Once seen as an industry resistant to change, market research has embraced transformative technologies in recent years, with AI leading in reshaping traditional methods. Yet, diversity in the data remains elusive, presenting both an opportunity and challenge for researchers. As the founder of ThinkNow, a company at the forefront of multicultural insights, I’ve witnessed firsthand how critical accurate representation is in understanding diverse consumer behavior.

One way we are addressing this disparity is by creating synthetic samples. Over the years, we’ve developed ThinkNow Synthetic—a synthetic sample solution that leverages artificial intelligence to enhance diversity in data collection. However, for synthetic data to advance diversity, the quality of the training data is paramount. This article examines how AI, particularly synthetic sampling, can revolutionize the industry by producing more inclusive and representative datasets, while also highlighting the differences between synthetic sampling and traditional methods like weighting.

The Evolution of Synthetic Sample

Traditional sampling techniques in market research often fall short when it comes to representing hard-to-reach demographics such as Hispanic, Black, AANHPI, and LGBTQIA+ communities. Even with diligent panel recruitment efforts, certain populations remain underrepresented. ThinkNow Synthetic was born out of this necessity, using large language models (LLMs) trained on multicultural data to create synthetic responses that mirror real-world diversity.

The process begins with training the model on diverse datasets, like the General Social Survey (GSS) and ThinkNow’s proprietary data collected from our panel, DigaYGane.com. This ensures that the synthetic sample reflects the population in question and produces responses representing a wide range of cultural experiences. Our approach enhances the inclusiveness of the data and reduces biases often associated with AI-generated responses.

Synthetic Sampling vs. Weighting

A common misconception in market research is to equate synthetic sampling with weighting. While both aim to adjust the data to reflect population diversity better, they employ fundamentally different methodologies. Weighting, as many researchers are familiar with, takes a small sample size and extrapolates the results to a larger population. This can inflate the representation of underrepresented groups but doesn’t truly increase the diversity of responses. Essentially, weighting adjusts the numbers, not the underlying richness or authenticity of the data.

In contrast, synthetic sampling, particularly ThinkNow Synthetic, is designed to create entirely new data points based on the learned behavior of respondents from diverse communities. For example, if you are conducting a study among bicultural Latinos and face difficulty recruiting sufficient respondents, our AI model can generate synthetic responses that mimic those of a bicultural Latino based on actual data collected from our panel. This method doesn’t simply inflate responses but creates new, culturally nuanced data that enriches the overall dataset.

This difference is significant. Weighting amplifies a limited dataset, while synthetic sample expands it by simulating a broader range of responses. This approach has the potential to dramatically increase representation without sacrificing data accuracy.

The Role of Diverse Training Data

The success of any synthetic sample solution hinges on the quality and diversity of its training data. If the training data used to create synthetic responses is skewed or biased, the results will reflect those same biases. Training LLMs on rich, multicultural datasets ensures that synthetic responses are representative and culturally relevant, effectively mitigating the biases often found in AI-generated content.

ThinkNow Synthetic’s hybrid model combines panel data and synthetic responses to create complete and representative datasets. When a client comes to us with a quantitative study needing 1,000 completed responses, for example, we can provide 500 actual survey responses from our diverse panel and supplement the remaining 500 using synthetic data generated by our AI. This hybrid approach preserves the integrity of the study while reducing costs and accelerating delivery.

Applications and Future Potential

Synthetic sampling is still in its early stages, but the potential applications are vast. From understanding consumer trends to informing policy decisions, synthetic sample can provide a fuller picture of societal behaviors across diverse populations. By filling gaps in datasets with culturally relevant synthetic responses, ThinkNow Synthetic helps clients make more informed decisions that reflect the reality of the communities they serve.

This approach also addresses a major challenge in market research: the underrepresentation of marginalized groups. As brands seek to engage diverse audiences, producing accurate and inclusive data authentically has become a business imperative. Synthetic sampling offers a path forward, equipping researchers with the tools to understand these audiences more deeply.

