Exactly how do companies like Skyscanner, Daniel Wellington, Pinterest and Under Armour scale their customer service operations across multiple languages?

Do they hire hundreds of native agents? Use Google Translate?

The answer is no, they don’t. However, these high-growth companies have some other tricks up their sleeves that have enabled them to handle multilingual customer support with small teams, while also increasing the level of customer satisfaction across different languages.

But how?

Customer Support often has a bad name. And that’s strange. Because everyone who works in customer service also knows exactly what it’s like to be a customer. They also know what it’s like to experience both excellent and lousy customer service. So how come so many CS operations have traditionally been closer to lousy than excellent?

Well, management teams have in the past done an almost perfect job of creating an environment in which customer support fails to deliver:

Traditionally, the Customer Support function has been the unsung hero of business, for a very simple reason: the customer neither sees nor envisages a problem at the point they hand over their money. 

When you get a mortgage, buy a dress or book a flight, you don’t exhaustively assess a business for its quality of service. At this stage, we are still much more likely to think about price, product quality and features as our main points of comparison.

But anyone who has worked in a call centre or on a helpdesk knows how important the support function is; often the last opportunity to rescue a negative situation and lock down a customer for life.

However, the role of Customer Support as a hidden asset has been changing for much of the last decade. 

There is no one moment when SaaS Software as a Service was conceived, because SaaS as a concept has a host of components; all of which have had to come together in the right context in order to produce value for any sector or vertical market. Different sectors have moved towards SaaS models at different speeds.

In technical terms, SaaS relies on cloud delivery at scale, a minimum degree of widely available connectivity, and enterprise-grade security. If any of these are weak, SaaS drops off the agenda.

Artificial intelligence (AI) in gaming isn’t a recent innovation. As early as 1949, mathematician and cryptographer Claude Shannon pondered a one-player chess game, in which humans would compete against a computer. 

Indeed, gaming has been a key engine of AI, and a proving ground for the simulations, constructed environments and tests of realism that are the foundation of virtual experiences. 

It’s easy to lose ground in e-commerce. In a crowded market where customers can buy from a competitor as simply as following a link, retailers have to work hard. Every search term, product description and user pathway matters.

Not surprisingly, smart players have come to depend on the abundance of data consumers make available at every step of the business process. They deploy predictive analytics, machine learning and other Artificial Intelligence techniques to redefining the rules of the game, helping some stay ahead of the competition, and improving the customer experience overall. 

Here are just a few examples of how.  

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