Synthetic sample quickly evolved from a novel idea to a practical research tool. In just a few years, it has shifted from theoretical debates about data integrity to real-world use in projects where speed, cost, and reach are critical. For the Latin American market, where achieving representative coverage has always presented unique challenges, synthetic sample is emerging as a powerful complement to traditional research methods to gain broad coverage.
But with innovation comes skepticism. Many researchers in LatAm and globally are asking the same questions:
The answers to these questions start with showing your work. Be clear about how the data is being built, demonstrate how it’s validated against real-world benchmarks, and ground every step in the cultural and demographic nuances of the region. Let’s dig deeper.
Latin America is a region with massive diversity. It spans urban hubs like Mexico City and São Paulo, where digital engagement is high, to rural areas where internet access and participation in online research are still emerging. Language, cultural traditions, and economic realities vary widely not just between countries but within them.
For researchers, this means traditional online panels alone often cannot achieve the coverage needed for high-quality, representative studies. Some audiences are too small, too geographically dispersed, or too underrepresented in online research to be reached cost-effectively. This is where synthetic sample proves valuable.
By modeling from robust, permission-based seed data, synthetic sample can fill in the gaps left by traditional recruitment, extending coverage to these hard-to-reach, chronically underrepresented audiences while maintaining statistical integrity.
Transparency is key in expanding synthetic sample use in LatAm as it builds trust. Researchers must not only show how the data is created, but also clearly explain the role synthetic data will play in the research. Researchers do this in a number of ways.
For innovators in the space, starting with culturally representative, zero-party datasets collected directly from respondents in the markets is foundational. This ensures that the seed data is accurate, consented, and reflective of the diversity in the region. From there, AI-driven modeling techniques create synthetic respondents whose profiles mirror the attitudes, behaviors, and demographics of real people.
It’s important to note that synthetic sample is not a replacement for traditional respondents. Instead, it is a way to supplement coverage, reduce field time, and increase feasibility for studies that would otherwise be cost-prohibitive.
Synthetic data is only as good as the data it is trained on. In LatAm, that means seed datasets must reflect the full complexity of the region’s markets.
For example, suppose your seed data over-represents urban, middle-class consumers in Mexico City. In that case, your synthetic model will miss key rural and lower-income perspectives that are essential to understanding the national market. The same applies to language. In countries like Peru and Bolivia, indigenous languages play a critical role in cultural identity and consumer behavior. Ignoring these variables in your seed data will limit the value of your synthetic outputs.
This is why local expertise matters. Synthetic sample expansion in LatAm cannot simply be an export of methods developed in North America or Europe. It must be grounded in the lived realities of the people we are trying to understand.
The most effective use of synthetic sample in LatAm will likely be hybrid models that combine traditional and synthetic respondents.
For example, a study might begin with a traditional sample to gather fresh, in-market responses. These real-world results can then be used to refine and validate synthetic models, which in turn can fill demographic or geographic gaps. This approach delivers the best of both worlds: the authenticity of live respondents and the scalability of synthetic data.
Hybrid approaches also provide an opportunity for ongoing validation. By continuously comparing synthetic outputs with live data from the field, researchers can fine-tune their models and ensure they remain relevant as markets evolve.
One of the challenges in introducing synthetic sample in LatAm is overcoming the perception that it is a “shortcut” or a way to cut costs at the expense of quality. The reality is that when done right, synthetic sample can increase quality by addressing coverage gaps that traditional methods cannot reach efficiently.
Education is critical. Researchers, clients, and stakeholders need to understand how synthetic data works, what it can and cannot do, and how it fits into the broader research ecosystem. The more we demystify the process, the faster we can build confidence in its value.
Synthetic sample is not a passing trend. In LatAm, it has the potential to transform how researchers approach challenging recruitment, improve feasibility for large-scale studies, and deliver richer, more representative insights.
But success depends on doing it right, and that means:
Synthetic sample provides researchers with an innovative tool to ensure everyone’s voice is included in market research, at scale, and in ways that make research more inclusive, more efficient, and more effective.
Money habits aren’t formed in a vacuum. They’re shaped by the crises we live through, the culture we’re raised in, and the tools we trust to manage our future. Today’s financial landscape is being redefined by generational shifts, cultural influences, and emerging technologies, like artificial intelligence, each impacting how people save, spend, and invest.
