Synthetic data in market research is being marketed as a game-changer—offering faster, cheaper, and supposedly just as reliable insights as real consumer data. The appeal is clear: why spend months conducting surveys and focus groups when an AI-generated dataset can provide answers instantly?
But here’s the real question: Can you trust the source?
Before adopting synthetic data, it’s crucial to ask:
Was the AI trained on high-quality, representative datasets, or does it replicate existing biases?
Were the synthetic insights validated against real human behavior, or are they just algorithmic approximations?
Does the provider disclose its methodology transparently, or is it wrapped in vague, AI-powered jargon?
When used responsibly, synthetic data has the potential to transform market research, providing greater efficiency, flexibility, and cost-effectiveness. However, if the industry prioritizes speed over statistical rigor, it risks creating a credibility crisis rather than a revolution.
So, does synthetic data belong in market research? Absolutely.
The real challenge isn’t whether to use it—it’s ensuring that its outputs are accurate, validated, and reflective of actual consumer behavior.
In the next sections, we’ll break down how synthetic data can truly deliver on its promise, starting with a critical look at its definition, benefits, and limitations.
What Is Synthetic Data in Market Research?
Synthetic data in market research refers to AI-generated datasets that simulate real-world consumer behaviors, offering an alternative to traditional survey-based methods. At its best, synthetic data mimics the statistical patterns and distributions of real-world data, making it valuable for a wide range of applications. However, the hype surrounding synthetic data has led to confusion, as not all solutions meet the high standards of realism and reliability they promise.
The creation of synthetic data, known as data synthesis, relies on advanced techniques like decision trees, machine learning models, and deep learning algorithms. One prominent method is Generative Adversarial Networks (GANs), a technology frequently used in fields like image recognition. GANs operate by pairing two neural networks: one network generates synthetic data, while the other evaluates its authenticity by comparing it to real-world data. Through repeated iterations, the system fine-tunes the synthetic data to closely resemble real-world equivalents.
So, why has synthetic data become such a buzzword in market research? The answer lies in the transformative capabilities of AI and Generative AI. Recent advancements in these fields have made synthetic data more accessible and scalable than ever before. Generative AI models, such as GANs and Large Language Models (LLMs), leverage vast amounts of training data to produce synthetic datasets that are not only diverse but also tailored to specific use cases. This leap in capability has opened doors to generating large volumes of realistic data, enabling researchers to simulate scenarios that were previously unimaginable.
However, it’s essential to temper the excitement with a dose of realism. While the potential of synthetic data is immense, challenges such as ensuring statistical accuracy, avoiding biases, and maintaining transparency in the synthesis process remain critical. These aspects are particularly relevant in market research, where the accuracy and representativeness of data directly influence decision-making.
Opportunities of Synthetic Data in Market Research
The advent of synthetic data has introduced a groundbreaking shift in market research, offering transformative possibilities that were previously out of reach. Here are some of the key opportunities synthetic data presents:
Accelerated Data Collection: Traditional methods of gathering data often span weeks or months. Synthetic data, on the other hand, can be generated within hours, drastically reducing timelines for insights.
Cost Efficiency: Compared to traditional data collection, synthetic data is significantly more affordable. By removing the need for large-scale surveys or fieldwork, companies can reduce costs without compromising on exploratory power.
Unmatched Flexibility: Synthetic data allows for more dynamic research processes, enabling researchers to simulate hypothetical scenarios or even interact with synthetic personas. For instance, businesses can replicate conversations with synthetic customers to uncover deeper, nuanced insights.
Scenario Optimization: With synthetic data, researchers can test and refine strategies by exploring dozens, if not hundreds, of permutations for a given research brief. This enables scenario modeling at a scale previously impossible.
Enhanced Consumer Understanding: Synthetic data provides an opportunity to explore consumer behaviors and attitudes in ways that traditional survey-based techniques may struggle to achieve. By broadening the range of simulated scenarios, it can illuminate hidden patterns or underrepresented viewpoints.
How Can Synthetic Data Pose Risks for Brands and Businesses?
While synthetic data in market research holds immense promise, it is not without its risks. Much like the "hallucinations" observed in GenAI systems, synthetic data can produce outcomes that are superficially convincing but fundamentally flawed. Here are some of the dangers businesses must consider:
Inaccuracy: Synthetic data may fail to accurately represent what traditional, primary data collection would uncover. This can result in misleading conclusions, especially when decisions rely heavily on the generated insights.
