Customer reviews are vital to the travel industry and many of the large hotel groups, airlines and online travel agents attract customers from many different countries, which means they receive reviews in many different languages. So, what’s the best way to manage this and get the most out of every review that’s submitted?
There are two main reasons why you should translate your reviews into all of the languages that your site supports. The first is for your own business to better understand your customers and their motivations and potential gripes. It’s often said that customer feedback is a gift – it gives businesses an opportunity to identify customer concerns before they escalate and impact business performance.
The second reason to translate reviews is for other customers. We know how vital reviews are to the customer’s decision-making process and having a body of review content in their own language may really help them complete a purchase and results in higher conversion rates.
That’s even true if reviews aren’t 100% positive. Customers are more likely to trust nuanced reviews and they tend to be suspicious if only positive reviews are visible. And if your reviews seem too good to be true, there are other travel review sites like TripAdvisor which they can easily access to read other customers’ opinions in order to make informed purchase decisions.
The challenge of scale
Businesses of any significant scale, particularly international ones, can attract a huge number of user reviews. TripAdvisor, for example, boasts a whopping 435 million reviews covering almost 7 million accommodations, restaurants and attractions in over 50 territories. Furthermore, the US-based travel and restaurant review site estimate it receives over 280 traveller reviews and opinions every minute.
This makes it hard to keep up with all the review content posted by users and potentially very expensive and time-consuming to employ human translators to translate it all. There’s also the issue with fake reviews and comment spam which are harder to identify when dealing with multiple territories and languages.
Many online operators are addressing the challenge of user-generated content translation using machine translation software. Although this doesn’t result in a flawless translation, it’s usually enough to get the gist of what the reviewer is trying to express and costs the fraction of the price of a professional, human translator.
Apart from making the reviews available in other languages, there’s also the need for managers to understand the message within the review in order to determine if they’re positive, negative or neural without relying on the rating the user has provided.
Sentiment analysis (the process of measuring public opinion about a brand or product) can now be conducted across several different languages and machine translation and natural language processing can be used to gauge the tone of multilingual reviews and other feedback when this is performed.
Multilingual reviews are also routinely machine translated for customer consumption with site owners asserting very little control over the process. Google, for instance, routinely uses machine translation on reviews for its mapping and search services.
When it comes to multilingual reviews for online travel agents, such as reviews embedded into accommodation pages, travel operators generally take the approach of combining machine translation with human input to try to increase the efficiency of the translation process while improving the quality of translated content.
If you’re dealing with a huge volume of review content, a post-edited machine translation approach is likely to be the most effective for your brand.
As travel sites adopt personalisation to better tailor content to users, understanding the message within review content becomes even more important. This means there’s more at stake here than just language translation – you also need to implement a system that can determine which reviews to serve to which audience members at any specific moment.
Machine translation is already being combined with natural language processing and relevance scoring to automate this process.
New language activity
In recent years, there has been a proliferation of content in other languages and a corresponding increase in activity when it comes to users writing travel industry reviews in languages other than English. And with a saturation of internet penetration in English-speaking markets and a huge growth in the online populations in emerging markets, this is a trend we expect to continue.
New audiences are also becoming increasingly active in the global tourism market and starting to both consult reviews and write their own. But they may be publishing these on local language forums or popular social media platforms in their own countries, which means they are less visible to established Western travel providers.
For travel brands, this brings a fresh new challenge of finding, understanding and reacting to this review content.
Providers need to keep abreast of not only the languages themselves but also where the review activity is being published. New third-party sites and forums can emerge quickly and brands need to stay abreast of changes in the internet landscape that impacts on them.
Discoverability of reviews is becoming more of an issue for travel providers as new language audiences become increasingly important to their bottom line.
Most hoteliers and tour operators are familiar with Tripadvisor but they may not know Baidu’s travel forum, a popular resource for the Chinese traveller, or travel-focused website QYER or Mafengwo, where Chinese travellers share their experiences often in some depth.
This means travel providers may not be aware of review activity that’s impacting their brand in other territories. Brands are increasingly being challenged to stay on top of a more complex landscape of review activity.
It’s not just a case of translating multilingual reviews in unfamiliar languages. Travel industry professionals also need to understand the review landscape, the review sites favoured by audiences in different markets and the cultural significance of reviews in different markets where users have different habits and expectations and use these resources to better tailor products and services to travellers in global markets.