Machine learning in customer service takes that idea a bit further: it applies discovered insights in ways that can optimize the customer experience.
It can be something that makes support agents more knowledgeable (like through predicted analytics) or efficient (like when an AI (Artificial Intelligence) powered tool can handle remedial customer issues all on its own). Here is how machine learning is being used in customer service.
Chatbots are what come to mind for many when discussing AI technology in customer service. Their ability to simulate an interaction with a customer service representative and resolve simple inquiries is an effective self-service solution. Machine learning enables chatbots to learn when they should use specific responses, when they should gather necessary information from users and when they should hand off a conversation to a human agent.
Virtual assistants are different from Chatbots in how they don’t try to simulate an interaction with an agent. Instead, they focus on specific areas in the customer journey where they can provide assistance to the customer. When enabled with machine learning capabilities, they can learn what kind of information they can pass along to agents (or saved to be used in analytics programs) and enhance the kind of assistance they provide. Virtual assistants offer support with everything from project management to paralegal services.
Many customers claim that searches within knowledge bases don’t generate the help articles they’re looking for. Machine learning can be used to analyze the data that comes in from support tickets and turn them into actionable insights for agents to apply to help articles. Those insights point out how users describe their issues and if those descriptions are similar to the content of the knowledge base.