How Verizon designs customer-valued chatbots

With AI fast becoming the new UI, Verizon is continually honing two customer-facing chatbots for everything from device troubleshooting to technician updates.

How Verizon designs customer-valued chatbots
Thinkstock

Become An Insider

Sign up now and get FREE access to hundreds of Insider articles, guides, reviews, interviews, blogs, and other premium content. Learn more.

Verizon didn’t set out to build a customer-facing chatbot. But around ten years ago, the company was hot off the launch of Fios, its fiber-optic network. And technicians were having problems. “[They] used to have a huge amount of difficulty when they needed to look at our systems to access [the] customer network or if they were in the field and they needed some help,” says Ashok Kumar, vice president of digital at Verizon. So the company developed IVAPP Buddy — an internal chatbot that service techs used on the job.

Driven by natural language processing (NLP), chatbots help people navigate information more quickly. To the general public, they have a reputation for posting fake news on Facebook. But for business, they integrate with communication platforms so employees can access HR and tech information Kumar calls “so complex, there is no way for any one human to learn it all.”

“AI is going to be the next UI,” he says, replacing websites. In fact, consultancy firm Gartner claims 25 percent of customer service departments will have implemented chatbots or another virtual customer assistant by 2020. Kumar says, “Everybody knows language and everybody knows how to speak” No matter how well you design a website’s user interface, nothing is as intuitive as conversation. Designing your interface to communicate with human language takes away its complexity.

So Verizon began designing bots not just for employees, but for customers. Today, two are public-facing: One in My Verizon App for cellular consumers and one in My Fios App for entertainment. The two, which received a CIO 100 Award in IT excellence, share a single machine learning engine, but were trained to respond in different ways.

To continue reading this article register now

NEW! Download the Fall 2018 digital issue of CIO