Brazil is one of the most dynamic and diverse markets in Latin America. Yet for many research and insights teams, achieving true national representativeness remains a challenge.
At ThinkNow, we believe inclusive data leads to better decisions. That’s why we’re strengthening our operations in Brazil with a dedicated commercial team and an expanded strategy focused on accessibility, precision, and partnership.
Many business decisions in Brazil still rely heavily on samples concentrated in major urban centers and higher socioeconomic groups. While those audiences are important, they don’t fully reflect the country’s vast regional, cultural, and socioeconomic diversity.
To support more accurate and inclusive research, we’ve expanded our infrastructure to ensure access to respondents across all regions of Brazil — including harder-to-reach areas — and across diverse audience segments.
Today, ThinkNow supports research in Brazil with a panel of 877,222 Brazilian respondents, covering:
“Our goal is to support Brazilian research companies with the tools and reach they need to execute complex studies with confidence,” says Mario Carrasco, Co-Founder of ThinkNow. “Our focus is on precision, accessibility, and partnership.”
For Brazilian brands and research agencies managing multi-country studies, coordination can be complex and costly.
With panels in more than 17 Latin American countries and exclusive access to over 1.8 million U.S. Hispanic panelists, ThinkNow offers a streamlined solution for regional and cross-border research initiatives.
“As we combine a dedicated local presence in Brazil with transparent pricing and fast turnaround times, we’re offering agencies and brands a practical solution for both regional research and U.S. Hispanic expansion,” adds Roy Kokoyachuk, Co-Founder of ThinkNow.
Accessibility is a core part of our commercial philosophy. Our Brazil expansion includes:
This structure removes traditional financial barriers and allows small and mid-sized research firms to operate with the same flexibility as larger multinational agencies.
As reach expands, data integrity becomes even more critical.
ThinkNow leverages ThinkNow Shield, our proprietary fraud prevention system powered by Artificial Intelligence and advanced geolocation tools. This ensures high-quality sample protection across metropolitan and interior regions alike.
Our expansion in Brazil marks an important milestone in ThinkNow’s broader mission: to make inclusive, representative research more accessible across the Americas.
As the Brazilian market continues to evolve, we remain committed to partnering with agencies and brands that seek deeper cultural understanding and stronger regional representation in their insights.
To learn more about our Brazil panel or explore partnership opportunities, contact our South America leadership team.
Commercial Contact
Maria Victoria Gonzalez
Managing Director, South America
Email: mariavictoria@thinknow.com
As 2025 draws to a close, businesses worldwide are shifting their focus from reflection to preparation. End-of-year planning is no longer just about reviewing performance—it’s about anticipating what lies ahead. In this environment, quantitative market research has emerged as the compass guiding organizations toward smarter, data-driven decisions.
This guide explores how companies can leverage quantitative research to prepare for 2026, highlighting fresh approaches, emerging technologies, and practical strategies that go beyond the traditional survey-and-statistics model.
Quantitative research provides the numerical backbone of market insights. While qualitative studies capture the “why,” quantitative methods deliver the “how much” and “how often.” In a world where executives demand measurable ROI, quantitative research is the language of boardrooms.
As we enter 2026, the challenge is not whether to use quantitative research, but how to modernize it for speed, relevance, and strategic impact.
Traditional surveys often take weeks to design, distribute, and analyze. By then, consumer sentiment may have already shifted. In 2026, real-time data collection will become the norm.
This shift means companies can pivot strategies within days, not months—a critical advantage in fast-moving industries like retail, fintech, and consumer electronics.
Artificial intelligence is no longer a buzzword; it’s reshaping how data is gathered and interpreted.
For 2026, companies that integrate AI into their quantitative research will gain a competitive edge in foresight.
Markets are increasingly interconnected, but consumer preferences remain deeply local. Quantitative research must balance global comparability with local nuance.
This dual approach ensures that strategies resonate both globally and locally, a necessity for multinational firms entering 2026.
Surveys remain central, but they’re no longer sufficient on their own. Companies are increasingly blending multiple data sources:
By triangulating these sources, businesses can validate survey findings and uncover more profound insights.
As data collection intensifies, so does scrutiny. Consumers are more aware of how their information is used, and regulators are tightening rules.
In 2026, companies that prioritize trust and transparency will not only comply with regulations but also strengthen brand loyalty.
To translate these trends into action, here are five steps companies can take before the year ends:
The end of the year is not just a time for closing books. It’s a launchpad for innovation. Quantitative market research is evolving from static surveys into dynamic, AI-powered ecosystems that provide continuous, actionable insights.
As 2026 approaches, companies that embrace these changes will be better positioned to anticipate consumer needs, outpace competitors, and make data-driven decisions rather than rely on guesswork.
The message is clear: quantitative research is no longer about collecting numbers; it’s about creating foresight.
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.
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.
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.
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.
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.