Synthetic Data for Market Research FAQ

The conversation around synthetic data in market research is moving fast, and so is the scrutiny. That's a good thing. Research has always advanced through rigorous questioning, and synthetic data should be held to the same standard as any other research methodology.

Below, you’ll find answers to the questions researchers are asking Qualtrics about synthetic data and what we’re thinking about as we develop our models: what it is, where it works, where it doesn’t, and how Qualtrics approaches the science, security, and ethics behind it.

We’ll continue to update this FAQ as conversations progress.

We’re investing heavily in synthetic data technology because we believe it will transform how organizations understand and act on the needs of the customers and markets they serve. And we’re equally invested in building the right foundations and capabilities in the right way.

Understanding synthetic data

What is synthetic data for market research?

Synthetic data refers to AI-generated responses that replicate the statistical patterns, relationships, and characteristics found in real-world data. Instead of surveying human participants, synthetic models generate new observations that reflect how a target population would likely respond based on patterns learned from large, diverse datasets.

Unlike traditional techniques like imputation or extrapolation, synthetic data generates entirely new data points, making it useful for early-stage exploration and hypothesis testing.

For a deeper introduction, see Synthetic Responses 101 for Researchers.

What do people actually mean when they say “synthetic data”?

While there are differing interpretations in the market research space, as per our 2026 Market Research Trends report, we believe researchers associate the term “synthetic” with at least five fundamentally different things:

Synthetic personas: AI-generated representatives of a target segment, which are useful for exploring how a prototypical customer might think or respond.
Synthetically-derived insights: Aggregated findings; responses are not provided as individual-level data.
Simulated individual-level data: A completed survey dataset like a traditional human panel, but generated by AI.
Digital twins: AI-generated replicas of a specific, known person, designed to mirror their individual behavior and preferences.
Simulated conversations: AI-powered interviews or focus group-style interactions with synthetic participants.

This fragmentation matters because providers often use the same language to describe very different capabilities. A vendor offering synthetic personas is solving a different problem than one generating simulated individual-level survey data.

If you’re not clear on what a provider actually delivers, you can’t evaluate whether it fits your research needs.

Qualtrics has intentionally invested first in simulated individual-level data, generating complete survey datasets with the same structured output researchers get from traditional panels. This is the foundation of our synthetic model, and where we believe the most rigorous application of synthetic technology exists for quantitative research today. By prioritizing this approach we have established the foundation for everything else synthetic data can do, which will result in trusted, accurate insights our customers can trust.

How are synthetic research models actually built?

We see three primary approaches:

LLM wrappers use prompt engineering on top of general-purpose AI models (the same data powering ChatGPT or Claude) to get answers. They can provide quick directional reads on generic topics, but lack the granularity needed for segmentation, demographic breakdowns, or structured quantitative research.
ML-powered models use machine learning trained on human-collected responses to expand datasets. For example, if you collect 300 responses from UK consumers, the model extrapolates additional synthetic responses mirroring the original data's patterns. Powerful for augmentation, but dependent on the quality of the seed data.
Purpose-built foundational LLMs combine large-scale proprietary human response data with public sources to build specialized models for simulated survey responses. These deliver stronger accuracy across demographics because they're trained on the kinds of questions and response patterns market research demands—not general internet text.

Qualtrics' synthetic model combines elements of all three. At the core is a patent-pending LLM training method: we fine-tune an open-source language model with a proprietary blend of millions of rows of anonymized survey responses, additional licensed research training data, and publicly available data. This multi-layer architecture enables the accuracy and nuance that quantitative research demands.

For a detailed comparison of these approaches, see Synthetic Panels in Market Research: What You Need to Know.

How should I evaluate synthetic research providers?

Whether you’re evaluating Qualtrics or any other provider, we recommend asking these questions:

What kind of synthetic output are you delivering: personas, insights summaries, or actual respondent-level data?
What data was used to train the model? Is it publicly available data only, or does it include proprietary research data?
How is the output validated against real-world benchmarks? Can I see validation results?
Can I audit the methodology, and do insights satisfy the same validation tests research-grade studies require?
Is the provider building purpose-built research models, or wrapping a general-purpose AI in a research interface?

Providers that are transparent about both their capabilities and their limitations are more likely to deliver tools you can trust for real research decisions.

When to use synthetic data, and when not to

When should a company use synthetic data?

Synthetic data performs strongest in early-stage and exploratory research projects where speed and breadth matter more than pinpoint precision on final decisions. The most common applications include:

Idea screening and early-stage concept testing, rapidly validating dozens of concepts before investing in full-scale human research
Pre-testing survey design, identifying potential issues with question wording, flow, or bias before fielding to human respondents
Strategic understanding studies, attitude and usage, market landscape, needs assessment
Protecting intellectual property, testing new products or features without exposing proprietary concepts to external panel participants

Many organizations find the greatest value in blending synthetic and human data, using synthetic for fast initial exploration, then validating and deepening findings with human panels.

Where shouldn’t synthetic data be used?

We’re direct about this: synthetic data is not the right tool for every research question. It is less suited for:

High-stakes final decisions, such as go/no-go launches, major pricing commitments, or regulatory submissions where precision is non-negotiable
Detailed behavioral recall, questions like “when did you last visit…” or unaided brand awareness that depend on actual lived experience
Deeply nuanced cultural or emotional research, to understand aspects that are not easily reported
Highly regulated industries, where methodological requirements mandate human-sourced data for compliance

Being honest about limitations is part of being a responsible provider and ensuring organizations deliver value. Researchers should hold every synthetic vendor to this standard.

Is synthetic data replacing human panels?

No. Synthetic data augments human research, it does not replace it.

