Many international businesses use localization services to adapt their content to a specific region or market. A step beyond translation, localization can help when products or services need to be adjusted to local market preferences and cultural nuances. Even so, many organizations find localization and translation services to be siloed within a specific department, leading to costly inefficiencies that can easily be avoided.
In contrast, operationalizing language throughout the entire company, giving ownership to a centralized team, and leveraging machine translation technology can help international businesses scale much faster. This approach, called Language operations (LangOps), breaks localization efforts out of their siloes and provides real revenue and brand-building potential. Let’s learn more about localization, LangOps, and how Unbabel can help make language a strategic priority.
What is localization?
Localization services require adapting the content to local audiences, dialects, or cultural nuances. It’s different from translation because it involves giving a product or service the look and feel of the target market.
There are a few different aspects to the localization process, including:
Adapting design and layout of the product, website or application to show translated text in the local language
Changing the formatting of dates, currencies, times, etc. for target locations
Modifying content or graphics for the tastes and cultural preferences of a target location
Addressing legal or compliance requirements of a local market.
Traditional localization departments often report to product or marketing teams, depending on what they’re localizing and why. Some departments that have strong multilingual translation needs, such as customer service, haven’t been a part of the localization department. Many organizations have departmental language silos, where Unbabel’s approach to LangOps may be a better fit.
How LangOps differs from translation and localization
LangOps uses existing tools in the technology stack to help anyone communicate in any language. Compared to localization and traditional language translation services, LangOps can help build strategic momentum for language across the organization, ease the burden on localization teams, and prevent duplicated effort.
Typically, a single department owns individual localization efforts in the company. For example, localizing a website or a digital campaign for different audiences is owned by marketing. Most often, the practice involves translating and regionalizing websites, software, and other products piece by piece. Localization efforts often rely on outsourcing, an approach that doesn’t scale as the business grows into additional markets. In addition, ensuring translation quality can be a difficult and manual process that falls on a single person or small group of project owners.
A LangOps approach, on the other hand, would have all language-related efforts roll up to a chief operating officer or a dedicated LangOps executive or team. Instead of viewing localization services as one-and-done projects, language becomes a strategic imperative. Statistics show this approach can impact the bottom line. A recent study from Intercom showed that only about a quarter of people find they can get customer support in their native language. Ignoring the impact of language could be problematic: 70% of people would feel more loyal to companies with native language support — 35% of them even said they’d switch products altogether.
Tactically, a LangOps team would focus on how the entire organization can roll out into a new market, from sales to marketing to customer service and beyond. They’d consult about how to do business in a local market, and help individual team members make the smartest use of AI.
They’d choose a multilingual machine translation technology like Unbabel that can help equip anyone to communicate in multiple languages and measure the success of these technologies over time.
Let’s look at a customer service example to understand how Unbabel makes translation and localization efforts much easier. First, a customer service agent would receive a customer email question in the customer’s language (for example, French). Unbabel would translate the French email into English, the agent’s native language. The agent would reply in English, Unbabel would translate their response back into French, and an editor would check the translation to ensure that it’s localized appropriately depending on the customer’s region (e.g. French Canadian vs. European French). Beyond email, Unbabel also supports multilingual chat workflows.
Interestingly, people on the team help AI systems like Unbabel improve, while Unbabel helps people handle work efficiently and effectively. Human-in-the-loop machine learning can help teams scale by using state-of-the-art machine learning models and fine-tuning them with human input. This approach means organizations can focus more on hiring experts, versus native language speakers.
Making the transition from localization to LangOps
For localization experts, making the transition to LangOps is intuitive. Instead of relying on native speakers alone, LangOps teams become the learning engines for AI, helping to improve the system’s use of terminology, and making translations more fluent. A major priority is adapting the machine translations culturally to the target language.
Every language has its own way of expressing certain emotions. For example, the Japanese language has several levels of formality, depending on who you are speaking to, and who is speaking, while business communications in English may be far more casual. Retraining a machine translation system to understand these nuances is a critical part of ensuring the success of these efforts.
Rather than handing individual assignments to freelancers or outsourced providers (as localization usually entails), the LangOps team can look at the company’s strategy for growth or expansion, and decide on the best course of action. They can make these decisions with the benefit of the full perspective of the entire organization, versus the single project or task they’re working on. Many times this requires evaluating the performance of machine translation software, using open-source frameworks such as MT-Telescope or COMET. Once the selection process is complete, centralizing all language efforts on a single platform can make it easier to measure their success over time.
For example, the Unbabel Portal provides insights on key metrics such as translation quality, volume, turnaround times, and translator performance. These insights can help teams understand where to prioritize language efforts, and where current efforts need improvement. Using Portal, teams can perform a quality analysis for every language pair, across every channel. From there, they can dive deep into specific customer conversations to perform spot checks on translation quality.
Ultimately, this approach helps teams evaluate their translation pipelines, understand problems and quickly fix them. Many teams start with customer service as their gateway to LangOps, and quickly scale this approach across departments to create even more economies of scale across the organization.
