The sharing economy—where individuals exchange, rent or share goods and services with their peers—has gone global. And one prime example of a company successfully expanding in this economy is Airbnb. In the last decade since its inception, Airbnb has grown to operate in almost every country in the world. Let’s talk about how the company got there.
Airbnb is a pioneering platform that allows travelers to book sleeping accommodations and experiences (day trips, outings, walks around town) with locals, instead of booking hotels or other traditional accommodations. This allows tourists to deeply experience a city and neighborhood through the eyes of a local.
Airbnb’s global expansion has uncovered somewhat eye-opening insights about the global traveler’s language expectations and use, which in turn impact Airbnb’s localization strategy. The company’s trial and error with programs such as MT and machine learning have also led to surprises and adaptations in its localization approach. Six surprises in particular caught our attention.
1. Travelers aren’t that different from country to country
People are far more similar globally than you might expect them to be. It doesn’t matter if travelers are from China or Cuba or London—they all want the same authentic experiences. Travel expectations and wishes are a “globally shared phenomenon” as Airbnb has said. They want to meet people, they want to connect and they want to explore the world. They also have similar attitudes about how language is used when traveling. For example, tourists do not expect to have experiences in their own language when they go abroad.
2. The world is more multilingual than you think
In large parts of the world, many people are bilingual or trilingual. It’s easy to underestimate how many people are reasonably fluent in more than one language, particularly across Europe and APAC.
How does this manifest at Airbnb? The platform is now accessible in more than 30 languages, and many hosts can write their listings in more than one. This broadens their own sales potential without any help or additional cost.
On the other hand, it’s also easy to assume that all renters need translations. Some are happy to do business in English. Though they still may prefer translated content, not having it doesn’t necessarily stop a sale.
3. Users don’t expect flawless language
Even if a seller lists properties or excursions in a second language, the customer doesn’t expect the content to be perfect. They know their host in another country might not speak their language fully or at all.
Airbnb’s mission is about connecting people, and part of that magic is the endearing awkwardness of language barriers. Traveling means taking yourself a little bit out of your comfort zone, and struggling with the language—perhaps getting by with just a few words—is part of the authentic experience. That language challenge is indeed part of the fun.
4. MT may or may not be useful
Airbnb has used machine translation (MT) to translate some of their content, such as user reviews, but with some unanticipated results.
The team surmised that MT would be great for those who could not read the local language; obviously, it helps you better understand the content, right? But one of their findings was that the user’s experience with MT is not always better than reading a second language with a little difficulty.
Even though getting a translation is easy—an MT option is part of the interface—remember that some renters seem to not need that translation after all. People are using content as-is, untranslated, and still succeeding at booking their stay.
(Despite this, MT—and increasingly neural MT—still has a place in Airbnb’s program.)
5. Transcreation is difficult, but worth it
Airbnb ran a global marketing campaign around the idea that when two people from different cultures connect, there is “one less stranger” in the world. It’s a beautiful idea that’s central to Airbnb’s values: when people aren’t strangers, when they understand each other, there is more tolerance and inclusivity.
However, translating the phrase “one less stranger” into different languages was challenging. In English, this has a poetic, positive feel. In German, the word “stranger” is very close to the word for “foreigner”; it’s not “one less foreigner” that they wanted to say.
So, to avoid the message getting misconstrued, they transcreated it instead for each local market. This meant that Airbnb was able to preserve the value and intent of their message worldwide.
6. Machine learning can help with language selection
It’s not always feasible or logical to translate all content into all languages, but how does an enterprise decide what to do? Airbnb has started to use machine learning to predict which languages should be included in specific listings based on where past customers were from.
For instance, if a host publishes a listing in Notting Hill in London, and if most travelers booking there are from France, Portugal and Croatia, then the listing should be in French, Portuguese and Croatian. And this can happen at a grand scale: the data can show the listings all over the world that would most benefit from, say, being in French as well as English.
In this way, languages chosen will cater to most travelers to that area—and save money by not translating into every language.
As large global industries evolve their language programs, these kinds of lessons learned will surface. Gathering and analyzing data in unique ways can also spur new, unconventional methods to reach your customers and bridge cultural gaps. Airbnb’s global program is a forerunner: their a-ha moments and the resulting changes to their localization strategy provide insights that can apply to any global business.
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