Synthetic data, artificially generated data designed to represent the statistical properties of real-world or natural data, like patterns, distributions, and relationships you’d normally observe from real people.  

In market research, that “natural data” usually means information collected directly from consumers through surveys, focus groups, and other forms of direct input. Synthetic data is typically trying to represent those people and mimic what you’d get from actual survey respondents or focus group participants. 

The phrase “trying to represent” is key here.  Synthetic data isn’t a new source of truth, but a generated dataset that aims to behave like the real thing. 

That’s also why synthetic tends to trigger an immediate reaction: if it’s generated, doesn’t that mean it’s fake? 

There’s a lot of skepticism around synthetic data. “The most important distinction between synthetic data and fake data, or dummy data,” says JT, “is that synthetic data has been designed with a repeatable process to reflect the real world, and it has some ability to be checked against what the real world would actually have said, should you have spoken to real individuals.” 

That doesn’t make it perfect, but it does make it meaningfully different from fake data. So yes, it’s generated but not made-up. The intent is not to invent but to reflect something that can be validated, tested, and compared.  

In other words, synthetic data is designed to be accountable to the real world. Fake data isn’t. 

That accountability is what makes synthetic data worth considering in research workflows. But to use it responsibly, it helps to be strict about what counts as synthetic data and what doesn’t. 

What counts as synthetic data in market research: 

• Generated data at the participant/respondent level, where the result affects the distributions and behaviors you’d normally expect to see from real people. 

• Scenario simulations and predictive models that aim to behave like natural data and can be validated. 

• Other forms of augmented datasets, where synthetic records are created in a structured way to support analysis. 

What doesn’t count: 

• GPT or AI-generated summaries. Those are interpretations, and they can be different each time you run them. They’re not a dataset that behaves like survey responses. 

• Masked or anonymized real-world data. If the data originated from real people and you’ve simply obscured identifiers, that’s still real data, just protected. Synthetic needs to be generated separately. 

Those distinctions matter because they shape what you can responsibly do with the output. A summary can be helpful, but it shouldn’t be confused with respondent-level data. An anonymized dataset is still grounded in lived responses. A synthetic dataset is not and needs different controls. 

Synthetic data isn’t new as a concept, but its visibility in market research is. JT describes three forces that are pushing it forward at the same time: privacy and governance, commoditized analytics, and a growing obsession with efficiency and effectiveness. 

1) Privacy and governance  

Outside market research, synthetic data is often discussed as a way to analyze or share sensitive data with reduced privacy risk. Sectors like financial services have pushed that conversation into the mainstream, and parts of market research touch that world, especially anywhere personal information is handled upstream in the supply chain. 

In market research itself, privacy has long been a core principle, but synthetic data still gets pulled into the conversation because governance expectations are rising everywhere. 

2) Analytics is commoditized 

Ten years ago, advanced modeling felt scarce. Now, the tools are broadly available. The same Python libraries, the same deep learning approaches, the same general techniques: most teams can access them, often cheaply. 

Once methods become common, the advantage shifts to what you feed them. Data becomes the fuel, and the differentiator becomes data quality, richness, and readiness: how cleanly it can be used in models, how quickly it can be updated, how reliably it reflects reality. 

That framing matters because it changes the synthetic data pitch. It’s not just “AI is here.” It’s “everyone has AI, so what makes your decisions better?” The answer comes back to data. 

3) Efficiency and effectiveness 

Synthetic data gets attention because it can change the economics of research. It can be far cheaper to generate than to collect new responses from real people. In a climate of compressed budgets, that’s an attractive promise. 

But the more interesting argument is effectiveness: synthetic can help teams do more with the data they already collect. “For example, that could be where you add analytics on top of brand tracking or campaign tracking to give you more utility from the data that you already collect.” 

That’s where synthetic starts to become interesting, not as a replacement for research, but as a way to extract more value from research you’re already doing – if you apply it thoughtfully. 

And that “if” is exactly where misconceptions show up. 

This is where a lot of marketing goes too far. Synthetic data is often presented as a replacement for research companies and traditional methodologies, especially from a buyer perspective that associates “survey data” with slow, expensive, disconnected processes. 

“Synthetic data is a good augmentation or supplementation for natural data, real data from real consumers, but can’t replace it,” says JT.  

Even if the outputs are synthetic, the core dependency remains: “You may use a model that is 100% synthetic, but it must be trained on real-world data, and it must be kept up to date with changes in the real world. It can’t be a once-and-done.” 

Synthetic data is not a one-and-done solution. It’s not something you train once and trust forever. It needs updating, testing, and control, and it always comes back to natural data as the anchor. 