Embedded Bias: If the real-world data or methodologies used to generate synthetic data are biased, those biases can be codified and amplified in the synthetic outputs. This can lead to skewed insights that misrepresent consumer realities.
Limited Richness: Unlike real human data, synthetic datasets may lack the diversity and depth of perspectives that true respondents provide. While they might capture general trends, they often miss the granularity and variability of real-world opinions.
Recycled Insights: Synthetic data can sometimes feel derivative, offering insights that are essentially regurgitated versions of existing data patterns. This risks stifling innovation and perpetuating mediocrity in research outcomes.
Misaligned Validation: Synthetic data may be validated for one specific purpose, such as tracking broad trends, but fail in other applications like segmentation analysis or advanced multivariate modeling. Moreover, its performance may vary depending on the area of focus, particularly if the synthetic dataset lacks recent or context-specific training.
These challenges underscore the importance of careful evaluation and validation when employing synthetic data in market research. Brands must ensure that the methodologies used to generate synthetic datasets are robust, transparent, and tailored to their specific use cases.
What Are the Main Types of Synthetic Data in Market Research?
In market research, synthetic data is not a one-size-fits-all solution. It encompasses a variety of methodologies, each designed to address specific challenges and research needs. From enhancing existing datasets to creating fully artificial respondents, synthetic data offers significant opportunities for innovation but also comes with unique risks. Below, we explore the primary types of synthetic data and their applications, benefits, and potential pitfalls.
1. Data Boosting/Augmentation
Data boosting involves supplementing real-world datasets with synthetic data to create a more robust, comprehensive, and representative sample.
Benefits:
Improves data availability, especially for underrepresented or niche segments.
Reduces the cost and time associated with extensive data collection.
Enables better decision-making by enhancing the richness and diversity of data.
Risks:
The quality of synthetic data is heavily dependent on the underlying training models and datasets.
Biases and errors in the original data can be amplified in synthetic datasets, leading to inaccurate conclusions.
2. Data Imputation and Fusion
Imputation fills in missing or incomplete data points using predictive algorithms. Fusion combines multiple datasets into a cohesive whole by filling gaps and creating comprehensive profiles.
Benefits:
Optimizes the use of existing data by addressing gaps and missing values.
Reduces questionnaire length, improving respondent experience.
Facilitates the integration of disparate data sources for a holistic view.
Risks:
Accuracy relies on the validity and purpose alignment of the imputation or fusion models.
Imputed data is often better suited for summary or descriptive statistics but may fall short in predictive modeling or advanced analytics.
3. Generative AI Agents and Persona Bots
Customized digital assistants and persona bots emulate specific consumer segments or even individual respondents, offering insights based on synthesized data from predefined topics or research data.
Benefits:
Enables innovative and scalable segmentation research.
Provides a dynamic way to engage with synthetic consumer personas and simulate interactions.
Facilitates continuous research by generating real-time, directional insights.
Risks:
Fully relying on synthetic personas risks generating misleading or incomplete insights, as synthetic personas may lack the depth of real human responses.
Potential misuse, such as generating deepfakes or spreading misinformation, raises ethical concerns.
Requirements:
Persona Bots must be built on thoroughly validated datasets and research frameworks.
Transparency about the synthetic nature of these tools is crucial to maintain trust and credibility.
4. Full Synthetic Data
This approach involves creating datasets composed entirely of artificially generated respondents, rather than augmenting or imputing real data.
Benefits:
Enables rapid data collection at a fraction of the cost of traditional methods.
Offers a scalable solution for exploratory or preliminary research.
Risks:
May fail to capture the richness and diversity of real-world data, leading to oversimplified or inaccurate insights.
Synthetic personas based solely on algorithmic extrapolation can distort the representation of consumer diversity.
Lacks the nuanced, human-driven variability required for high-stakes decision-making.
Requirements:
Full synthetic datasets demand rigorous validation processes to ensure their accuracy and reliability.
They should complement, rather than replace, primary data collection to provide a balanced and realistic perspective.