Gen Z is proving to be more disciplined and frugal than other generations, driven by the economic crises they’ve witnessed in their households and their determination to avoid the same pitfalls. They’re saving earlier, budgeting more carefully, and leaning on side hustles to build financial security. Compared to Millennials, Gen Zers lean toward spending less on experiences. These differences highlight how context and culture influence money decisions in ways that numbers alone can’t explain.
Race and ethnicity also significantly influence financial priorities and levels of trust in financial institutions. Disparities in homeownership, retirement readiness, and perceptions of financial health remain stark, underscoring the need for inclusive financial education and culturally relevant outreach. Providing access alone falls short of creating solutions that meet people where they are.
And while technology is reshaping the landscape, trust remains a hurdle. Many consumers are open to using AI for simple financial tasks, but skepticism grows when higher stakes are involved. The key is balance. Pair AI with human oversight, clear guardrails, and transparent communication to build confidence across generations.
On this episode of The New Mainstream podcast, Aijaz Hussain Shaik, Senior Director of Thought Leadership & Research at Empower, unpacks how generational shifts, cultural influences, and technology are redefining financial behavior and what it takes to create more inclusive financial systems.
What does feminism mean to U.S. women in 2025? The #MeToo movement is waning while toxic masculinity appears ascendant. Will the gains made by previous generations be lost? Our latest nationally representative survey of 739 women aged 18 and older uncovers a complex and often divided landscape. Views on feminism, gender equality, and social progress are shaped by significant generational, cultural, and racial differences. While the term “feminist” remains contentious, its ideals are supported by a vast majority of women.
Download the report here.
The first wave of feminism started in the mid-1800s with the suffrage movement. However, not all women supported it. Many believed that men and women had distinct but complementary roles: men in the public sphere (politics, business), women in the private sphere (home, family, church). The suffragists, however, were successful in gaining equal property rights, educational access, and most notably, the right to vote in 1920.
The term “feminist”, however, didn’t gain popularity until the second wave of feminism in the mid-60s when a new generation of women fought for reproductive freedom and workplace equality. Popular films and television shows of the era like Norma Rae and Mary Tyler-Moore celebrated strong, independent women, while songs like Aretha Franklin’s Respect and Helen Reddy’s I Am Woman became popular empowerment anthems. Around 30% of women identified as feminists at that time, but even then, the term was polarizing.
By the 1980s, during the Reagan era, feminism started facing a backlash. Movies like Fatal Attraction and sitcoms like Family Ties either demonized women or suggested that they return to more traditional roles. This trend continued into the 90s with conservative commentators like Rush Limbaugh coining the “feminazi” label, which conflated feminism with extremism. Powerful women at the time like Hillary Clinton felt pressure to conform to traditional roles and, in Clinton’s case, change her last name from Rodham to Rodham-Clinton to just her husband’s name, Clinton.
The early 2000s saw the rise of conservative media personalities like Sean Hannity, Bill O’Reilly, and Laura Ingram, who promoted traditional gender roles and mocked feminist ideals. That changed in 2017, when the #MeToo movement and the Women’s March (a reaction to Trump’s first term) reenergized the conversation around reproductive rights, workplace harassment, and gender-based violence.
In 2025, under Trump’s second presidency, the pendulum appears to be swinging back toward cultural conservatism. Thus far, we have seen the following:
Today, American women are nearly evenly divided on the term “feminist”:
This topline, however, masks deeper generational and racial divides. Our research found that Asian women lead in self-identifying as feminists, but they also express the most uncertainty. Gen Z women are the least likely to reject the label, whereas Millennials are the least likely to adopt it.
These trends suggest a growing discomfort with ideological labels, even as support for feminist principles remains high.
Despite decades of activism, only 44% of women in the U.S. know that March 8th is International Women’s Day (IWD). This limited awareness may be tied to IWD’s roots in European socialist and labor movements, and unlike Mother’s Day or Valentine’s Day, IWD isn’t easily monetizable, so major U.S. retailers and media don’t generally promote it.
Some key facts from our study:
Our research found that most women define feminism as promoting gender equity, eliminating discrimination, and advancing equality. Gen Z women are especially likely to view feminism as fairness across genders. Yet despite broad agreement on its goals, fewer than 1 in 5 women believe society views feminism positively. Nearly half say it’s perceived negatively.
Other findings include the following:
Women identify the biggest obstacles to gender equality as:
When asked which areas need the most urgent attention, women pointed to:
The report breaks out those findings by ethnic and generational differences. With some issues like pay equity resonating most with Boomers at 78% vs. 49% of Gen Z, and others like stopping gender-based violence resonating with 61% of Latinas but only 39% of Black women. Despite these priorities, optimism about the future of gender equality remains muted. Only 43% of women report feeling optimistic. Optimism is highest among Asian women and Boomers, while Gen Z and Hispanic women are notably more skeptical.