Conclusion

AI-powered synthetic sample has the potential to revolutionize diversity in market research. However, this is only possible if the training data is as diverse as the populations we aim to represent. At ThinkNow, we are committed to using our years of expertise and rich multicultural datasets to ensure that synthetic sampling doesn’t just mimic diversity but truly reflects it. By combining synthetic data with real-world panel responses, we are creating a new era in market research—one where inclusivity and accuracy go hand in hand.

The future of market research is diverse, and with synthetic sample, we’re ensuring that no voice is left unheard.

This blog post was originally published on HispanicAd.com

Awareness of artificial intelligence (AI) has rapidly grown since the launch of ChatGPT in November 2022. Since then, a flurry of similar AI tools, such as Google Gemini and Microsoft Copilot, have emerged, each aimed at familiarizing consumers with the benefits of this technology. Although AI is not a new concept, its current applications are reshaping creative industries, communication, and the economy through automation and enhanced decision-making, influencing how we work, create and connect.  While many are excited by these changes, they raise concerns for others.

To better understand public opinion on AI, ThinkNow conducted a quantitative survey of a nationally representative sample of U.S. adults.  Our findings revealed both expected and unexpected attitudes toward AI. While widely seen as a useful tool, its adoption varies significantly across age, cultural, and racial/ethnic groups.  Let’s dive in! 

Download the report here.

Familiarity and Use 

Our research shows that AI technology is widely recognized and utilized across generations, except for Boomers. Gen Z is found to be the most familiar with AI, with 49% being very familiar and 38% being familiar. Interestingly, only 13% of Gen Z report being unfamiliar or very unfamiliar with the technology. On the opposite end of the spectrum, only 6% of Boomers report being very familiar with AI, with an overall understanding at 62%. They therefore lag significantly behind Gen Z, Millennials and Gen X. 

However, the extent to which AI is used isn’t equal among generational groups. Cultural/racial background seems to play a role. Our findings show that African Americans report the most familiarity, with 35% feeling “very familiar.” Asians are the next most familiar with the technology, with 33% total reporting strong familiarity. Hispanics (22%) and non-Hispanic whites (22%) report they are “very familiar” with AI. 

These findings highlight the generational and cultural disparities in AI familiarity, suggesting that while AI technology is increasingly recognized, its adoption and understanding vary significantly across different demographic groups. 

Use of AI in Daily Life 

Our findings also find that most respondents perceive that AI is currently being used in three primary areas: scientific research and analysis, daily life applications, and business productivity tools. Twenty-five percent of respondents report that the best use of AI would be in scientific research and analysis. This usage of AI is followed by daily tasks (customer service, scheduling, navigation, etc.) at 24%. Twenty-two percent report that AI should be used in business and productivity, followed by entertainment recommendations or games (18%), and a total of 11% of respondents suggest that they aren’t sure how the technology should be used (5%) or that the presented options do not apply (6%). 

Overall, our findings show that the public's understanding of AI is largely centered on its potential as a valuable tool and supportive resource. Sixty percent of respondents in the total market describe AI as a helpful tool or assistant. This is followed by 39% of respondents describing it as something essential for the future and 36% as an ethical tool for the use of information. A total of 36% of respondents report that AI is a machine that surpasses human intelligence, while 34% report that it’s complex data. Interestingly, only 34% of respondents perceive the integration of AI into daily life as a threat to privacy and only 22% report feeling that AI would threaten their employment prospects.

Thus, while overall perceptions are generally positive, many respondents report concerns about the technology. When compared to other generational cohorts, Boomers are most concerned about the ethical use of AI and the protection of personal information in the face of data collection. This finding is not surprising given this generation’s overall skepticism toward the technology, as we further demonstrate below. 

Acceptance, Education and Regulation

Approximately half of respondents report comfort with AI integration into daily life. Asians and African Americans report the most comfort with daily use of virtual assistants, while non-Hispanic Whites and Gen Z are the least likely to report feeling ‘very comfortable’ interacting with virtual assistants. Generationally, comfort with usage of AI-powered virtual assistants is most prevalent among Millennials (70%), followed by Gen X (52%), Gen Z (51%), and Boomers (39%). 

