Machine learning
Machine learning is a branch of AI where systems learn patterns from data and improve at a task without being explicitly programmed for every case.
Machine learning is a branch of artificial intelligence in which a system learns to perform a task by finding patterns in data, rather than following rules written out by a person for every situation. You give the system examples, and it builds a model that can make predictions or decisions on new inputs it has not seen before.
There are a few common styles of learning:
- Supervised learning, where the system learns from labeled examples, such as messages tagged with the correct topic.
- Unsupervised learning, where it groups or organizes data without labels.
- Reinforcement learning, where it learns from feedback on the actions it takes.
In context
In customer support, machine learning powers many of the routing and classification steps that used to be manual. A model trained on past tickets can predict the topic of a new request, guess the customer's intent, or estimate how urgent a message is, which lets the help desk send each ticket to the right place.
It also improves over time. As more conversations are resolved, the patterns in that history can be fed back in to make predictions on triage, tagging, and suggested replies more accurate for the kinds of questions a given business actually receives.
How Auralis uses Machine learning
Auralis uses machine learning under the hood so its done-for-you support service can classify, route, and answer tickets accurately from day one, and the team tunes the models on each client's own history so deflection and routing keep getting better over time.