Critical Comparison of Synthetic Data Tools in Market Research
Navigating the growing field of synthetic data tools in market research requires a discerning eye to avoid falling victim to flashy claims of "instant consumer insights." Many tools in the market promise results comparable to real data but fail to back these promises with robust methodologies or transparency. By critically analyzing these tools, businesses can make informed decisions and avoid costly mistakes.
Table 1 provides a comprehensive comparison of synthetic data tools based on criteria such as functionality, transparency, pricing, statistical validation and many more. While the table offers a full analysis, this section highlights the key strengths and weaknesses of each tool to help you better understand their positioning in the market.
To set the stage, Figure 1 illustrates a positioning map that evaluates these tools based on their statistical validation transparency and pricing:
Figure 1: Positioning map of selected synthetic data tools in market research
The positioning map offers an insightful overview of synthetic data tools in market research, comparing their statistical validation transparency and pricing. While each tool serves unique purposes—ranging from survey augmentation to persona creation and data cleansing—the map helps identify trends, gaps, and opportunities in the market.
Q1: High Validation, High Price
Current Status: No leading tool currently occupies this quadrant, highlighting a significant market gap. There is an absence of tools that combine robust statistical validation with premium features, leaving room for innovation in this segment. However:
Yabble and Fairgen: both tools pricing is on the higher end but lacks sufficient transparency in data sourcing and statistical validation. Although technically occupies the high validation range, actually provides moderately reliable statistical validation, such SaaSy by Enäks and SyntheticUsers. So, for companies aiming to base strategic decisions on synthetic data, these limitations could be costly and misleading.
Q2: High Validation, Low Price
Standout Tools:
SaaSy by Enäks: This tool leads the quadrant with moderately reliable statistical validation and free accessibility, offering unmatched value. Positioned primarily as a lead magnet, SaaSy focuses on building trust through experimentation and transparency, making it a compelling option for users. However, while a detailed and technical blueprint for its statistical validation has been published, the experimental design—conducted in a controlled environment—raises questions about its statistical representativeness in real-world scenarios. That said, Enäks has openly acknowledged these limitations and outlined necessary improvements for further validating SaaSy in its blueprint. Given its full transparency and zero financial commitment, SaaSy remains a compelling option for SaaS C-level executives seeking real-time, simulated conversations with their SaaS buyers.
SyntheticUsers: Falling in this category with moderate pricing, this tool offers moderately reliable validation for its specific use case considering that it does not provide detailed explanations of its parity scoring methodology, which might deter technical users.
Q3: Medium Validation, Medium Price
Roundtable: offers average statistical validation transparency and mid-tier pricing. Roundtable specializes in data cleaning, making it highly valuable for agencies handling large datasets. However, its semi-transparent methodology may not appeal to users demanding full validation clarity.
Q4: Low Validation, High Price
Native: This tool occupies the least favorable quadrant, offering limited transparency in statistical validation while maintaining a premium price point. The lack of clarity in its methodology and validation raises concerns about its integrity for businesses seeking reliable and actionable insights.
Q5: Low Validation, Low Price
OpinioAI: Positioned here, it appeals to cost-conscious users with accessible pricing. However, the lack of transparency regarding data sources and validation limits its suitability for businesses requiring rigorous and reliable insights. Also it’s worth noting that among all the listed tools, OpinioAI was considered the least transparent from a statistical validation standpoint. While this tool may serve as a starting point for small-scale projects, it struggles to compete with more robust offerings in higher quadrants.
The positioning map provides an overview of where these tools stand in the synthetic data market. To gain deeper insights, the next section will analyze each tool in detail, highlighting its strengths, weaknesses, and alignment with user needs based on Table 1.
