By Maria Ward, Account Manager & Knowledgebase Engineer
Good day – Guten tag – Buenos días – Bonne journée – Goededag – Buona giornata – There are more than 7,000 known languages spoken in the world today. So, it’s no surprise that language is a common barrier in both personal and business interactions.
Back in 2014, the International Customer Management Institute (ICMI) published a report titled The Growing Need for Multilanguage Customer Support. Their survey of customer service leaders found that 72% said support in a customer’s native language increased their satisfaction with customer support and 58% said it increased loyalty to the brand. Over half acknowledged that offering support in a customer’s native language was a competitive differentiator.
This research is old now, but the desire of customers to have native language support is still very much there. Luckily for businesses, new technologies are making it easier for them to offer multilingual customer service on digital channels than it was in 2014.
One of these technologies is machine translation which has seen huge improvement in recent years. Developments over the past two years have greatly increased the accuracy and reliability of many translation engine applications. This has opened up new possibilities for delivering multilingual customer self-service.
For example, this year I’ve been working on several conversational AI projects with businesses taking advantage of machine translation to provide customer service in multiple languages. One is with an organisation that has used V-Person technology since 2016 on their UK website. They are an international company and became interested in exploring ways they could leverage their successful English-speaking virtual agent in other countries.
Using an automatic translation engine is a great solution for them because it is cheaper, simpler, and easier than creating a whole new virtual agent in a second language. It lets them build on the years of investment they had already made in their English-speaking virtual agent. Now they are using that same knowledgebase to provide self-service on their German website by adding translated versions of their virtual agent answers and integrating with a translation engine.
Here’s how it works: The customer enters their question in German in the virtual agent. A translation engine is utilised to translate that input into English. The translated input is then matched in the knowledgebase to the correct piece of content. The virtual agent selects the German version of the response from the knowledgebase and presents that answer to the customer.
The company started the project by identifying the top FAQs for their German website. They then provided German translations for those pieces of content. The team also worked on making any modifications to the natural language processing (NLP) to accommodate for differences in how a German user might ask those questions or ‘weird’ automatic translations that may be returned by the engine. After a successful launch of the German-speaking virtual agent, work got underway to slowly expand the content.
Another project I’ve been working on recently is for a brand-new virtual agent. One of the reasons Creative Virtual was selected as their conversational AI provider is our ability to integrate with translation engines and manage multiple languages within one knowledgebase. This company is starting their project with seven languages.
The process for this multi-lingual virtual agent has been a little different than my first example because there was no existing knowledgebase at the start. My recommendation for any organisation looking to build a new virtual agent in multiple languages is to start by finalising all content in the main language first. This will save you time with the translation work because changes to an answer typically means having to make updates to that answer across all languages.
Using automatic translation to expand a virtual agent to multiple languages is cost-effective and saves time, but it’s not a perfect solution. You’re likely to encounter content clashes and inputs that aren’t matched with your existing content. This is why you need a virtual agent management platform that has the right functionality to specifically support integration with a translation engine. The projects I’ve been working on are successful because of our V-Portal™ platform.
The right conversational AI platform will support workarounds for the content clashes and customisations for your different languages. It should also use artificial intelligence and machine learning to provide relevant ‘did you mean’ suggestions to users when their input doesn’t match with a specific piece of content. You also have the ability to set the virtual agent to ‘auto-select’ answers. This means that if the NLP fails to match the input directly with the correct answer, it pushes one of the ‘did you mean’ answers automatically as long as that answer meets a specified confidence level.
Maintenance of your multi-lingual virtual agent is also easier when you have a highly functional management platform integrated with a translation engine. When you need to make updates to an answer, you can do that quickly across all languages since all answers are listed under the same intent in the knowledgebase. Also, any changes you make to the NLP in your main language benefits all languages. And as machine translation engines improve, you automatically benefit from the most recent developments without having to do any work on your virtual agent.
The quality of your customer service affects customer loyalty, repeat business, and your brand reputation. Offering native language support can really improve your support experience. Technologies like automatic machine translation are making it easier than ever to give customers multi-lingual customer service options. Contact the experts at Creative Virtual to learn more about how we’re helping companies deliver these solutions.