The future of customer service is here

We’re living in the future now. Technology has propelled us to have bigger and better expectations for what our lives can look like. And enterprises are starting to jump ahead of those expectations to provide customer experiences that resonate with us so much, that they form the foundation of brand relationships. In the next part of our 2017 Forrester customer service trend series, Josefine Fouarge will detail how these customer experiences thrive on the power of artificial intelligence and Natural Language Understanding.
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As consumers become increasingly accustomed to having automated conversations, businesses must adopt artificial intelligence and natural language understanding to enhance customer service.

Over the past few weeks, you’ve heard our take on some of the key trends that were presented in Forrester’s “2017 Customer Service Trends: Operations Become Smarter and More Strategic” report. From self-service via IVR to smarter field service, we’ve witnessed in our blog series that customer service is working smarter. To continue the series, let’s take a look at Kate Leggett’s prediction that companies will sustain automated customer conversations. At Nuance, we completely agree about the importance – and necessity – of embracing automated conversations. But, in order for those engagements to work smarter, they require embedded artificial intelligence (AI) and Natural Language Understanding (NLU).

2017 is the year in which the future will be pulled into the now, and as soon as more businesses get on board to what is happening, these futuristic possibilities will become reality. Take, for example, the financial services industry. A recent Accenture report found that artificial intelligence will become the primary way banks interact with their customers within the next three years.

The following scenario illustrates the magic that’s happening in the background of our everyday interactions with intelligent agents of all varieties.

Picture, if you will, in the not-so-distant future, Rick, who is waking up from a good night’s sleep. Rick’s intelligent virtual assistant tells him from his home speaker all about his upcoming day; he then asks it to move his 12 p.m. appointment with the bank to 12:30 p.m. Shortly afterwards, he receives a notification via SMS, telling him that the appointment has been rescheduled.

On his way to work, Rick tells his phone’s voice assistant, “Call my bank.” When he reaches the bank’s conversational IVR, it asks him if he is calling about the appointment later that day, which he confirms. Because the IVR understands natural language, Rick can ask it, “What papers do I need to bring with me?” However, if the IVR doesn’t have the answer, it can immediately move him to a call agent who tells him what documentation he would need at the appointment.

Rick’s appointment goes well, and shortly after the visit, he receives a quick summary via SMS with a link to the bank’s mobile app. While looking at the update in the app, he asks the virtual assistant to check for outgoing wire transfers. The virtual assistant suggests adding some buffer to the checking account since it looks like the current balance will not cover all the transfers. While Rick is talking, the virtual assistant verifies his voice and schedules a money transfer from savings to checking for the next day.

On his way home, Rick receives a Facebook message from a friend sharing news about increased fraudulent charges on credit cards, similar to what Rick has. He advised his phone’s voice assistant to start a new text message using Facebook Messenger to his bank. The phone translates his voice to text and sends the message. After a few Facebook Messenger exchanges with the bank, he gets connected to a chat agent who informs him about the current news and assures him of the procedures the bank has already put into place to protect Rick.

Rick’s day was reliant on the AI interfaces that allowed him to interact with the bank when and where he needed to. Sounds like a great way for customers to connect with enterprises, right? The problem is that enterprises know what their customers want, but have difficulties deploying such a solution. The key lies in communicating with the customer using natural language. Natural Language Understanding allows customers to use their own words to describe why they are engaging, resulting in a faster, more direct path to resolution. NLU enhances the customer’s self-service experience, while delivering efficiency and automation improvements.

These fully AI-integrated, language-intelligent engagements are becoming universal at this very moment, almost second nature. If financial institutions can envision a scenario like the one you’ve just read, and anticipate that AI will change the face of banking in the near future, then that’s a good sign that the customer service trend of automated conversations is more than just a trend. It’s an everyday function – like clicking a mouse.

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Not just what customers say, but what they mean

Natural Language Understanding serves as a foundation for self-service solutions that deliver unprecedented flexibility, efficiency, and customer satisfaction.

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Josefine Fouarge

About Josefine Fouarge

Josefine Fouarge is Sr. Product Marketing Manager in the Nuance Enterprise Division, focusing on automated and human assisted engagements in digital channels. Josefine brings more than 10 years of experience in sales and marketing for technology related businesses in Germany and the U.S. Her past expertise ranges from selling and configuring Apple computers to defining the market and messaging for a security software for on-premise datacenters and services offered through the cloud.