Table 1. Comparison of synthetic data tools in market research
Function | For whom | Features/Capabilities | Transparency on Data Sources | Transparency on Statistical Validation | Transparency on pricing | Specific conditions to use | Customization Options | Ease of Integration / UI | |
B2B SaaS audience simulation | SaaS companies, C-level managers and marketers | Simulates the audience needs and preferences in a realistic content style and tone of voice | Transparent: -public consumer behavior data and research -public industry and macro reports -community generated insights with Voice of Experts | Transparent: -full blueprint of the methodology and statistical validation, including response accuracy, F1 score and Brier score -More statistical validation claimed on the next months | Transparent: FREE | Work in the B2B SaaS industry | Not yet available | Clear, easy to use UI; no integration options | |
Augment and debiasing survey data | Researchers and companies working on specific and niche segments | Enhances survey datasets by generating synthetic respondents, enabling more granular insights into niche audiences | Transparent: -processes survey data provided by its clients | Transparent: -parallel testing for comparing the synthetically generated data to the original data | Not transparent: No publicly available price | Have previous primary quantitative dateset | Fully customizable based on original dataset form the client | Clear, easy to use UI; no clear info of integration options | |
Market research automation | Market researchers, businesses | Extracts market trends and insights, builds virtual personas | Transparent: -proprietary datasets, academic content, and real-time web searches -RED FLAG on secondary sources | Transparent: -statistical outputs cited such as confidence intervals and match rates, but lack of detailed technical transparency | Transparent: $8,900 USD - $80,000 USD (annual) | No required | Allows use of your own data | Clear, easy to use UI; no clear info of integration options | |
Simulated user personas for interviews | UX researchers and businesses | Generates personas for UX purposes and interviews | Transparent: -Not explicit information of their data source, although in one article they suggest: social media posts, online reviews, forum discussions, and public datasets | Transparent: -high parity scores but lack of detailed technical transparency | Transparent: Flexible pricing per interviews and personas | Basic UX Research knowledge | User customization options available | Clear, easy to use UI; no clear info of integration options | |
Automated data cleaning | Research agencies, panelists, and businesses | Automates the identification and flagging of low-quality or suspicious responses in open-ended survey data, reducing the need for manual review. | Transparent: -processes survey data provided by its clients | Semi-transparent: -experience in analyzing over 5 million open-ended responses.However, detailed methodologies or statistical validations of their AI models are not publicly available. | Transparent: $1000 USD - 10,000 participant/ month | Have previous primary qualitative dateset | Potentially limited | Clear UI, Easy-to-integrate API | |
Market research automation | Market researchers and businesses | Create interactive personas of existing research participants to gather new predictive responses. This feature allows users to converse with digital clones of their customers | Semi Transparent: -Hundreds of data sources, including major retailers and data provided by its clients | Not transparent: -offers features like Advanced Orchestration and Synthetic Output Controls BUT no explicit methodology shared | Not transparent: No publicly available price | Have previous dateset | The platform also allows for the upload and analysis of first-party data, providing flexibility in data integration | Easy-to-use but limited details | |
Synthetic data generation | Businesses | A platform for generating synthetic datasets tailored to market research needs. It offers two approaches: augmenting user-uploaded datasets or creating data from scratch. | Not transparent: -For cases where users do not upload primary data, the platform does not disclose clear information about the data sources or methodologies used to generate synthetic data | Not transparent: -specific methodologies for statistical validation are not publicly documented | Transparent: free-$200 USD / month | Can upload previous dateset | Allows use of your own data | Clear, easy to use UI; no clear info of integration options |
SaaSy is a B2B SaaS audience simulator that replicates audience needs and preferences using realistic content styles and tones of voice. It is specifically designed for SaaS companies, C-level managers, and marketers, helping them gain a deeper understanding of their audiences (Figure 2).
Figure 2: Example of SaaSy by Enäks interface
Source: Enaks Market Intelligence, 2025
Pros:
Transparency on Data Sources: SaaSy utilizes a robust, three-layered approach to data sourcing:
Public consumer behavior data and research.
Industry and macroeconomic reports.
Community-generated insights gathered from the Voice of Experts.These sources collectively leverage more than 210 statistics and research studies.
Real-Time Updates: The tool integrates daily updates from community-generated insights, ensuring its users work with the latest information.
Accessibility: SaaSy is completely FREE, aligning with the company’s mission to make advanced data accessible for SMEs. This eliminates the typical financial barrier associated with cutting-edge AI tools.
Efficiency: It primarily leverages secondary data to provide SaaS decision-makers with broad audience insights. SaaSy reduces the time spent on traditional desk research by an initial estimation of 75% to 97.92%.
Accuracy: The tool offers an average response confidence rate of 87% and an accuracy rate of 96%, with plans for further statistical validation and enhanced metrics in the near future.
Figure 3: User interface of SaaSy by Enäks
Source: Enaks Market Intelligence, 2025
Cons:
Niche Target Audience: SaaSy is only suited for companies operating within the B2B SaaS industry, limiting its applicability for organizations outside this domain.
