August 16, 2021
AI for Customer Service: Key AI Terms
If you plan to use AI for customer service that means you need to have a decent understanding of what it is, how it can work, and what all is involved with it.
However, with all the information available online, it’s hard to find a solid definition of what AI for customer support is and what all goes into it being possible. There’s a lot of terminology to understand and you deserve to know all you can.
So, today we’re here to help simplify things.
Here is what AI and some of the most common terms associated with it mean.
Plus, we’ll fill you in on how AI for customer support can help the way you do customer support and increase customer satisfaction.
What is Artificial Intelligence (AI)?
Artificial intelligence, also called “machine intelligence,” is simply intelligence displayed by machines. It is a machine or computer system that can perform activities that require some level of intelligence to be completed, but not quite human intelligence.
It’s the theory and development of computer systems being able to perform tasks normally requiring more than basic machine intelligence, such as visual perception, speech recognition, decision-making, and even translation between languages.
As opposed to more traditional software, customer support AI is a system that does not need to be explicitly programmed to return specific outputs according to some given input.
AI, however, can be broken down further into its subsets — the technologies that make it up.
With machine learning, an AI application has the ability to learn and improve from experience without being explicitly programmed. This means a program can learn from other data without being “made” to do so.
Machine learning focuses on the development of computer programs that can access data and use it to learn.
Unlike less sophisticated customer support org tools available today, our AI tools are advanced enough to learn from data sets and continue to do so from new incoming information.
Getting even more granular, deep learning is a facet of machine learning. It teaches computers how to learn from data independently or without oversight. Thus the more data a computing system with deep learning has at its disposal, the better it can perform.
Deep learning is a type of machine learning and artificial intelligence that imitates the way humans gain certain types of knowledge. While traditional machine learning algorithms are linear, deep learning algorithms are stacked with hierarchies of increasing complexity.
This is a subset of machine learning that teaches computers to do what naturally comes to humans. Deep learning algorithms require a large amount of data to be able to learn. The more they take in the better they perform.
A great example of deep learning in action are chatbots and service bots. These bots are capable of providing intelligent and helpful responses to online customer service inquiries.
Supervised vs. Unsupervised Learning
Within artificial intelligence and machine learning, there are two basic approaches: supervised learning and unsupervised learning. The main difference is one uses labeled data to help predict outcomes, while the other does not.
Supervised learning is an approach that’s defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.
Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention, therefore they’re “unsupervised.”
The best AI for customer support can learn through both models and continue to do so as your organization grows.
Clusters are just sets of data that have commonalities and are thus organized based on those shared traits as a result.
Clustering data is a machine learning technique which aims to group data by common data points that have common properties or features.
A neural network is another machine learning-based technique used to solve real-world problems.
It is an algorithm loosely modeled after neural networks in the human brain and designed to recognize patterns. Neural networks help in the process of classification to create data clusters.
Algorithms (loosely) fashioned after the human brain, neural networks are created to identify patterns. As such, they’re used to help categorize data, to make clusters.
Natural Language Processing (NLP)
NLP is a subset of AI and involves programming computers to process massive volumes of language in data. It involves numerous tasks that break down natural language into smaller elements in order to understand the relationships between those elements and how they work together.
NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans.
Natural Language Understanding (NLU)
Natural language understanding (NLU), in simple terms, is reading comprehension for machines. It is a type of natural language processing (NLP) that uses the computing power of AI to comprehend text or speech as a human would.
While NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans, NLU is focused on a machine’s ability to understand human language.
An example of this is if you search something like “How hot is it?” on Google, the search will be able to understand you want to know the weather and temperature.
How You Can Benefit From AI
Knowing what these AI terms mean is great for understanding what goes into making AI for customer support possible.
When powered by the right tech, AI for customer support can help deflect and properly route tickets better than the simple chatbots you may have encountered in the past.
What if you could deploy a tool and have 90% accuracy in ticket resolution? All while increasing agent productivity and improving the customer experience.
Customer support AI can help!
Because we use NLU to understand customer sentiment and intent, Forethought’s AI will learn to serve up more accurate responses in shorter amounts of time. You’ll eliminate the time your agents spend answering repeated questions and the time they spend researching for answers.
Implementing customer support AI has meant a 10x increase in ROI for Route, a 26% increase in agent productivity within a local services marketplace, and a 56% decrease in first response time for D2L. And more companies can benefit too!
The Next Step: Piloting AI
Implementing AI isn’t as difficult as you might think.
You might have it in mind that this process takes weeks, if not months to get started with.
In reality, implementing customer support AI can be done in as little as 1-2 weeks!
Don’t believe us?
We’ve detailed the steps in our eBook Piloting AI for Customer Support where we go over what you need to implement AI, our 5-step process, and how you can prepare your teams for organizational change.
If you have other questions, schedule a time to chat with our team.