The saying goes, "The squeaky wheel gets the grease". Your car might have four bad wheels, but it's the noisiest that gets your attention. A customer support team has to manage their time and resources. Every agent starts their day with a pile of new support tickets to open, triage, assign and respond to. If they could detect a level of rage in customer tickets, should they respond to the angriest first?
Amershi, Saleema (University of Washington) | Lee, Bongshin (Microsoft Research) | Kapoor, Ashish (Microsoft Research) | Mahajan, Ratul (Microsoft Research) | Christian, Blaine (Microsoft Corporation)
Network alarm triage refers to grouping and prioritizing a stream of low-level device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing rule-based tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to constantly learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known a priori and evolve constantly. Our evaluations with real operators and data from a large network show that CueT significantly improves the speed and accuracy of alarm triage.
We are excited to announce our MonkeyLearn integration with Zapier! Wouldn't be amazing if you had a simple idea on how to automate a manual workflow with AI and just try it out in a couple of minutes? Labeling your emails, tagging customer support tickets or organizing billing invoices are just a few examples of manual human processing that are time consuming and boring. Those tasks should be automated, but they usually involve some degree of human intervention to read and understand the content. In order to automate that, you must add a layer of Machine Learning to make machines understand that content.
At Alphanumeric, we spend a lot of time helping our clients add artificial intelligence to their service desks. What are the advantages of artificial intelligence in a service desk environment? We like that question because it's a necessary one. It requires a commitment of time and resources to implement AI. With that in mind, let's review some of the advantages of artificial intelligence in a service desk environment.
We're thrilled to announce our MonkeyLearn integration with Zendesk! Most of everyday business interactions with our customers are done through text from emails, chats, and social media channels, among other sources. In particular, customer support involves processing huge quantities of text, in the form of support tickets that are opened by customers whenever they need help. Usually one or multiple agents in the support team have to read through the ticket to understand the request from the customer, which takes a considerable amount of time. What if machine learning understood human language on these tickets?