Machine learning: a new potential in customer service

If you’re on top of the latest technology, there’s a good chance that you’re taking advantage of machine learning in your daily life. It’s a type of artificial intelligence that allows computers to learn new data and apply it to a service, all without being explicitly programmed to do so. There are a few obvious examples, like chatting with Amazon’s Alexa in your living room or Siri on your iPhone. Machine learning can also be more subtle, like when Google suggests a new route after sneakily cross-referencing your common destinations with a daily traffic report.

Similar conveniences have made their way into customer service via machine learning. The difference is that while the aforementioned machine learning examples learn and adapt to your daily routines, AI tools for customer service focus more on the customer’s journey and the workflows of support staffs. We’ve highlighted some of the potential innovations coming to customer service by way of machine learning:

Leaving routine tasks to the machines

Effective machine learning tools could have the same impact as a personal assistant, one that’s infallible when it comes to routine tasks. For example, agents will commonly receive menial customer inquiries such as requests for subscription downgrades or cancellations. Agents bogged by low-level routine tasks could benefit from an AI-powered assistant that can quickly point customers towards the answer they’re looking for.

Scaling your self-service efforts

A knowledge base with a strong information architecture (one that’s structured with multiple articles rather than a giant FAQ) can work wonders for self-service… as long as your customers know that it’s there or how to use it. Machine learning technology has the potential to automate the promotion of a help guide and drive good self-service habits among your customer base. For example, you can apply ML technology to recognize a customer query and suggest an appropriate article for a quick resolution, leaving the agent out of it. This provides more bandwidth for agents to optimize help articles and scale their self-service efforts more easily, which in turn will make the automated suggestions even more effective.

Enhancing current support channels

Most people think of chatbots when it comes to machine learning in customer service (as they should, since they’re all the rage at the moment), but don’t forget about your other support channels. A well-rounded approach to customer service means customers can communicate their issues in different ways. Machine learning is already being applied to self-service initiatives beyond chat, but we’ll soon see it a lot more in social media, texting, email, and phone support services.