In today’s business environment where knowing what your customers think is critical to staying ahead, user-generated information is truly king. The content generated from social media applications, discussion forums, and wiki sites all contain a treasure trove of information for companies looking to gain insight into customer behavior and perceptions. For most businesses, however, the awareness gleaned from analyzing all this Big Data only goes as far as the company’s native language allows.
A company based in the U.S., for instance, may not have the means or budget to translate and analyze social content generated from their Japanese or Portuguese markets. But ignoring what international audiences have to say about your products, solutions or brand can equate to missed revenue opportunities. After all, the 275 million internet users in North America only represent slightly more than 10 percent of the world internet population.
Language is at the core of any global business, but for most companies, accessing the right information, at the right time, and across languages to influence business decisions and customer experience initiatives is difficult. Current translation requirements, particularly for business intelligence purposes, require fast turnaround times that traditional translation processes can’t support. In addition, translation efforts are typically focused on publishing content across multiple channels and languages, not translating multilingual content into a single language for analysis. As such, internal processes are not setup to support these efforts. Finally, translation is often handled as a separate process and a separate team, which adds additional steps into the process.
So how are companies accessing this critical global customer data?
According to IDC, the use of a SaaS-based machine translation service for handling multilingual content is the key to tackling these Big Language challenges and staying competitive in an increasingly globalized market. With machine translation, user-generated content can be automatically translated into a single language, and then fed into integrated text analytics solutions. This gives businesses access to all foreign language data containing global customer insights and business trends, providing organizations with more complete data to make business decisions and enhance the customer experience.
Here are a few examples of where this type of solution can be used for accessing and analyzing multilingual data.
Sentiment analysis: Knowing what a customer has to say about your products or brand is valuable, and accessing information across languages from blogs, forums, correspondence, product reviews or surveys provides valuable business insights. Translating all this data into a single language using machine translation provides companies with the ability to discover how consumers are using products by region and language and what improvements could be made at a targeted level to improve customer sentiment about a company and its brand. This is priceless when determining strategy, designing new product rollouts and improving the customer experience.
Predictive analytics: Predicting customer behavior requires large amounts of historical data. Having access to this data from global sources allows companies to predict behaviors and patterns by region and language for better business planning. This can save money long term and enables managers to allocate resources to their proper places based on hard facts and data. Machine translation can be utilized to translate all information into one language so a single team within the company can decipher the information globally and plan for product and solution capabilities.
Search: Within a global organization, employees and customers need to search for information that exists in multiple locations and languages. Adding machine translation to the enterprise search process can deliver more relevant results because when keywords are translated, documents can be searched in their native language and more accurate results are returned. For an employee searching for key data in customer survey responses, for example, translating the results on the fly enables timely access to information so it can be taken into account for upcoming initiatives.
Every day, companies are providing information to customers – in one language or many. For this reason, it is important to pay attention to what customers are saying so companies can continue to add value to products and solutions, as well as help build loyal customers and brand advocates. This all starts by listening. Adding language to existing text analytics solutions creates opportunities to listen globally and access more accurate, actionable information to build a more solid foundation that delivers a positive customer experience across multiple languages and geographies.
Keith Laska is the CEO of SDL Language Technologies.