated answer is only as reliable as the human data beneath it, so synthetic data grounded in real, directly-asked survey responses holds up far better than data inferred from web scraping or a model's best guess.
No. Simulation works best as an extension of real research, letting you move faster and test more ideas from a foundation of genuine responses. The ongoing survey data underneath is what keeps the simulation honest, so it complements fieldwork instead of removing the need for it.
Simulated data is GWI's term for simulation that's anchored to real survey responses. Every answer traces back to a real GWI survey, so the audience is simulated from real people, and the data underneath each answer is real.