Lack of personalized insights: Since this tool primarily relies on secondary data, the insights it provides reflect broad audience opinions. While valuable for gaining strategic direction, caution should be exercised when using these insights for final decision-making, as they may lack the nuance required for tailored or highly specific scenarios.
Conclusion:
SaaSy by Enäks delivers on its promises by offering transparent, accurate, and accessible audience insights. The tool’s standout feature is its free pricing model, making it a valuable asset for SMEs and startups exploring audience behavior without any financial commitments. However, it remains confined to the B2B SaaS niche and could benefit from more advanced customization and statistical validation. For professionals within this space, SaaSy is a good start for getting real-time audience insights.
Fairgen is a synthetic data tool designed to augment and debias survey data, enabling researchers to generate synthetic respondents and gain granular insights into niche audiences (Figure 4).
Pros:
Transparency on Data Sources: Fairgen processes client-provided survey data, ensuring insights are rooted in relevant primary data.
Niche Segment Focus: The platform excels at generating insights for hard-to-reach and highly specific audience segments, making it ideal for specialized research.
Statistical Validation: Fairgen is transparent about its methodology, using parallel testing to ensure synthetic data aligns with the patterns and insights of original datasets.
Customization: Fully customizable outputs based on clients’ datasets allow users to tailor synthetic data to their specific needs.
Time Efficiency: By enhancing existing survey data, Fairgen saves researchers the effort of repeatedly collecting new data.
Figure 4: User interface of Fairgen
Source: Fairgen, 2025
Cons:
Dependency on Primary Data: Fairgen requires users to supply an original quantitative dataset, making it less accessible to organizations without the resources for primary research. This could limit its utility for smaller businesses or those operating on tighter budgets, particularly when researching niche segments.
Opaque Methodology: The platform does not disclose details about the models or algorithms it uses for data augmentation and debiasing, which may raise concerns among users who prioritize methodological transparency.
Pricing Ambiguity: Fairgen does not publicly disclose its pricing, requiring users to request a quote. This lack of transparency may deter cost-conscious buyers.
Conclusion:
Fairgen is a powerful tool for researchers and companies seeking to enhance and debias their existing survey data. Its focus on niche segments and granular insights is particularly valuable for specialized research. However, the requirement for primary data and the absence of pricing transparency may limit its appeal to smaller businesses or those exploring broader, less resource-intensive solutions.
Yabble is a market research automation tool that streamlines the research process for businesses and market researchers. It offers capabilities such as market trend analysis, persona building, and insights generation, leveraging advanced AI models to simplify traditionally time-intensive tasks (Figure 5).
Pros:
Market Research Automation: provides multiple solutions such as automating trend analysis, insights generation, and persona building, helping users save significant time and resources.
User Data Integration: Yabble allows users to incorporate their own data into the platform, creating more relevant and tailored insights.
Statistical Validation: The platform claims to provide statistical validation for its outputs, including confidence intervals, match rates, and variance analysis.
Figure 5: User interface of Yabble
Source: Yabble, 2025
Cons:
High Cost Barrier: Yabble’s pricing ranges from $8,900 USD to $80,000 USD annually, making it inaccessible for many startups and smaller businesses despite its flexibility.
Questionable Data Sources: A significant concern with Yabble is its reliance on some data sources of questionable quality. For instance, its published tutorial (Figure 6) reveals that it utilizes MDPI publications—a journal often criticized by the research community as a predatory journal.
Figure 6: Tutorial video of Yabble
Source: Yabble, 2025
What’s Wrong with Predatory Journals?
Predatory journals exploit the open-access publishing model by charging fees without providing rigorous peer-review or editing services. MDPI, in particular, has faced criticism for its lack of robust peer-review standards and a business model that prioritizes high publication volume over scientific rigor.
Why Does This Matter for Yabble?
The inclusion of such data sources poses a substantial risk for a company that charges a premium price for high-quality insights. While some MDPI publications may meet acceptable standards, they often fail to demonstrate the same rigor as more esteemed publishers. This lack of scrutiny raises serious questions about the reliability and credibility of Yabble’s insights.
