- Better to learn to play nice with the robots, as they seem to be the future...
When Google translate first hit the web, it wasn’t very good. Music fans were the first to prove this by loading lyrics to see what ridiculous or funny translations Google would generate. But things have changed; we’ve got jobs and got to use translation software from the likes of Google for spot-on answers most of the time.
You can learn how to order a beer in Japanese or how to say airport in German only by using Google Translate surreptitiously or otherwise. However, that doesn’t mean that pure MT (machine translation) is fluent, makes fewer mistakes than us, or is indistinguishable from a qualified translator. Yet, their level of complexity makes good use in the business marketing sphere.
Common Languages Boost Trade Flows
Businesses wanting to connect with foreign markets and scale their user experience have always been held back by language barriers. Hiring experienced translators can be costly and time-consuming, and you may not find people to translate the massive shares of content produced today.
MT software may be more affordable and quicker, but it’s not capable of maintaining the tone of voice companies need across languages and markets to protect their status and build meaningful customer relationships. But not all companies have a grim outlook on this. There are automated services in the likes of machine translation API such as Pangeanic’s ECO platform that plug into applications to retrieve content and deliver translations, normally with a very high level of parity versus humans.. Automated services may not be perfect, but they can run into common pain points of heavy project management work, high delivery costs, and topic expertise.
Smarter Sentiment Analysis For Effective Marketing Campaigns
Sentiment analysis can work wonders for any marketer. By understanding what audiences are thinking, businesses can tweak a campaign, product, and more to meet customer needs and let them know they’re listening. With AI-driven sentiment analysis, marketers can detect people’s feelings or opinions about a topic and unearth actionable insights that would otherwise be unobtainable.
Natural language processing algorithms analyze the sentiment of the audience and compare it against competitors or follow market trends and emerging topics. The most common application of sentiment analysis is social media monitoring or social listening, where marketers uncover the truest customer opinions as users feel freest to react fast and emotionally to what they see and hear.
In fact, it’s estimated that 83% of social media users who make a complaint or comment on socials expect a response the same day, and 18% want it right away. This analysis, coupled with the odds of AI, enables companies to target marketing campaigns directly and follow the response to find out what languages receive the most positive impact.
Whether you want to use machine translation to gauge the success of your marketing campaigns or eliminate the language barriers between collaborators, there’s no doubt that it has become a necessary tool for any marketing strategy. A smooth machine translation pipeline can drive customer satisfaction and global reach for businesses around the world.