At ThinkNow, we believe that understanding people starts with listening and getting beyond data points. By integrating artificial Intelligence (AI) into our online panels, we’re transforming how we capture and analyze open-ended responses in market research.
For years, open-text analysis was a manual, costly, and limited process. Today, AI enables us to process qualitative insights with unprecedented speed and precision, optimizing every stage of the research cycle. With these technologies, we don’t just analyze words; we interpret emotions, tone, and context, uncovering the authentic voice of the consumer that traditional methods often miss.
One of the most significant innovations is the ability to collect responses in audio or video format within the panel. This approach allows participants to express themselves more naturally, adding nuances that written text cannot capture. AI transforms these recordings into structured, automatically coded information, available in real time to analysis teams.
Moreover, machine-learning algorithms can assess the coherence and authenticity of responses, enhancing panel quality and reducing human bias. This results in more reliable, representative insights, especially in multicultural studies where expression and context are key to accurate interpretation.
This convergence of AI and online panels ushers in a new era in research, one where the boundaries between quantitative and qualitative blur, giving way to a faster, smarter, and more human ecosystem of insights.
ThinkNow is also expanding these innovations through synthetic sample, an advanced approach that broadens the reach and representativeness of studies without compromising methodological integrity.
If you’d like to learn more about how AI, online panels, and synthetic sampling are revolutionizing research, click here.
When we launched ThinkNow Audiences, our goal was simple: put multicultural data where media gets bought. We saw a gap between multicultural insights and how those insights were being activated in media buys, so we built a bridge.
Now, that bridge is getting wider, smarter, and faster.
ThinkNow Audiences 2.0 isn’t just a refresh. It’s a strategic evolution in multicultural research and programmatic media buying. We’ve doubled down on contextual relevance, expanded private marketplace (PMP) partnerships, and focused on what matters most to buyers – culturally relevant campaigns that drive top-line results.
Traditional multicultural targeting has often been limited to high-level demographics, like age, ethnicity, and language. While still useful, those markers alone do not fully reflect how people engage with media or express their identities in 2025.
Today’s audiences are fluid. They move between languages, cultures, and platforms depending on their mood, the moment, and the medium. So, our audience strategy needed to evolve to capture the nuances of today’s consumers.
ThinkNow Audiences 2.0 introduces a new layer of cultural context built around behaviors, affinities, and signals that reflect this complexity, including:
By mapping these signals, we’re creating segments that reach not only Latino, Black, and Asian consumers, but also those from other diverse backgrounds. They speak to who they are and what they care about in the moment they’re engaging.
The loss of cookies has made contextual data more valuable than ever. While much of the industry is still catching up, multicultural audiences have always been more effectively engaged through context, not just identity signals.
We’ve leaned into the shift to contextual by:
In short, we’re shifting from basic audience targeting to authentic audience connection.
A big part of our 2.0 rollout has been focused on private marketplace deals, where we’re seeing serious traction. The agencies and brands we work with are looking for:
PMPs allow us to deliver all three. They provide our partners with an easy entry point into multicultural activation, eliminating the need to overhaul their entire media strategy.
We’ve seen success working with Hispanic-focused agencies, Black-owned publishers, and general market programmatic buyers who want to reach growth audiences with more intention.
What makes ThinkNow Audiences different isn’t just the multicultural data. It’s how the data is created. Our segments are built on:
ThinkNow Audiences is not repackaged, generic data with a multicultural label on it. It’s original and culturally grounded, the result of over a decade of working at the intersection of culture, data, and media.
As we move into 2026, we are committed to making it easier for brands to meet multicultural audiences where they are in ways that are important to them.
ThinkNow Audiences 2.0 is a step forward, but it’s also an invitation to the industry to make multicultural marketing, central, not secondary, to data strategy to drive relevance in marketing and media. The future of audience targeting is not just more diverse, it’s more human, and that’s what we’re building for.
In today’s global marketplace, data has become the single most valuable asset for businesses. Every strategic decision, whether it’s a new product launch, entering a new market, or refining customer experience, is anchored in insights drawn from quantitative research. But here’s a reality check. The accuracy of research is only as strong as the panel it draws from.
That’s where proprietary panels enter the conversation.
Many organizations rely on third-party sample providers, but an increasing number are realizing that owning a proprietary panel can serve as a strategic driver of competitive advantage. Here’s why.
Third-party panels are convenient, but they come with risks, including duplicate respondents, fraudulent behavior, and a lack of transparency in recruitment. In a world where online fraud has become increasingly sophisticated, depending solely on external sources can expose your research to inaccuracies that undermine decision-making.