When synthetic is used well, JT sees a few areas where it can help teams move faster or go deeper. A few use cases stand out: 

• Stress testing and iteration 

Generated datasets can create broader distributions that help teams test models faster without collecting endless new waves of data. That’s useful for experimentation, development, and analytics workflows where you want to iterate quickly. 

• Prototyping tools 

Synthetic data can be used to prototype products and internal tools. It can help teams build and test workflows without needing to run a full research program every time they want to trial an idea. 

• Low-incidence and hard-to-reach audiences 

This is a common operational pain point in survey research. When sample sizes are small, slicing the data becomes unreliable. That’s where “Augmented sample solutions come to play: “a combination of natural organic data from real people and additional synthetic participants that can be made available in your analytics.” 

But it comes with a very important caveat: the math and interpretation can get murkier than the traditional confidence intervals researchers are used to, and “so whether it’s more accurate or not is a bit up for debate.” 

Synthetic can help, but it changes what “confidence” means, and teams need to be careful not to pretend it’s the same as classical survey certainty. 

• Scenario planning and data fusion 

When teams want to join disparate datasets or run scenario simulations, synthetic approaches can help create joinability, common variables and model structures that support integration and planning. 

• Sensitive datasets and sharing constraints 

In some situations, synthetic data can make it easier to share patterns and behaviors without sharing identifiable data. It can shift a difficult data protection problem into a more manageable commercial sharing context, depending on the use case. 

Across all of these, the theme is consistent: synthetic data can reduce friction and increase usability, but it becomes risky when it’s treated as a substitute for reality. 

At Delineate, synthetic isn’t treated as a single headline feature. It plays roles across what’s described as the survey supply chain: from survey testing through data collection, processing, and the delivery of datasets for client analytics. 

That includes brand tracking and campaign tracking work. Synthetic data has a practical place in tracking specifically because it can help when audiences are hard to reach, and sample sizes make analysis difficult. Augmenting those situations can unlock more usable analytics while still being cautious about how conclusions are framed. 

There’s also a less flashy but deeply valuable use case: quality assurance. Synthetic approaches can support training models to spot outliers and anomalies, and to distinguish stronger from weaker response patterns. That improves the overall reliability of what clients see downstream. 

In other words, synthetic data is used as a support system, helping with analytics, QA, and targeted augmentation, while keeping the core measurement anchored in natural data. 

Synthetic data is valuable, but it should sit on top of a strong foundation of real consumer input. 

“Natural data first is a guiding principle. We strongly believe in asking people their opinions through surveys, and that has worked for many, many years and continues to be a great signal on the world around us.” 

However, a lot of the synthetic data hype is built on the complaint that traditional research can be slow and expensive. If your baseline experience is a traditional agency model that feels disconnected and unresponsive, it’s easy to become receptive to anything that promises speed. 

But that’s not an argument against natural data. It’s an argument for modernizing how natural data is collected, processed, and used. 

This is where the Delineate approach comes into focus: natural first, synthetic when it helps, and only when it’s controlled. 

“Through technology, well-crafted surveys, and through our Delineate Proximity® platform, we can provide a good and fair price for interviews that are high quality, and drive the majority of our data sets through natural data,” says JT. 

“Those natural data sets can be augmented with synthetic data in some circumstances, and we partner with clients in specific use cases.” 

That’s the middle path that often gets lost. You don’t have to choose between “old school research” and “synthetic-only future.” You can build a modern research engine that produces real signals quickly, and use synthetic data as an extension layer, not as a substitute. 

Over the next year, more businesses will talk about synthetic data in many forms, from digital twins and GPTs to synthetic respondents and focus group participants. That will bring innovation and noise. As with most shifts like this, there will be early leaders, fast followers, and a wide range in quality, before the industry starts to settle on clearer norms: what a reasonable mix of synthetic and organic data looks like, how drift and “accuracy” should be discussed, and how synthetic usage should be reported. 

The bottom line is that synthetic data can be helpful. It can speed up iteration, expand what is possible in analytics, improve usability in low-incidence situations, support scenario planning and integration, and strengthen quality assurance. But it does not replace the foundation of market research: asking real people what they think, and treating that as a meaningful signal about the world. Natural data first keeps synthetic from becoming a shortcut to convenient answers. 

At Delineate, we use synthetic data where it adds value, keep it anchored to high-quality natural data, and build the guardrails that make it usable for real decisions. If you are exploring where synthetic data fits in your brand or campaign tracking, we are here to help – Get in touch with us today!