For example, using data from sources with methodological flaws or biased findings can generate misleading synthetic data or inaccurate insights, especially when the starting price is $8,900 USD. This undermines the credibility of the platform’s outputs.
A Personal Note on MDPI:
As a scientific researcher and former PhD student, I’ve also published in MDPI journals under specific circumstances. Academic researchers face immense pressure to publish frequently, with limited time and resources to ensure groundbreaking quality for every paper. In such cases, MDPI provides a viable option for faster publication and broader citation reach, especially for early-career researchers. However, while this may be acceptable for academia under certain conditions, a company positioning itself as a premium insights provider cannot afford to compromise on the quality of its sources.
By contrast, platforms like Enäks adopt a stringent quality control process when incorporating academic research, focusing exclusively on the top 25% of scientific journals and prioritizing studies with robust methodologies. Such an approach aligns with their commitment to transparency and credibility—qualities Yabble appears to lack in its data sourcing practices.
Conclusion:
Yabble offers a promising set of features for market research automation, particularly for trend analysis and persona creation. Its ability to integrate user-provided data and streamline insights generation can be valuable for businesses with large budgets. However, significant concerns about its data sourcing practices and transparency hinder its credibility. The reliance on questionable data sources, such as MDPI, raises red flags for users expecting high-quality, scientifically validated insights.
For businesses considering Yabble, it is essential to weigh its automation capabilities against the potential risks of relying on outputs derived from less-than-reliable data. While the tool shows potential, it currently fails to meet the standards of rigor and transparency required for premium pricing.
If Yabble aims to maintain its competitive positioning, it must address these issues by enhancing transparency in its validation processes and adopting stricter quality controls for its data sources. Only then can it justify its pricing and deliver truly reliable insights to its users.
SyntheticUsers is a cutting-edge tool designed for generating simulated user personas tailored to UX research and testing purposes. It enables businesses and UX researchers to efficiently create realistic personas and interview data, allowing for faster design iteration and usability testing (Figure 7).
Pros:
Specialized for UX Research: SyntheticUsers is specifically designed to address the needs of UX researchers by generating realistic user personas and conducting simulated interviews, saving considerable time and resources.
Transparent Statistical Validation: The platform boasts parity scores between 85% and 92%, validating the alignment between synthetic and real-world personas.
Flexible Pricing: Custom pricing models accommodate projects of various sizes, offering scalability based on the number of interviews and personas required.
Figure 7: User interface of SyntheticUsers
Source: SyntheticUsers, 2025
Cons:
Limited Methodological Details: While the platform provides clear statistical validation metrics, it does not disclose the detailed methodology behind its parity score calculations, leaving a gap for users seeking a deeper understanding.
Lack of Transparency in Data Sources: SyntheticUsers does not clearly communicate its data sources. While one of their articles mentions sources such as social media and public datasets, it remains unclear whether these are general examples or specific to their own data collection.
Integration Limitations: SyntheticUsers lacks advanced integration features with external analytics tools or UX pipelines, potentially hindering scalability for larger organizations with more complex workflows.
Conclusion:
SyntheticUsers is a good choice for UX researchers and businesses looking to accelerate their design processes withuser personas and interview simulations. However, while the platform is primarily positioned as a user research tool, effective user testing often involves more than just interviews—it typically includes hands-on testing of websites or applications, combined with pre- and post-test interviews. It remains unclear whether SyntheticUsers supports this comprehensive approach, which may limit its utility for end-to-end user testing.
Roundtable is an automated data-cleaning tool that optimizes the process of identifying and removing low-quality or irrelevant responses in open-ended survey data. It caters to research agencies, panel providers, and businesses seeking to improve the quality of their datasets while reducing manual review efforts (Figure 8).
Pros:
Specialized Functionality: Roundtable focuses exclusively on cleaning open-ended survey data, automating the flagging of low-quality or irrelevant responses. This specialization addresses a critical pain point for researchers.
Transparent Data Processing: The tool processes survey data provided directly by clients, ensuring it remains tailored to specific research needs.
Proven Scalability: Roundtable claims to have analyzed over 5 million open-ended responses, saving clients an estimated 10,000 hours of manual cleaning effort.
Ease of Integration: Its simple-to-use API allows seamless incorporation into existing research workflows or data pipelines.
