by Vishal Anam, on Jun 4, 2025 9:00:23 AM
We would like to think that words are everywhere. But in Experience Management, words don't simply transfer information—they create perception, build (or destroy) trust, and stimulate emotional responses that vary wildly by market.
And here's the clincher: Literal translation is not localization. It's mere ornamentation if the message means nothing to the customer on the other side.
Consider this: A simple “Thank you” email after a transaction might feel thoughtful in the U.S., but in Japan, where politeness is already a baseline expectation, it can feel robotic unless it’s personalized and humble.
A “We’d love your feedback!” pop-up may work on an e-commerce site in Canada, but try that same tactic on a banking platform in Germany, and it can feel like surveillance.
A global online learning platform implemented the same UX vocabulary in every region—"crush your goals" and "smash your next lesson." Indian and Chinese students dropped out at a higher rate. Why? The upbeat motivational wording that was effective in the U.S. came across as too harsh and even childish to students who were expecting a more polite, guide-like tone.
When AI Goes Wrong—Linguistic Bias in NLP
Then add AI to the equation, and it gets even more complicated.
Most NLP applications—sentiment analysis software, feedback classification robots, chat summarizers—are learned on English-centric, Western-language-heavy data. That is, they're taught how to "read emotion" through one cultural filter.
The issue is: Emotion doesn't cross borders well.
Suppose: An NLP model learned on U.S. customer data reads brief responses such as "It's fine" or "Okay" as neutral.
But in Australia, “It’s fine” often means “I’m annoyed but being polite.”
In China, people may rate 7/10 while feeling very satisfied, because humility and moderation are valued. But a U.S.-trained model sees that as lukewarm sentiment and flags it for follow-up. ???? False alarm.
Real-World Impact:
One international hotel brand applied AI to analyze sentiment on reviews across the geos. Their dashboard indicated unusually low scores in France. On review, it was found that customers were being negative, but constructive. The AI identified passion as negativity. The French market was highly engaged, but the algorithm couldn't discern tone.
Business Translation ≠ Customer Translation
Jargon is another trap. What sounds suave in one market sounds ridiculous—or arrogant—in another.
“Frictionless onboarding” makes sense in a Silicon Valley pitch deck. But to a retail banking customer in Southeast Asia, it raises more questions than answers: What friction? What onboarding?
“Hyper-personalized engagement at scale” might fly in a B2B SaaS newsletter in New York. But to a client in Eastern Europe, it sounds like you’re experimenting on them with robots.
Case in Point:
A fintech firm attempted to scale up from the UK to Kenya with identical copy—full of "next-gen digital identity ecosystems." Clients there, more concerned about day-to-day mobile payment stability, were baffled—and churned. Local staff later rewrote the copy in more accessible terms: "Verify who you are. Secure your money." Retention went through the roof.
Bottom Line:
If Your Words Don't Land, Your Strategy Won't Fly. Words are your frontline experience in XM. If they don't speak to them, regardless of how lovely your design, witty your surveys, or sophisticated your AI, you'll lose the signal, misinterpret intent, and get what customers feel wrong.
So don't simply localize your platform. Localize your meaning. Begin with the question:
"What would this message sound like if we said it in their language—and their world?"
That's not simply improved copywriting. That's experience management.