As AI becomes more ubiquitous, respondents report concerns with education about AI and call for increased government regulation of the powerful technology. Nearly 9-out-of-10 respondents emphasize the importance of AI education. African Americans report the most concern with education, with 62% saying education is “very important,” followed by Asians, with 61% of this demographic reporting the same. Sixty percent of Hispanics and 54% of non-Hispanic Whites say that education about AI is “very important.” The perceived importance of educating the public about AI increases with age. Seventy percent of Boomers think that education on AI is “very important,” followed by Gen X and Millennials. Gen Zers show lower levels of concern about AI education compared to other age groups, likely driven by their self-reported level of familiarity with AI.  

These numbers largely track with interest in regulation of AI. While 57% of Boomers believe AI regulation is very important, this figure drops to 48% among Gen X, followed by Millennials (43%) and Gen Z (39%). Asians are the least likely to consider regulation on AI development as very important, while African Americans are the most. Of all respondents, most identify tech companies (32%) and the government (30%) as the primary entities responsible for AI regulation. These entities are followed by independent bodies (14%) and international organizations, like the UN (9%).

However, 14% of respondents report being uncertain about how to allocate responsibility for regulation. When asked about the future of the technology, responses are almost evenly split. While the majority respond that they are optimistic (54%) or neutral (26%), 20% report feeling pessimistic. Overall, non-Hispanic Whites and Boomers report the most pessimistic outlook, while Hispanics report feeling the most optimistic. 

The Future of AI 

Our research reveals a complex landscape of AI recognition, familiarity and comfort, shaped by generational and cultural factors. While AI continues to grow in influence, the varying levels of adoption highlight the need for targeted education and thoughtful regulation to dispel detractors like conspiracy theories and prevent abuse.  Moving forward, addressing these disparities will be essential to ensure that AI becomes an inclusive and beneficial tool for everyone.

Download the report here.

The pandemic didn't create new consumer trends but significantly accelerated existing ones. The consumer landscape is dynamic, continuously evolving as people change, societies evolve and cultures shift. Consequently, researchers must stay adaptable, embracing new methods and technologies like artificial intelligence (AI). AI enables innovations like automating tasks, personalizing experiences and enhancing data through synthetic samples, which closely mimic human responses. However, AI should complement, not replace, human connection.

One effective way to cultivate this human connection is through market research communities (MROCs). These communities provide a platform for obtaining in-depth consumer insights through ongoing conversations, allowing researchers to more profoundly understand consumer needs and frustrations. However, the success of MROCs relies on planning and execution.

As consumer behavior evolves, researchers face new challenges, however. Today’s consumers are demanding simplicity and security. To address these expectations, researchers must design studies that are both easy to participate in and protective of sensitive data. The integration of AI, with its associated privacy concerns and potential for inaccuracies, adds to these challenges.

Ultimately, a successful researcher blends a deep understanding of consumer behavior with a readiness to adopt new technologies. By integrating traditional research methods with innovative approaches, researchers can gain valuable insights and help brands stay competitive.

In this episode of The New Mainstream podcast, Dan Comenduley, Senior Manager of Consumer Insights at UScellular, explores how AI can enhance consumer data collection while emphasizing the importance of preserving the human touch in research.

Meet Our Guest:



Dan Comenduley has been with UScellular for 3 years and is a Senior Manager on the Customer Insights Team. Dan has a wide range of experience including working at Philip Morris, Pillsbury, Discover Card, United Airlines and Synchrony and a few other places. 

He has run a wide range of research and analytic projects, sponsorship campaigns and evaluations and social media and advertising campaigns. Dan is focused on customers and representing their thoughts and lives to the entire organization.  

Dan earned his Bachelor’s degree from The University of Illinois and his MBA from Vanderbilt University.   

In his spare time, Dan likes to travel and wants to visit every state 5 times and every continent at least once. Dan also was a DJ and enjoys sports and he is working on being a published author.