<img height="1" width="1" src="https://www.facebook.com/tr?id=2072464173003314&ev=PageView &noscript=1">

3 Ways Machine Learning Shortens Ticket Response Time

It doesn’t help anyone to let an open ticket sit in-queue. Unresolved concerns and long ticket reponse time often compromise customer faith and cause churn rates to rise. Add to that the lost momentum and increased room for error as a ticket is passed from one rep to another, and you lose quite a lot. Time is money, that’s for sure. But don’t be discouraged. There are 3 ways your support processes can be streamlined right now using the data crunching intelligence of machine learning technology.

1. Assign tickets automatically and intelligently.

You never know what type of ticket will arrive in-queue next, but you do have a lot of data on those you’ve already closed as well as data clues that define your support teams’ expertise individually and in groups. Even if you are using a ticket management system, chances are the actual routing is still aided by human intervention.

By sifting through or “mining” ticket history data, machine learning organizes and finds relationships between all of these seemingly disparate pieces of data—relationships that predictive analytics engines can then use to intelligently optimize your existing workflows. Future tickets can be routed automatically and with great precision to the agents with the ability to close them in the least amount of time.

Having an automated ticketing system as intelligently designed as this shaves time off today’s routing routines and delivers increasingly efficient support processes that can scale with your company’s future growth.

2. Eliminate the manual nature of repetitive responses.

Manually responding to the same issue can be very time consuming and tedious. Macros do exist that help streamline customer support queries, but these activities are manual in themselves, which adds time and leaves room for human error.

AnswerIQ Support Enables 100% Automated Ticket Classification for ThredUp

Machine learning recognizes patterns in the context of a ticket (like a subject line or keyword combination) and the macros that agents actually used in the past and then recommends the best response template, replacing manually entered and/or hand-selected information. These automated templates hasten response times dramatically, improving customer satisfaction and allowing companies to get even more mileage from their finite support resources.

In addition, putting the right response templates in the hands of customer service agents ensures that all of your customers are getting the same detailed and accurate instructions. This kind of consistency increases company credibility, improves customer trust and gives a big boost to branding. And in this highly competitive age, who doesn’t need that?

3. Take full advantage of knowledge base content.

If your team is like many support organizations, you’ve accumulated a repository of questions and answers that comprise the vast majority of the support issues your agents face and you’ve put all that information in a knowledge base for your customers. But the challenge is making customers aware of this knowledge so you can better leverage previously tapped support resources and achieve greater ROI.

Machine learning understands the intent of a support ticket and the language in each knowledge base article. Patterns also emerge in how your team has used those articles to respond to customers’ questions. As a result, machine learning can automate responses back to customers, suggesting the right knowledge base article to resolve their issues immediately. This not only improves self service by customers, but also positions your support department as highly responsive while still giving your team extra time to follow up if necessary.

Download our ebook and learn how you can leverage machine-learning technology with highly-scalable applications that increase your company’s ability to make support agents more productive.

Contact Us 


Topics: Machine Learning

Leave a Comment