A proprietary panel, however, gives you control over respondent recruitment, profiling, and validation. You know exactly who is in your panel, where they come from, and how they’ve been verified. This control significantly reduces noise in the data and ensures the insights you’re analyzing are authentic.
When organizations conduct research over time to track brand health, consumer sentiment, or product adoption, consistency is critical. If the respondent pool changes dramatically between waves of a study, the insights can become blurred or misleading.
Proprietary panels allow businesses to maintain a consistent respondent base. This makes longitudinal studies more reliable and will enable you to compare data points over time with confidence. For a multinational organization, that consistency can be the difference between identifying a true trend and chasing a data anomaly.
A proprietary panel isn’t just a list of random respondents. It’s a dynamic database of deeply profiled individuals. You can segment by demographics, purchase behavior, attitudes, or any niche criteria that matter to your research.
This level of profiling enables businesses to conduct highly targeted studies, ensuring that respondents are genuinely relevant to the research question. For example, suppose you’re testing messaging for an electric vehicle campaign in Latin America. Your proprietary panel can instantly identify urban professionals considering EVs in Mexico City or São Paulo rather than relying on the broader, less-specific pools of third-party providers.
In cross-border research, one of the biggest challenges is capturing cultural nuance. Localized behavior, language, and attitudes can shift how respondents interpret survey questions. Proprietary panels built with a global footprint solve this by ensuring representation across diverse regions and markets.
By owning the panel, you’re not just sampling “a group of consumers,” you’re cultivating communities in specific regions. This enables stronger localization of surveys, leading to greater cultural accuracy and deeper insights into how consumer behavior varies between regions, such as Southeast Asia and Western Europe.
Respondents who join proprietary panels often build a relationship with the brand or research firm. With regular communication, fair incentives, and transparent practices, you cultivate trust.
This trust translates into higher engagement and reduced dropout rates during surveys. Respondents are more likely to provide thoughtful, accurate responses because they feel part of something consistent rather than a one-off transaction.
In contrast, third-party respondents often treat surveys as “quick clicks for cash,” leading to rushed or careless responses that weaken the data.
Given the specificity, building a proprietary panel might seem expensive. Recruitment campaigns, incentive management, and panel technology platforms all add up. But over time, however, the economics become clear:
Ultimately, proprietary panels don’t just protect data quality, they also protect budgets. For companies conducting frequent research, the ROI compounds quickly.
Every business is looking for an edge. Owning a proprietary panel sends a clear message to clients, investors, and stakeholders that you’re serious about data integrity.
It positions your organization as a leader that doesn’t just “buy insights” but invests in building a robust and trustworthy ecosystem to generate them. Industries such as consumer insights, healthcare, and financial services find this invaluable.
Moreover, in the era of AI-driven analytics, having clean, high-quality proprietary panel data also future-proofs your business. AI is only as smart as the data it’s trained on. Proprietary panels ensure that the data feeding your models is trustworthy.
In the rush to gather insights quickly, many organizations fall into the trap of over-relying on third-party panels. While they have their place, the risks of fraud, inconsistency, and lack of transparency can erode the foundation of decision-making.
Investing in a proprietary panel is a strategic move that builds an organization’s credibility by avoiding these pitfalls and providing accurate insights that reflect the voice of the consumer. If accurate quantitative research data fuels growth, proprietary panels are the engines that ensure the journey is reliable.
Artificial intelligence (AI) is rapidly reshaping society, but with its transformative power comes pressing ethical, cultural, and social questions. The conversation around AI often centers on new capabilities, but equally important are the implications for equity, transparency, and human values.
A key concern is the concentration of AI development in a handful of industries, particularly technology and finance, which risks creating tools that benefit only a narrow segment of society. When innovation prioritizes speed and competition, the so-called “AI race” can result in systems being released prematurely, riddled with bias, or inaccessible to much of the global population.
Language representation in AI models is another critical issue. Many large language models are predominantly trained in English, resulting in the underrepresentation of other languages and cultural perspectives. This imbalance not only limits accessibility but also reduces the quality of AI outputs. Advocates stress that LLMs trained on multicultural data lead to better, more representative systems, ones capable of reflecting the world’s diversity rather than reinforcing existing biases and stereotypes.
Still, the potential for AI to drive positive impact is significant. From creating accessible tools for immigrants navigating new systems to providing voice-based digital companions for older adults, socially conscious applications of AI can foster inclusion and improve quality of life.
On this episode of The New Mainstream podcast, Norman Valdez, CEO of BrainTrainr, discusses the urgency of developing responsible AI and highlights both the dangers of exclusion and the opportunities for technology to serve as a force for good.
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.