Clear Pricing Structure: Roundtable is transparent about its pricing, starting at $1,000 USD for up to 10,000 participants per month.
Figure 8: User interface of Roundtable
Source: Roundtable, 2025
Cons:
Semi-Transparent Statistical Validation: While the platform highlights anecdotal success metrics (e.g., time saved, responses analyzed), it does not disclose the statistical methods or algorithms used for flagging and cleaning, raising concerns about validation robustness.
Limited Customization: The default settings for data cleaning offer little flexibility, potentially limiting its applicability for datasets requiring highly specialized rules or algorithms.
Conclusion:
Roundtable is a practical and efficient tool for automating the data-cleaning process in open-ended survey responses. Its strengths lie in time-saving capabilities, transparent pricing, and ease of integration, making it ideal for research agencies and businesses managing large datasets. However, its semi-transparent validation and limited customization options may deter users who need more control over the cleaning process or deeper methodological insights. Roundtable is best suited for organizations looking to streamline their workflows and optimize existing datasets.
Native is a market research automation platform that creates interactive digital personas of existing research participants. It enables businesses and researchers to simulate customer behavior, gather predictive responses, and conduct conversational analyses using digital clones of their customers (Figure 9).
Pros:
Innovative Functionality: The ability to create interactive digital personas sets Native apart, allowing businesses to simulate customer behavior, gather predictive insights, and conduct in-depth behavioral analyses.
Diverse Data Sources: Integrates data from hundreds of sources, including major retailers, public datasets, and user-provided inputs, broadening the scope of insights.
Customizability: Supports the upload and analysis of first-party data, enabling users to tailor the platform to meet their unique needs.
Figure 9: User interface of Native
Source: Native, 2025
Cons:
Semi-Transparent Data Sources: While it claims to use diverse data sources, the platform does not offer detailed verification or a breakdown of these sources, which could impact data reliability.
Opaque Statistical Validation: The methodology for validating predictions or outputs is not disclosed, leaving critical gaps in transparency and trust.
Pricing Ambiguity: The lack of publicly available pricing makes it difficult for businesses to assess the platform’s value relative to competitors.
Conclusion:
Native's innovative approach to creating interactive personas makes it a valuable tool for businesses looking to gain deeper behavioral insights. However, the platform's limited transparency regarding data sources, statistical validation, and pricing may deter users who prioritize methodological rigor and clear cost expectations.
OpinioAI is a synthetic data generation platform tailored to market research. It offers two primary functionalities: enhancing user-uploaded datasets or creating synthetic data from scratch. This flexibility allows businesses to either improve existing research or develop entirely new datasets (Figure 10).
Pros:
Flexible Functionality: Provides options for augmenting user-provided datasets or generating synthetic data independently, accommodating diverse research needs.
Affordable Pricing: Features a transparent pricing model, with plans ranging from free to $200 per month, making it accessible for organizations of all sizes.
User Data Integration: Enables seamless augmentation of uploaded datasets, ensuring compatibility with ongoing research workflows.
Figure 10: User interface of OpinioAI
Source: OpinioAI, 2025
Cons:
Opaque Data Sources: When users do not upload their own datasets, the platform lacks clarity about the origin or reliability of the data sources used for synthetic data generation.
No Model Specification: The platform does not disclose details about the machine learning models or algorithms used to generate synthetic data, limiting trust in its technological foundation.
Lack of Statistical Validation Transparency: There is no publicly documented methodology for ensuring the validity or reliability of the synthetic data outputs.
Conclusion:
OpinioAI offers a budget-friendly and flexible solution for businesses seeking synthetic data for market research. However, its lack of transparency around data sources, underlying models, and statistical validation may raise concerns for users requiring high levels of reliability and rigor in their research.
Where Are the Big Market Research Companies in the Synthetic Data Landscape?
The adoption of synthetic data in market research has sparked interest among industry leaders, but the response so far appears measured and cautious. While many acknowledge its transformative potential, they are wary of its statistical complexity and the rigorous validation required to ensure reliability. This caution reflects a strategic approach: rather than rushing into the creation of fully synthetic datasets, most major players are integrating synthetic methodologies as enhancements to existing research processes. Below is a critical look at how some of the leading market research companies are positioning themselves in this emerging field.
