Verizon boosts customer service with NLP

The telecom giant has reduced costs and improved customer service response times by leveraging natural language processing and deep learning to automatically process requests.

Verizon boosts customer service with NLP
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Keeping customers happy is key to running a successful business, but with more than 100,000 inbound customer request comments per month, Verizon's Business Service Assurance group was struggling to keep up. Each request had to be read and individually acted on until Global Technology Solutions (GTS), Verizon's IT group, leveraged natural language processing (NLP) and deep learning to automate the process.

"In essence the Digital Worker takes tasks that can be executed automatically away from the engineers so that we can use the engineers' time in operations to actually deal with real, complex scenarios that need their intelligence," says Stefan Toth, executive director of systems engineering for Verizon Business Group, GTS. "We want to take away the time engineers spend answering emails and allow them to deal with complex networking problems."

In addition to the sheer volume of customer requests, primarily via email and Verizon’s web portal, delays and human errors in responding to those requests hurt the customer experience, Toth says. The team first attempted to automate responses to the most frequent requests using a static, rules-based approach that ultimately proved limiting.

"What we found is the traditional methods no longer fit the bill when we have free-flowing text and the context is very important. We couldn't just try to understand what the customer said and take action," Toth says.

In the end, the rules-based approach wasn't accurate enough. Engineers would have to get involved, and by the time issues were resolved, little if any time had been saved.

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