The pros and cons of synthetic data for market research
By Laura Ojeda Melchor●6 min. read●Aug 27, 2025
Synthetic data is suddenly everywhere in market research conversations.
Advocates say it’s faster, cheaper, and more flexible than traditional research methods that rely on human participants. Skeptics warn that it’s only as trustworthy as the data used to generate it. The truth is somewhere in between.
Artificial data can be an incredibly useful tool for researchers when it's used in the right context. It can complement studies that need to collect real human perspectives. But in most cases, it can’t — and shouldn’t — replace them entirely.
Dive into what synthetic data is, the pros and cons of using it, and where it adds the most value for research projects today.
Key takeaways
What is synthetic data? It’s AI-generated data that’s modeled on patterns found in human participant datasets.
Pro: Because it can produce large, diverse samples in minutes, it offers a speedy and budget-friendly way to generate market research insights.
Con: It’s only as accurate as the training data behind it. It needs humans to read and validate whether it’s realistic or not.
Best uses: Early concept screening, simulating situations, and boosting sample sizes in low-stakes projects.
Incentives still matter: When you need human participants to validate synthetic insights, thoughtful recruitment and incentives are crucial.
What is synthetic data?
Synthetic data is information that mimics real-world datasets but is built using AI models.
What’s the appeal of synthetic data? Convenience and cost for market researchers.
For example, let's say your client is a skincare brand that wants to learn how many steps are in its customers' skincare routines. Instead of putting time, effort, and expense into asking hundreds of customers, your research team could tap into a synthetic dataset.
Using algorithms trained on real consumer survey results or behavioral data, your research team could generate a synthetic dataset to get directional answers instead of fielding a net-new survey.
You may be wondering: How can you trust the insights if they come from artificial data? It all comes down to training and validation. You need to be:
Confident that the data the algorithm was trained on is current and represents the right demographic
Willing to ask real people in your target demographic for validation to make sure the information reflects reality
Even though synthetic data for market research is still relatively new, firms and businesses are already using it in 2025. According to a Qualtrics report from late 2024, 71% of market researchers agree that the majority of market research will be done using synthetic responses within 3 years.
That’s a lot of market researchers who are leaning into this trend.
How are market researchers using synthetic data?
Market researchers are using artificial data to:
Boost sample sizes: Supplement small or niche datasets to make findings more robust.
Screen early concepts: Test product ideas before they invest in large-scale studies with actual people.
Model different scenarios: Run what-if analyses, like how different pricing strategies might play out with consumers, without waiting for fresh survey data from the field.
Conduct privacy-conscious research: Create datasets that use patterns and findings from sensitive information without exposing real participant data.
As one Redditor put it, a company they work with thinks “they know enough about their segments to create [artificial datasets] for each segment, and when their marketing people come up with communication materials they can test how each segment would react.”
Another commenter added, “Consider the time and cost involved in running multiple focus groups or surveys — from designing to recruiting, incentivising to analysing, etc. Now imagine being able to have a panel of people who never get tired, never stop answering, [and] have no limits on what can be asked. That’s quite compelling.”
It is compelling. But every research technique has its positives and drawbacks.
4 pros of synthetic data in market research
1. Scale and speed
Synthetic data allows researchers to work at a pace that traditional fielding can’t match.
Instead of waiting weeks for survey responses, synthetic datasets can be generated in just hours. Qualtrics notes that its AI-powered synthetic panels reduce survey costs by up to 70% and shorten collection times from weeks to minutes.
This level of efficiency is huge. And it’s especially valuable during early concept testing, when teams want quick insights to guide their next steps.
2. Diversity of samples
Synthetic datasets can include responses that represent populations often underrepresented in traditional research.
Kantar highlights this as a core benefit:
“Synthetic data can offer many benefits for market research, including increasing sample size and diversity by mimicking hard-to-reach populations at low cost.”
For researchers who struggle to reach niche demographics, synthetic data can help fill some of those holes. Say you need a target audience of people with a rare medical condition. Synthetic data can get you those insights quickly — or at least, insights modeled on human responses.
3. Cost efficiency
Traditional research is expensive. The costs of recruiting, vetting, and analyzing responses from hundreds or thousands of participants can add up quickly.