While much work still needs to be done to achieve true gender equality, 43% of women are optimistic about improvement, while only 19% express pessimism. Support for gender equity is strong, but the feminist label remains polarizing. Younger and diverse populations, however, are picking up the mantle and pushing the conversation forward.
At ThinkNow, we believe in amplifying diverse voices to inform brands, policymakers, and advocates on where the conversation on gender equality is headed. Whether or not women embrace the label “feminist,” the values behind it, such as equality, justice, and dignity, remain widely shared. Those ideals matter, regardless of what we choose to call them.
Download the report here.
Veterans are undoubtedly our nation’s heroes. They bring with them a set of skills honed through years of service, skills that, if clearly communicated, can achieve the same success in business that they achieved on missions. The key to transferring these skills to civilian roles is breaking down what was done in a military context into terms that show hiring managers how those capabilities can drive results for a company.
Yet too often, employers overlook or diminish this value. Without awareness, unconscious bias and outdated stereotypes can pigeonhole veterans into narrow roles. The reality is that the discipline, strategic execution, and situational awareness cultivated in service are exactly what organizations need to navigate the complexity of the marketplace and rally teams toward common goals. Employers who are intentional about being inclusive and who make the effort to understand these skills gain access to a high-performing, job ready talent pool.
Community-building within organizations amplifies that impact. Veterans’ networks, for example, offer mentorship and onboarding support from the start of the hiring process. Once hired, employee resource groups provide safe spaces that foster belonging, educate allies, and dismantle biases, ultimately creating an inclusive workplace culture. Even smaller companies can take meaningful steps by partnering with local veteran groups to source talent or provide job training.
In this episode of The New Mainstream podcast, Ari Friedman, Talent Development Manager, Global Early Careers at Microsoft, offers strategies for translating military skills into business impact and creating workplaces where veterans can thrive, benefiting both talent and employers alike.
Synthetic sample is changing how we think about data. Once static, data is now dynamic, opening up possibilities we’re only beginning to understand.
No, we’re not talking about bots or fabricated data. These are intelligent models generated from real data that allow us to simulate behaviors, attitudes, and responses of specific populations with a level of precision and control that traditional methods simply can’t deliver. It’s a way to fill the gaps where panels fall short, whether due to logistical limits, participation bias, or market fatigue.
It matters because the landscape has changed. It’s harder than ever to get people to participate in surveys, especially within diverse and underrepresented communities. There’s fatigue, there’s distrust, and there’s noise.
And while the industry continues chasing the “ideal respondent,” at ThinkNow, we’re building robust analytical models based on real data that allow us to generate insights with more agility, diversity, and depth.
It’s important to note that synthetic data is not a replacement for people. It’s an amplifier.
Synthetic doesn’t replace human voices, it only enhances them. It enables us to utilize our existing data in more strategic and responsible ways, such as helping to fill data gaps, anticipate trends, and design better questions.
And when we combine that with our real, culturally diverse communities – people who are genuinely motivated to share their opinions – the result is a robust, more agile, and far more representative insights ecosystem.
Step 1: Integrate real data from our multicultural research.
Step 2: Apply AI and machine learning techniques to model specific audiences.
Step 3: Validate models through observable behavior and direct feedback.
We do all of this with a team that understands culture, context, and the responsibility of representing authentic voices within synthetic models.
We’re moving past methods that only work “when everything goes right.” We’re investing in research that’s more resilient, more human, and yes, more intelligent. Because in the end, it’s not just about collecting responses. It’s about understanding people. With synthetic sample, we’re opening new ways to do exactly that.
Want to learn more about how ThinkNow is using synthetic sample to improve the accuracy and diversity of research? Reach out. We’re building the future of insights, and you can be part of it.
We're halfway through 2025 and one thing is undeniable: AI is no longer on the horizon, it is in the room. For the market research industry, this has come faster than most expected. What felt like an existential threat just a year ago is now transforming how researchers approach everything from segmentation to recruitment to data analysis.
But as AI becomes embedded in our workflows, a critical question arises. Are the datasets powering these models truly inclusive? Do they reflect the diverse populations researchers aim to understand, or are they building the next generation of tools on top of the same old blind spots?
