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.
In the digital age, consumers no longer interact with brands through a single channel. Today, a single customer might discover a product on Instagram, research it on a website, receive a promotion via email, and finally make the purchase in a physical store or on an app. This fragmented and dynamic behavior is what we know as omnichannel.
But what does this mean for those of us in market research?
Traditionally, market research focused on more linear touchpoints. Today, the challenge is to map a user experience that unfolds across multiple platforms, devices, and moments. Omnichannel has transformed not only the way consumers shop but also the way researchers study them.
It is no longer enough to ask what they buy or where they buy it. We now need to understand how consumers move between channels, when they prefer one over another, and why they make certain purchase decisions in specific contexts.
Let’s look at what market research offers in this new landscape.
Understanding omnichannel behavior requires localized approaches in markets like Latin America, where digital adoption is growing but diverse. For example, in some countries, WhatsApp is key, while in others, e-commerce apps or marketplaces dominate the scene.
This is where culturally contextualized market research becomes essential. It’s not just about knowing what consumers do, but understanding why they do it based on their social, economic, and digital context. A middle-upper socioeconomic consumer in Mexico City may trust delivery apps more, while someone in rural Peru might prefer informal commerce or local fairs, even if they saw the promotion on social media. Without understanding these nuances, any omnichannel strategy remains incomplete.
The key takeaway is this: omnichannel is here to stay, and with it comes new opportunities to gain deeper insights into consumer behavior. Brands that align their marketing strategies with actionable insights from solid market research adapted to the omnichannel environment will be the ones that stand out.
Because in a world of multiple channels, the true differentiator remains customer knowledge. And today, that knowledge requires listening and connecting the dots between every click, conversation, and step in the consumer journey.
Imagine market research as navigating a vast ocean. For years, we've used simple maps – surveys and panels – to guide us. But the ocean is changing, new currents are emerging, and those old maps just aren't enough anymore. That's where the new online sampling comes in – it's like having a smart compass that shows you exactly where to go and enabling researchers to collect data from diverse, geographically dispersed audiences quickly and efficiently. Removing barriers like travel constraints and logistical delays offers a more accessible and cost-effective way to reach the right respondents. Think of it as a “smart compass” getting you precisely where you want to go.
Think of it this way:
This new approach to online sampling can help businesses:
The future of online sampling is all about being smarter, faster, and more personal. It's about having a smart compass that helps businesses navigate the ever-changing market and reach their destination successfully. It's not just about collecting data; it's about using that data to make better decisions and build a stronger business.
Online sampling has revolutionized the way businesses gather insights and feedback. However, the rise of digital platforms has also heightened the risk of fraudulent activities. In this blog post, we'll delve into the strategies and techniques available to detect and prevent fraud in online sampling.
Let’s start with a definition. Online sampling fraud occurs when individuals or groups manipulate the sampling process to obtain incentives or rewards without providing genuine feedback. By “gaming the system,” these fraudsters create favorable outcomes for themselves to the determent of the research. Some common types of fraud include:
Preventing and detecting fraud requires a proactive, multifaceted approach leveraging technology and human intervention to identify and eliminate threats effectively. Here are a few strategies to do that:
Fraud is an industry-wide problem, not an isolated event. By collaborating with industry peers and adopting proactive strategies, sample companies can significantly reduce the risk of online sampling fraud and ensure the accuracy and reliability of their insights. As technology advances, so too does fraudulent tactics. To stay ahead of these evolving threats, organizations must invest in robust fraud detection and prevention measures. By doing so, they can drive successful business outcomes for their clients.
Nowadays, product reviews have become a crucial tool for both consumers and brands. Every comment posted online is a valuable source of consumer data. Thanks to Big Data, market research agencies can analyze thousands, even millions, of product reviews quickly and efficiently, allowing them to gain deep and actionable market insights about how their products are perceived.
Big Data in review analysis goes beyond simply tallying positive or negative comments. Advanced natural language processing (NLP) tools and machine learning can help identify hidden patterns and trends. For instance, a market research agency can analyze reviews to pinpoint product features that receive the most negative feedback, highlighting areas for improvement. This type of analysis is critical because it reveals both the reasons behind the comments and the broader impact on consumer perception.
Real-time analysis is another significant advantage of Big Data for product reviews. In the past, companies relied on traditional market research studies that required more time. With Big Data analysis tools and online consumer panels, brands can now access instant insights. This enables them to quickly respond to any shifts in consumer perception, a key advantage in both B2C and B2B market research.
Reviews provide data not only about the product itself but also about different market segments. Big Data enables companies to identify consumers in more detail, allowing for more personalized marketing strategies and product adaptations. Doing so helps brands optimize their campaigns and better connect with diverse audiences.
Companies like Amazon have used Big Data to analyze reviews, utilizing advanced algorithms to filter comments and identify trends. Other companies, like Nike, adjust their products based on insights gathered from consumer feedback.
The future of market research is closely tied to companies' ability to listen and respond to consumer opinions. Big Data turns reviews into a powerful tool for gaining market insights and improving products and services. Companies leveraging this technology will be better positioned to deeply understand their consumers and stay ahead of the game.