Stop Studying the Same People: Sampling Diversity Before Cross-Cultural AI

Why studying the same people leads to blind spots and how culturally diverse insights can drive better products and smarter decisions.

If you and I both call a car “red,” do we see the same thing? Could it be that you experience red the same way I experience blue? 

In 2006, a team of researchers set out to study the different ways people experience color. They interviewed members of the Himba tribe, a semi-nomadic group in Namibia who lead a hunter-gatherer lifestyle that involves close interaction with natural landscapes. It quickly became clear that the Himba categorized colors differently from people in Western societies. What we would call blue in English, they did not distinguish from lime green: both were called buru. Meanwhile, both lighter and darker shades of green belonged to other categories (dambu and zuzu, respectively).1

This difference may seem inconsequential, but it has deep implications for how we communicate with one another. Color bears rich associations. Blue, for example, calls to mind the ocean and the clear sky, denim jeans, and business suits. If an advertisement features a blue car, it conveys a sense of calm and stability to Western audiences. How different would its effect be if it also evoked the more playful associations of bright green, like sports fields and sour candy?

This contrast in language seems to reflect a difference in what our environments train us to see. We notice—and name—what matters in our world. In the West, we encounter a range of synthetic colors, with both blues and greens abounding. In Himba society, surrounded by desert and grasslands, distinguishing between shades of green can make a world of difference. This is not coded in our DNA: Homer, writing in Ancient Greece, described the sea as “wine-dark,” attributing little importance to its blue hue. There are cultural reasons that can explain why color perception differs between groups, and these provide both pitfalls and opportunities for those looking to attract consumer attention.

Large companies looking to connect with customers of diverse backgrounds need to consider how culture changes our perceptions. If not, they risk limiting the effectiveness of their outreach efforts to people from the same backgrounds as those in their marketing departments. Opportunity waits at the margins; valuable insights can be gained by expanding the kinds of perceptions you listen to.

Eyes on the West

Since the beginning of modern research, studies have oversampled subjects from countries that are Western, Educated, Industrialized, Rich, and Democratic (WEIRD, for short). The effect was first noticed in psychology, where a paper found that 80% of study participants are WEIRD, even though these societies make up only 12% of the global population.2 However, the effect extends beyond the bounds of academic social science. As of 2023, North America alone accounted for over half of global market research revenue.3 Meanwhile, AI voice chatbots have difficulty understanding speakers with accents, owing to a lack of accented English in their training data.4

It might seem natural that companies competing for consumer spending would focus on WEIRD countries. Western consumers have the greatest purchasing power, and so are sometimes considered the most “valuable” customers (a bias that risks overlooking emerging markets where innovation and growth are accelerating). The average household in America, for instance, has four times as much disposable income as its counterpart in Mexico.5 And corporations headquartered in the West can only be expected to draw on customers in their home countries, where their customer insights divisions are located.

But even businesses targeting WEIRD customers should mind the diversity of their insight pools. Entire markets of potential customers will be overlooked by companies that only cater to born-and-raised Westerners. AI systems that struggle to understand foreign accents, for instance, will be unable to reach many non-native English speakers.

What’s more, companies that draw on the insights of other cultures can often find solutions their competitors have missed. Condiment manufacturer Heublein, for instance, discovered that many Americans had such a strong, unrealized preference for French-style mustard that they would give up their Heinz forever after trying Grey Poupon once.6 If you want to find opportunities others have failed to see, you should look where they haven’t looked.

Cultural nuance matters even within supposedly homogeneous markets. The Grey Poupon example points to another problem: it is not enough to simply add non-WEIRD subjects to your studies. Overwhelming diversity exists both within the WEIRD world (for example, between French and American condiments) and beyond it. A Hadza hunter-gatherer would have little in common with a Mumbai office worker, despite the fact that they both live outside the West. We should think of WEIRDness more like a heuristic than a shortcut. A study lacking non-WEIRD participants is likely to be insufficiently diverse, but simply adding those participants will not ensure adequate range.

The Tech Fix?

Some creativity will be needed to solve the logistical issues at play. It would be extraordinarily difficult to assemble an in-person focus group capable of representing the full diversity of the world’s population. However, modern technology offers a few possible solutions.

Synthetic focus groups, in which AI-generated subjects give feedback on a product, are not limited by the people researchers are able to recruit. This is great for establishing quick, big-picture overviews. However, the large language models used to simulate these subjects have biases of their own. Their training data, from which they learn how to simulate focus group participants, is overwhelmingly WEIRD in origin.7

Because of this, AI models can imitate the voices of non-WEIRD subjects without actually getting at what they might say. A study from Cornell, for instance, showed that Indian writers were more likely to sound American when using AI. LLM-enabled participants more often listed foods such as pizza as their favorites, and neglected to include specific details drawn from Indian life.8

Researchers should take advantage of telecommunications technology to diversify their customer insight pools as much as possible. Online focus groups have their limitations (most notably, low participation and low engagement among participants), but when paired with other possible solutions, they can provide a useful reach beyond the pools available in person.9

Ultimately, there is no magic bullet that can provide perfect diversity in market research. Organizations looking to learn how the world thinks should use multiple strategies in concert: first gather a synthetic group, then use its insights to inform an online survey, then gather a meaningfully diverse in-person focus group, and so on. As difficult as this seems, the potential reward is immense. Whether it’s finding a new product niche (as Heublein did with Grey Poupon) or tapping into a market segment your competitors have missed, companies stand to gain by looking beyond their traditional demographics.

References

  1. Roberson, D. (2006). Colour categories and category acquisition in Himba and English. Progress in Colour Studies:  …. https://doi.org/10.1075/Z.PICS2.14ROB
  2. Azar, B. (2010, May 1). Are your findings 'WEIRD'? Monitor on Psychology, 41(5). https://www.apa.org/monitor/2010/05/weird
  3. Raphael, B. (2025). Topic: Market research industry. Statista. https://www.statista.com/topics/1293/market-research/
  4. Gorny, T. (2024). Why chatbots must tackle the ‘accented’ english challenge. Forbes. https://www.forbes.com/sites/tomasgorny/2024/08/26/why-chatbots-must-tackle-the-accented-english-challenge/
  5. Household disposable income. (2024). OECD. https://www.oecd.org/en/data/indicators/household-disposable-income.html
  6. Gladwell, M. (2004, August 30). The ketchup conundrum. The New Yorker. https://www.newyorker.com/magazine/2004/09/06/the-ketchup-conundrum
  7. Phillips, S. (2025). Solving AI’s WEIRD bias: Contextualizing with consumer data. Forbes. https://www.forbes.com/councils/forbestechcouncil/2025/09/23/solving-ais-weird-bias-the-case-for-contextualizing-with-consumer-data/
  8. Waldron, P. (2025). AI suggestions make writing more generic, Western. Cornell Chronicle. https://news.cornell.edu/stories/2025/04/ai-suggestions-make-writing-more-generic-western
  9. Poynter, R. (n.d.). The truth about bias in market research and insight communities—Platform one. PlatformOne. Retrieved 20 October 2025, from https://www.platform1.cx/blog/the-truth-about-bias-in-market-research-and-insight-communities

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