In this special guest feature, Ken Behan, Chief Growth Officer at SYSTRAN, discusses new technology that is aiming to totally eradicate the problem of language as one of the primary factors limiting small businesses from operating abroad. Powered by big data, AI and deep learning, Neural Machine Translation (NMT) advances from previous models that translated words one at a time, to the more human-like method of reading sentences for context and meaning. Ken is responsible for defining and implementing the growth strategy for SYSTRAN who have a global presence in the US, Europe and Asia. With 20 years’ experience in the language intelligence industry, he is considered a thought leader in language translation having held several Senior Executive roles within the industry. A native of Ireland he is also a serial entrepreneur as well as business mentor to several Irish Start-ups.
“귀걸이가 맘에들어요. 친구들에게 추천하겠습니다.” As an online seller, is this good news or bad?
In a survey commissioned by Education First, 49% of executives admitted that language barriers and communication difficulties had prevented significant international business deals from being done. In the same way that language barriers hamper big business, they hamper small business.
There are three ways to increase revenue: acquire more customers, increase the average spend per customer, and increase the number of transactions per customer.
Here’s the question for eCommerce platforms with millions of creative, ambitious, well-intended people looking to increase their income: can your seller in France close a deal in Japan? Can your seller in China provide customer support to his buyers in Spain? Can your users see reviews in their native language, no matter what the source language was?
Historically, these capabilities have been reserved for the captains of industry. eBay has been using their proprietary Machine Translation for years. But machine learning is making it available to everyone.
Technology firms have been working on the language problem for decades, but the last several years have seen significant advances that merit the attention of business leaders who are eyeing international markets. The technology is called Neural Machine Translation (NMT), a deep learning system that captures meaning in the context of translated sentences, not the single word. The net result is fluency, where previously, only a “gist” was possible. By combining NMT with existing big data tools that scrape, structure and analyze, new value propositions have suddenly become more attainable.
For example, let’s take a case from a peer-to-peer e-commerce site where a Korean customer inquires of an American Vendor, “미국에서 귀걸이를 한국으로 배송하면 얼마나 걸릴까요?”
When translated with NMT the result is “How long will it take to get earrings from America to Korea?” where as a statistical engine will return “In the United States, how long will it take to deliver the Korean earrings,” making commerce far more difficult to transact and probably several emails to clarify.
Similarly, reviews on web sites are extremely important with 88% of people saying that they now incorporate reviews as part of their buying process. In an “English only” world this tends not to be a challenge but, as HBR reported, 72.4% of people said they would be more likely to buy a product with information in their own language. NMT makes this possible today and with annual E-commerce revenue growth in double digits, e-tailers have a tremendous opportunity to accelerate revenue growth with minimal investment.
Customer self-service has also exploded over the last few years with many companies relying on “super users” to solve their clients’ problems. Again those outside the top 4 global languages find themselves in the dark most of the time. By implementing NMT solutions, companies are not only benefiting from happier customers in languages they struggled with, but also fend off local “copycat” technologies.
Of the three revenue growth options mentioned at the top of this piece, increasing spend per transaction and increasing number of transactions per customer are the easiest of the three. At the opposite end, losing a customer is a heavier cost then all. We live in a world where if two tech companies are created equal – the user will buy from the one with the better experience or the more relatable values. Offering cross-language chat, multilanguage reviews and knowledge based would put you in the upper echelon of competitors.
By the way, this ‘reads’ this. It was good news. This text means this: “I like the earrings. I’ll recommend them to friends.”
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Having spent 20 years developing Rule Based MT systems, I have spent the last six months building and improving a Dutch-English/English-Dutch Neural Machine Translation system. It took about a week to train my first neural model and when I saw that its output was far superior to that of the system I’d spent decades on I felt like bursting into tears! There are still niggles and hurdles but I’m convinced that this exciting new technology is the way forward.