Ticket Resolution with Advanced A.I. and Automation
Issue tracking and resolution systems are an essential part of any business. Yet, the tech used to streamline the trouble ticket process isn’t as effective it could be, and often relies on legacy systems. Combine these challenges with the steep increase in ticket volume over the past two years, and efficient response becomes increasingly difficult.
Improving issue resolution processes is obviously the solution, but overhauling or replacing a well-established existing system is costly, operationally disruptive, and increases risk.
Instead, businesses must look to build on the assets they have already invested in by integrating new technologies such as A.I. automation. Let’s take a look at a prime example of this from one of our clients.
MetTel is a telco company operating from New York. The organization’s existing ticketing system was well established in the business, and functioned as a multi-choice decision tree path. Building a new solution from scratch was therefore not a viable option.
Ticket Next Best Action (TNBA) is a trouble ticket resolution system that we designed, built and implemented for MetTel. It combines the power of A.I. and process automation and can flex to accommodate any ticketing system. Here’s how it works:
Learning from data
We began by training TNBA using MetTel’s historical data. We fed this data into our A.I., which learned which actions are best to take, and when. It is now integrated into MetTel’s issue resolution center, making decisions as queries happen. Once it has reached a decision on a ticket, Intelygenz software comes into play. The A.I. enabled automation acts upon the decision, resolving the issue without human interaction.
Automating analysis and action
TNBA is a two-fold system. It uses Advanced Machine Learning to sort the best possible choice for a ticket, and then uses automated software to resolve the issue. TNBA completes all tickets at all times, which allows businesses to scale while keeping customer support costs down.
MetTel can now deal with every ticket-related challenge automatically and consistently. The A.I. learns from the best historical data, meaning it behaves like MetTel’s most experienced engineer. It also has the capacity to keep on learning. MetTel staff now spend more time on other non-automated unique tickets and more complex tasks.
With the ability to provide quality support services and enhance customer retention, MetTel has effectively increased margins and made space for profitable growth.
Read the full case study here.
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