For all of the hype around
Today we’ll explain how AI in all its varieties – machine learning, neural networks, and natural language processing (NLP) – can help your customer support team resolve complaints and delight customers.
Machine learning is a massive time saver
Machine learning is ideal for:
- Recommending products
- Automating routine tasks for agents
- Making use of large quantities of structured customer data
- Looking up information for both customers and agents
Machine learning is among the most common varieties of AI. It’s what powers Amazon’s product recommendations or Google Search. It’s an algorithm that works by studying mass amounts of data, identifying patterns, and suggesting shortcuts.
In customer service platforms, machine learning is great for automating simple tasks like filtering emails, routing calls, and responding to basic support needs. It saves human agents hours of administrative work and allows them to prioritize inquiries so the most critical issues get handled first.
You see machine learning used a lot in customer service chatbots, to help make them more useful. Early bots had a tendency to misunderstand information or had a limited array of commands. Customers would often end up emailing support, the very process the bots were designed to eliminate. But modern machine learning chatbots get “trained” on large customer data sets and learn to match the right answers with the right queries, and get better the more they’re used.
By pairing machine learning-powered bots with human agents, the bots become even more effective. The bots automate away all the routine tasks while humans have more time to spend with
Neural networks and NLP provide a much-needed dose of empathy
Neural networks are ideal for:
Understanding customer choices
Reducing order error rates
Simplifying support interactions
Customer support tools grow even more useful as companies apply additional forms of AI. Neural networks and natural language processing (NLP), for example, give chatbots an empathetic edge that helps them interact with humans.
Neural networks work similarly to machine learning, but store information and arrive at decisions in similar ways to human brains, hence the term “
Similarly, NLP allows computer systems to understand language, how words relate to one another, and in some cases, context. You likely run into NLP every day: Any predictive search system that tries to fill in what you’re typing using a basic form of it. And voice interfaces such as Siri and Alexa use it to understand accents.
With NLP, conversational bots have made strides to the point where it’s possible for people to not even realize that they’re talking with a machine agent, making them a more reliable form of support. By 2025, the CX news site Servion predicts that 95 percent of all customer interactions will be powered by these sorts of AI.
NLP Is ideal for:
Simplifying support interfaces
Making use of unstructured customer data
"The truth is humans don’t scale and bots don’t build relationships, but when working together optimally the benefits are exponential and the ROI significant. Our clients see, on average, open rates eight times that of email and a 36 percent uptick in revenue."
The ultimate AI-booster is human networks
As fast as AI technology is growing, it won’t replace human agents and eradicate jobs so much as it will enhance and simplify human interactions. When support platforms and chatbots surface correct information to human agents who have the final say on what is communicated to customers, you get the best of both worlds: Impossible speed and accuracy with sound human
Read more about the impact of bots and automation in our State of Digital Customer Experience Report 2019: