Expanding Synthetic Sample in LatAm: Balancing Trust and Accuracy

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:

1. Can synthetic data be trusted?
2. How do we ensure it reflects reality, especially in diverse and dynamic markets?
3. What is the right balance between synthetic and traditional sample?

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.

Why LatAm is Ready for Synthetic Sample

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.

Building Trust in Synthetic Data

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.

Efficacy Through Cultural Context

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 Role of Hybrid Approaches

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.

Overcoming Perceptions

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.

The Future of Synthetic in LatAm

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:

• Using high-quality, culturally representative seed data
• Being transparent about methodology and limitations
• Validating synthetic results against real-world data
• Applying local expertise to model building and interpretation

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.