Human respondents remain the source of truth that anchors synthetic models. Without ongoing human research, synthetic models would lose their connection to how real people actually think, feel, and behave. The models learn from human patterns, which means human data is a critical ongoing requirement for model integrity.

The most effective approach combines both: synthetic for speed and breadth in early exploration, human data for depth and validation when decisions are at stake. This is the model that leading research organizations are adopting, and it’s the approach Qualtrics is built to support through our integrated platform.

Trust and validation

How do you validate synthetic data?

Validation is a rigorous process that confirms synthetic data is accurate, useful, and fit for purpose. Key approaches undertaken at Qualtrics include:

Statistical comparisons test whether synthetic data replicates the distributions, correlations, and patterns found in real-world data. Common techniques include the Kolmogorov–Smirnov test, correlation matrix analysis, and divergence measures.
Privacy and re-identification testing evaluates whether individual responses could be traced or reconstructed from the synthetic dataset, ensuring the data is safe by design.

Our goal is to provide organizations with the transparency and tools to evaluate the data themselves.

How does Qualtrics handle bias in synthetic data?

Our training datasets undergo internal approvals and periodic fairness and bias reviews before use. Statistical validation against known population distributions helps ensure that synthetic outputs reflect the demographic and diversity of real audiences.

When populations are underrepresented in primary data, synthetic data can actually help address that gap by simulating a more balanced view, but only when the underlying model has been thoughtfully constructed and validated.

Data privacy and security

What data does Qualtrics use to train its AI models?

Qualtrics trains its AI models, including those powering synthetic panels, on anonymized and aggregated data from the XM Platform.

Critically, no raw customer data is ever used. We follow a rigorous, multi-step process to prevent this: personally identifiable information (PII) and organizationally identifiable information are removed through automated tooling, reviewed by human data and language specialists, and approved through our internal data governance process.

For a comprehensive overview of our anonymization and aggregation process, see Qualtrics’ Commitment to Secure and Private AI.

How does the anonymization and aggregation process work?

Data anonymization at Qualtrics is a rigorous, multi-step process overseen by information security, legal, and data experts. The process uses secure workflows, executed entirely on Qualtrics infrastructure, and involves:

Anonymizing & Aggregating Training Data: Our training data is always anonymized and aggregated data. This ensures no uniquely identifiable information that would reference an individual or customer organization is present in our training data. Also no single customer's data is over-represented.
Strict Testing: Before any model goes live, it must pass rigorous testing designed to evaluate its generalization capability. These tests ensure the model understands the data generally, rather than memorizing specific input.
Implementing Secure Access & Guardrails: Access to our models is strictly limited to Qualtrics customers through secure, purpose-built applications. These applications have built-in guardrails that restrict how the model can be used and provide additional checks on the outputs of these models.

Who owns the data I collect through Qualtrics?

You do. When processing customer data, Qualtrics acts as a data processor. All customer data is owned by the relevant Qualtrics customers, who are the data controllers. Your survey data and the data you collect using the Qualtrics platform is governed by your contractual agreement with Qualtrics. Under these contracts, you own and control your data.

Can AI models memorize and replay individual survey responses?

No. Our models are not designed for, and are incapable of, that level of recall. Simulating how a consumer segment is likely to respond is fundamentally different from recalling what a specific person actually said. That distinction is built into our architecture.

Testing confirms that even when the model has seen an exact question from a specific response during training, it does not reproduce that same response.

Who owns the methodology, and can I audit it?

Qualtrics develops and maintains its synthetic research methodology internally. We believe researchers should be able to scrutinize the tools they use.

We provide validation results and methodology documentation to help researchers evaluate our approach. Our commitment to transparency extends to ongoing governance: methodology updates follow internal review processes that include security, legal, compliance, and technical oversight.

Where we are and where we’re going

What’s the current state of Qualtrics’ synthetic offering?

Qualtrics currently offers synthetic panels for U.S. General Population in English, generally available in the platform. Our synthetic model performs strongest with lifestyle, social science, economics, healthcare content, and in strategic understanding, innovation, and shopper research methodologies.

Synthetic is one capability within our broader research platform, which also includes access to100M+ third-party respondents for quantitative research, specialized participants for in-depth qualitative studies, and a full suite of research tools. We built synthetic to complement these existing capabilities.

Our roadmap includes expanding to international audiences and developing capabilities across niche segments like B2B, and conversational synthetic formats. We’ll share updates as they are available.

What ethical frameworks guide Qualtrics’ synthetic development?

Qualtrics has developed a set of principles to provide trustworthy AI capabilities while protecting data privacy and security:

We use anonymized and aggregated data for AI training purposes
Our AI operates on the data that the user has access to
We uphold all enterprise-grade security and privacy requirements
We ensure customer data ownership and confidentiality
We build on principles for responsible AI

These principles are delivered through our data governance committee, which includes representation from information security, legal, compliance, and technical teams. All data access requests for AI training are reviewed and approved through this governance structure.

How should I explain synthetic data to stakeholders who aren’t researchers?

Here’s a framing we’ve found works well: synthetic data uses AI to predict how a target audience would respond to your research questions based on patterns learned from millions of real survey responses. It’s like having a highly informed preview of what your audience thinks, validated against real-world benchmarks, that you can access in hours instead of weeks.

For executive audiences, the value proposition is straightforward: synthetic data lets your research team run more studies, faster, at lower cost, without sacrificing the rigor that makes research trustworthy.

This FAQ is a conversation, not a conclusion

Synthetic data is an evolving field, and the questions will evolve with it. We’ll continue updating this page as the industry moves forward, our capabilities grow, and as new questions emerge from the research community.

Have a question we haven’t addressed? Contact us or contact your account manager and we’ll work to include it in a future update.