Artificial participant panels can dramatically cut these costs, especially when you just need insights to help explore an early idea or run a low-stakes simulation.
As Greenbook puts it, “Synthetic data is a powerful way to reimagine how market researchers work. [It can] simulate consumer responses, test hypotheses, and experiment at scale — often without ever fielding a survey.”
With ever-increasing competition in the global research landscape, synthetic data can be a great way to efficiently use client budgets.
4. Privacy and compliance
Because synthetic datasets aren’t tied to real people, they can be a safer choice when you're researching sensitive topics.
Strat7 has found that synthetic data allows researchers to explore sensitive topics ethically and without compromising individual privacy. This makes these artificial datasets attractive for studies where data protection laws or privacy concerns could cause recruiting roadblocks.
4 cons of synthetic data in market research
1. Garbage in, garbage out
Artificial insights are only as reliable as the data they’re trained on. If the original training dataset is limited, biased, or outdated, those flaws will show up in your synthetic outputs.
Kantar cautions that a key precursor to a synthetic data-generating algorithm is an abundance of real baseline data, and that exclusively relying on off-the-shelf LLMs to generate data is often a poor strategy.
2. Risk of bias and distorted attitudes
Models that are trained on skewed or incomplete datasets will reproduce — and even amplify — those biases.
This is why Qualtrics emphasizes the need for systematic validation where a real person looks at synthetic data and decides whether it's realistic, representative, and fit for its intended use.
Without this crucial step, researchers risk drawing conclusions from distorted attitudes that don’t accurately reflect their target market.
3. Limited demographic precision
Artificial insights often fall short when researchers need to slice results into fine-grained demographic groups.
Synthetic data can help close gaps in research samples, especially for hard-to-reach demographics. But artificial insights aren’t substitutes for the lived experiences shared by real participants.
Additionally, synthetic datasets often lack the depth needed for studies that hinge on differences across age, gender, or culture.
4. Recency and realism concerns
Markets move fast, and artificial data can lag behind. Models are trained on existing responses, which means they may miss sudden shifts in consumer behavior.
Kantar cautions: “To be fit for decision making, synthetic modelling needs to reflect real-world complexities by not only learning from the past… but also leveraging the present – through fresh sample[s].”
Without validation against in-the-moment human feedback, synthetic insights can quickly go stale.
When to use synthetic data vs. traditional research methods
Synthetic data isn’t the magic solution for every research project. Its a good choice for projects where speed, scale, and cost matter, and nuance and emotional depth are less important.
Traditional research is key when your customers are using it to inform high-stakes decisions — or when you must capture real human insights.
When synthetic data works best:
Early concept screening: Quickly explore whether an idea has enough traction to justify deeper research.
Scenario simulations: Test what-if scenarios without investing in large human samples.
Sample boosting: Fill in gaps for hard-to-reach demographics or scale up small datasets.
Low-stakes decisions: Use it for exploratory work or less risky product and brand choices where you just need some guidance for what direction to take.
When traditional research is indispensable:
Message testing that hinges on emotion: You can’t model human sentiment or tone with enough authenticity to replace real reactions.
Moment-in-time sentiment: Only human participants can capture how people feel in a rapidly changing world.
High-stakes product, pricing, or brand decisions: When millions of dollars or reputations are on the line, you need actual human perspectives to inform your recommendations.
Why incentives matter even in the age of synthetic data
Synthetic data can take you far, but validation still depends on people. Testing synthetic findings against a real-world sample is becoming a new, critical step in research design. It's key to be upfront about what participants will be doing and offer compelling incentives that show their input is valued. Quick, transparent rewards attract participants and build enough trust to keep them engaged in an era where AI is doing more of the heavy lifting.
Summary
Synthetic data is fast and scalable. This makes it useful for early exploration, simulations, and boosting sample sizes.
But it has limitations. With bias, realism, and lack of nuance, synthetic data can't replace traditional research.
Traditional methods are essential. For high-stakes projects and studies that rely on emotional or contextual insight, you can't replace real participant responses.
The most reliable approach pairs the two. Use synthetic data for efficiency, and human inputs for validation and deeper insights.
Incentives are still critical. When you need real participants to validate synthetic insights or provide first-person responses, offer meaningful incentives and provide clear instructions on what’s expected.