Kantar:
Kantar is leading the synthetic data space with a strong focus on augmenting real data to generate new insights and simulate potential scenarios. They emphasize the importance of starting with high-quality real-world data to train synthetic algorithms, ensuring accuracy and minimizing bias.
Initiatives and Offerings:
Kantar’s AI-driven solutions, including LinkAI, ConceptEvaluate AI, and their GenAI assistant, KaiA, reflect their commitment to incorporating artificial intelligence into research methodologies.
The launch of their AI Lab initiative signals further innovation, particularly in the GenAI field.
Despite these advancements, Kantar has not yet clarified whether it plans to offer standalone synthetic data services.
While Kantar’s use of buzzwords showcases their innovative aspirations, the lack of concrete details on statistical validation and standalone offerings raises questions. This cautious approach may be deliberate, avoiding overpromising capabilities that are still under development.
Nielsen:
NielsenIQ appears to follow a path similar to Kantar’s, focusing on integrating synthetic data within specific research solutions rather than marketing it as a standalone product.
Key Initiatives:
NielsenIQ is experimenting with synthetic respondent generation to enhance specific research methodologies.
Their approach is incremental, suggesting a cautious exploration of synthetic data’s potential without committing to full adoption.
While this positions NielsenIQ as an innovator in the synthetic data space, the lack of clear synthetic data offerings indicates they are still assessing the risks and feasibility. Their focus on specific use cases demonstrates a pragmatic approach, but it also highlights their hesitation to embrace synthetic data at scale.
Ipsos:
Ipsos has taken a thought-leadership role in guiding the responsible adoption of synthetic data. Their messaging emphasizes combining human expertise with artificial intelligence to produce reliable synthetic outputs.
Current Position:
Ipsos has not launched standalone synthetic data services but actively advocates for its complementary use in traditional market research.
Their cautious stance aligns with their reputation for methodological rigor, ensuring that synthetic data meets high-quality standards before broader adoption.
Ipsos’ approach positions them as a responsible innovator, but their reliance on traditional methodologies could slow their pace of adoption.
YouGov
YouGov entered the synthetic data conversation through its acquisition of Yabble in August 2024.
Key Development:
This acquisition signals a strategic move toward incorporating synthetic data tools into their offerings.
YouGov appears to be leveraging Yabble’s capabilities to explore synthetic data applications, though their broader strategy remains unclear.
YouGov’s acquisition highlights their interest in innovation, but they face challenges in ensuring Yabble’s tools meet the rigorous standards expected in market research. Without transparency around validation processes, their entry into the synthetic data space could face scrutiny.
Dynata
Dynata remains a traditionalist in market research, cautiously experimenting with synthetic sampling for niche and hard-to-reach segments.
Current Approach:
They advocate combining synthetic samples with real-world data rather than fully adopting synthetic respondents.
Validation remains a key focus, ensuring that synthetic methodologies meet stringent quality standards.
Dynata’s approach mirrors Ipsos in viewing synthetic data as a complementary tool rather than a replacement for primary research. This conservative stance ensures reliability but could leave them trailing more innovative competitors in the long term.
Major market research companies are treading carefully in the synthetic data space, emphasizing quality, transparency, and methodological rigor. This caution contrasts sharply with the buzzword-heavy narratives of smaller startups, positioning these industry leaders as responsible innovators. The cautious optimism displayed by these companies may be the most sustainable strategy, allowing them to harness synthetic data’s potential without compromising their reputations for delivering reliable insights.
Conclusion: Is Synthetic Data the Future of Market Research—or Its Next Credibility Crisis?
Synthetic data in market research is at a crossroads. It promises speed, cost efficiency, and scalability, but without rigorous validation, it risks undermining trust in data-driven decision-making.
The core issue isn’t whether synthetic data has potential—it does. The real question is: Will businesses demand proof before investing in AI-generated insights?
What Happens Next?
If providers remain vague about methodologies → Expect growing skepticism. Researchers and decision-makers won’t trust synthetic insights without transparency.
If the industry prioritizes statistical validation → Synthetic data could revolutionize market research. But this requires clear documentation of data sources, training models, and real-world accuracy testing.
If businesses rush in without questioning validity → Wasted budgets, misleading insights, and flawed strategies will follow.
At the end of the day, bad data doesn’t just waste time—it leads to poor decisions that cost companies millions.