Market research has long struggled with inclusivity. Reaching Spanish-dominant Latinos, Gen Z respondents and even male participants has always been difficult. Despite decades of effort, many of these groups continue to be underrepresented in online panels and large-scale studies.
Now, imagine deploying AI on top of these incomplete datasets. Instead of closing representation gaps, AI trained on biased data risks amplifying them at scale. Biases that were once isolated can now be baked into algorithms and amplified across the entire research ecosystem, undermining the potential of AI to drive more inclusive insights.
When AI began gaining traction in the industry, initial skepticism emerged among some researchers, particularly regarding the use of synthetic data and AI-powered moderators. These tools seemed impersonal, disconnected from the human insights that drive understanding and trust among respondents.
Yet, over time, AI has proven itself capable of complementing, rather than replacing, researchers’ work. Instead of diluting what makes insights meaningful, AI can expand them by enabling researchers to finally address representation issues that more conventional methods have never been able to. This shift has prompted a more intentional approach to innovation. If synthetic data is going to shape the future of insights, it must be inclusive by design, representing the full diversity of the populations it aims to model.
The market research industry is uniquely positioned to lead in this space. While many tech companies face lawsuits for training AI on copyrighted or illegally scraped data, researchers have operated under strict privacy laws like GDPR and CCPA for decades. Upholding consent, data stewardship and adherence to ethical standards has been the norm.
Our datasets are not only large, but they are also permission-based and carefully vetted. This makes them ideal for training AI models that need to mirror real-world diversity.
But it is not enough to have access to data. The same rigor applied when building representative samples must be applied to training AI models. This means proactively identifying gaps, asking who is missing from the data and taking measurable steps to responsibly include them.
This brings us to the future of multicultural segmentation. Relying solely on broad demographic categories or historical internal datasets is no longer sufficient. Today’s consumers are multidimensional, and AI gives us the tools to see them more clearly.
To generate synthetic data that accurately reflects multicultural audiences, it is essential to incorporate information from historically underrepresented communities. This requires collaboration between technologists and cultural experts, as well as a commitment to designing systems that accurately reflect the reality of diverse identities.
For researchers generating synthetic datasets, combining privacy-compliant methods with culturally rich data points, powered by AI, helps ensure that communities often left out of the conversation are fully represented moving forward.
AI is not a passing trend. It is here to stay, and it is reshaping how we segment audiences, recruit respondents and activate insights. However, AI’s success depends on the quality and inclusiveness of the data behind it, and the researchers guiding its application.
For market research professionals, this is a challenge worth embracing. With deep expertise, ethical frameworks and a foundation in representative sampling, the industry is uniquely positioned to ensure that AI serves all communities, not just the most accessible ones.
The future of multicultural segmentation will belong to those who successfully integrate innovation and intention because the question is no longer whether to adopt AI, but how to use it in a way that advances representation.
Those investing in synthetic data and inclusive segmentation strategies play a crucial role in achieving this, and those seeking better representation in data must continue to demand it.
This blog post was originally published on Quirk's Media.
Let’s face it! Traditional research panels aren’t cutting it anymore.
For years, market research has relied on large pools of pre-profiled individuals, often referred to as “panels,” to generate insights at scale. And while panels gave us reach and reliability, they also lulled the industry into a comfort zone, where respondents became data points, not people.
But the world has shifted. Audiences have evolved. Attention spans have shortened. Expectations have skyrocketed.
At ThinkNow, we believe it’s time to rethink how we engage respondents not as panelists, but as people.
You know the type, the person who’s in 15 panels, knows the right answers, and is simply rushing to the incentive. They’re the product of outdated engagement models where surveys are transactional, not relational.
This results in flat data, low authenticity, and insights that don’t reflect reality, especially when researching diverse, underrepresented communities where trust and context matter.
The industry must move from mass reach to meaningful engagement. That means:
The online sample team at ThinkNow is building more than panels. We’re nurturing communities of people who want to be heard and willingly share their opinions, providing zero-party data brands can trust. Our approach combines cultural fluency, smart segmentation, and behavioural insights to go beyond checkbox answers.
We're also exploring new frontiers, including synthetic data modeling, AI-driven recontact strategies, and authentic content integration that makes surveys feel less like tests and more like conversations.
Because at the end of the day, insights don’t come from checkboxes. They come from connection.
It’s time we ask ourselves: Are we collecting data, or are we listening? The future of market research lies in making every respondent feel like their voice matters, because it does. Let’s ditch the dusty “panelist” label and treat our respondents like what they truly are: individuals with stories, context, and value.
When we do that, the insights take care of themselves.