Increasing competition in the U.S. has some brands seeking new markets, including those south of the border. More widespread internet access in Latin America has opened a gateway to growth for companies looking to do business in LATAM. But some brands are stumbling through the learning curve of understanding the region’s consumers. One of the biggest challenges has been dealing with pre-conceived notions about Race and Socio- Economic status in LATAM, which differs from classifications done in the U.S.

Let’s take a closer best practices on sampling in LATAM as it relates to race and socio-economic status, and how technology deployed in-culture facilitates accurate data collection.

LATAM Race targeting CAN’T be done the American way.

Did you know that Hispanic/Latino is not a race, and it’s not the way people identify in LATAM countries?

Same goes for Latin Americans that are of Asian, African, Middle Eastern descent and so on. Latin America is a true melting pot, rich in diversity, comprised of descendants of immigrants from all corners of the world. Racial identifiers and socio-economic status vary from country to country.

For example, a Mexican American in the United States (such as myself) identify as a U.S. Hispanic. My counterpart in Mexico, however, would identify as Criollo (European descent), Mestizo (mix of two races). Same goes for Colombia, Brazil, Peru, Uruguay, Argentina, etc. These countries all have their race identifiers. Someone in Colombia that is of African descent can identify as Mulata or Mestizo, but in America, they would be African American or Hispanic/Latina. So many variations of race are possible, and people can choose what they want to be. While this sounds quite liberating, for sample companies, this can harm the integrity of the data if not accounted for.

LATAM Socio-Economic Status targeting CAN’T be done the American way.

Americans identify socio-economic status based on yearly household income. However, in Latin America, it is based on different measures, and each country applies it differently. Colombia, for example, goes by a government-defined stratification system (STRATA), Argentina goes by SAIMO, and Mexico uses AMAI and so on. As noted earlier, sample providers not accounting for these nuances during data collection are painting an incorrect portrait of a very diverse landscape, with far-reaching implications on product development, branding and positioning, and advertising, just to name a few.

Programmatic Algorithms (AI) CAN be done the American way.

ThinkNow is unique in this space, as we understand the LATAM culture, and the impact technology has on the integrity of the data. While LATAM’s internet usage lags North American usage, it’s expanding rapidly, driven primarily by the rise in smartphone use. Smartphones, for example, are now cheaper and readily available to more social classes in the region and is the primary way LATAM consumer access the internet.

Because of the uptick in internet access, our LATAM clients are moving traditional face to face qualitative studies online and are seeking a more consultative approach to online sampling. Not only is the quality of the sample higher and data collection faster, but the process is more cost-effective.

Conclusion

When launching Lantin American research studies, it’s imperative to create race algorithms that properly identify each LATAM country as well as apply the proper socio-economics measure per country. Otherwise, results will be open to misinterpretation. Combining technology, data, and cultural awareness is the perfect recipe to help ensure actionable insights and better business decisions for our clients.