Customer service is no longer just the domain of humans. Machines with simulated human intelligence, after all, are increasingly used to help human support agents keep customers happy.
Consider the statistics.
According to Zoominfo, 80% of sales and marketing leaders say they already use artificial intelligence, particularly a chatbot software, in their customer experience or plan to do so by 2020. Juniper, for its part, predicts chatbots will be responsible for cost savings of over $8 billion annually by 2022.
The bottom line is, there’s no denying AI will continue to play a major role in customer service, customer experience in general, in the years to come. It pays then to have a good grasp of what it really is. In this article, let’s look at AI’s application, machine learning, which is particularly relevant to the customer experience.
According to Expert System, machine learning is an “application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.” Basically, the aim is to enable machines to learn automatically so that they can respond depending on the data given to them.
Let’s take chatbots as an example. Chatbots give different responses depending on the data given to them. So for, instance, if you ask “Sunshine,” the chatbot of a mutual funds company, how to open an account, it can interpret your question properly and answer this way:
Even when you don’t ask a question (in short, input data), it gives you this reply:
In short, the chatbot interpreted the absence of data correctly.
Basically, machine learning enables chatbots not just to learn when they should use specific responses. It also enables them to gather the necessary information from users and enables them to determine when they should hand over a conversation to a human agent.
You might ask, but how exactly can they do this in the first place? Enter the algorithms.
A machine learning algorithm is a process or sets of procedures that help a model respond to the data given to it. The algorithm basically specifies how the data should be transformed from input to output. It also specifies how the model should learn the mapping throughout the entire process.
There are different types of machine learning algorithms:
To understand this better, let’s look at how machine learning can specifically be applied to customer service.
Machine learning applications are actually more ubiquitous than what many people think.
The lack of awareness is not surprising since the study of machine learning, as it is, has been relegated to the academe. Even in school, not all students study the subject. Ask around and you’ll see that only those who specialize in computer science or other computer-related and even engineering courses will be required to take it up.
But let’s try to change that with this article. Here are specific machine learning applications that are used:
Chatbots, like the ones we’ve seen in the examples in the first part of the article, are one of the most used machine learning applications in the customer service industry. In fact, because chatbots are everywhere, Gartner says a whopping 67% of people already expect to see or use them when talking to a business.
Thanks to machine learning, chatbots can accurately identify the right tag for each conversation by using natural language processing. The result is the chatbot “reading” and understanding what you say. Once it understands what you’re saying, it sends you the appropriate response (see the “Sunshine” example above) or routes you to the right person who can handle your issue.
The more conversations the chatbot had, the more correct its response will be. The feedback it receives from customers who say whether the tagging is correct or incorrect also allows the chatbot to improve its performance.
Virtual assistants are more often than not confused with chatbots. They are, however, not the same. Chatbots simulate an interaction with an agent, while virtual assistants focus on specific areas in the customer journey to give assistance to the customer.
If you’re using Microsoft, for instance, you can verbally ask Cortana when summer begins, and she’ll give you the information you need.
The thing is, Cortana won’t just show you this when you ask:
Using Natural Language Processing that mimics human speech patterns, she will tell you the answer in a tone that simulates the human tone, creating more intimate interactions. But how do virtual assistants like Cortana, Apple’s Siri, Google Assistant and Amazon’s Alexa work exactly?
When you activate them, your request is sent to the servers owned by your device company (that’s why it’s important you have a good Internet signal). While this is being done, your phone or smart speaker attempts to figure out if it can handle the command without the information from the server. Once the request reaches the servers, an algorithm analyzes the words and tone of your request, and matches it with a command it thinks you asked.
So in our example above, Cortana’s algorithm clearly matched your request with the right command. But what happens if you asked something the algorithm is not sure of? This is when the virtual assistant can say “Did you mean _____?” or “I’m sorry, I can’t do that.”
If you’ve been embarking on an email marketing campaign or an email outreach your whole life, every time you send your emails, I’m sure you always make sure they have been verified by your email verification tool first. You do this all the time because you know that when emails are sent to invalid email addresses, they bounce. The higher your bounce rate, the lower your sender score will drop. The result is you can be trapped by spam filters.
But have you ever wondered how exactly that email verification tool worked? Well, that’s machine learning at play there. Simply put, sophisticated machine learning algorithms give that email verification tool the ability to track more elusive disposable address providers and analyze whether or not an email actually exists.
Those who use Uber have probably contacted the company at one time for support. Although it is still the human customer agents who directly provide a resolution to their problem, guess what allows the humans to do this at that speed and accuracy?
Through COTA, Uber’s Customer Obsession Ticket Assistant, human customer service agents are empowered to provide the most accurate solution to the thousands of tickets surfacing daily on the platform across over 400 cities worldwide.
When the Uber user accesses the app and answers questions on the type of issue he or she is facing, the user basically helps COTA “understand” the ticket through natural language processing.
COTA then routes the ticket to the proper team. Through machine learning algorithms, it determines the top three ranked solutions to the human customer agent. The human customer agent then picks which of the recommended solutions he or she thinks is the most feasible. This is the solution suggested to the customer.
According to Uber, better ticket routing thanks to COTA increased efficiency by a whopping 10%. “By improving agent performance and speeding up ticket resolution times, COTA helps our Customer Obsession team better serve our users, leading to increased customer satisfaction,” Uber said.
It added COTA’s ability to expedite ticket resolution saved Uber “tens of millions of dollars every year.”
Some companies use machine learning to analyze trends and behavioral patterns. This is important because if you know how your customers behave, for example, you can make the necessary adjustments to your services and products in order to better serve them If you analyze trends, you can also make predictions on which to base your business development decisions.
Codeacademy, an online platform that offers coding, for instance, uses Solvvy to analyze customer search trends. By doing this, it was able to find the topics that customers were searching for but did not find answers to. The result? Codeacademy was able to close the necessary gaps and reduced the workload of its customer service team. In the end, customers got better service.
Air Canada, for its part, used machine learning to look at the thousands of conversations with customers during online bookings. By looking at customers’ complaints, the company determined the common problems customers had in ticket booking and addressed them. Because the company improved customer experience, it was able to save on the labor costs for customer support.
Based on these examples, we can infer the specific ways machine learning optimizes customer experience. Here are only some of them:
There’s no denying machine learning, as it is, already plays a major role in customer experience. And the prospects are even brighter, according to some experts.
According to Digital Information World, machine learning “is going to explode,” as business demand continues to rise. An article by Oleksii Kharkovyna published on Towards Data Science says machine learning tools will continue to “evolve” and provide an optimized customer experience. And that isn’t at all unlikely.
Chatbots and virtual assistants, for instance, while useful, have not yet reached their full potential. As it is, some chatbot and VA responses are off. Sometimes, they have no responses to specific questions, or do not comprehend specific questions.
As machine learning continues to evolve, chatbots and virtual assistants can have a full range of responses in their database, and a greater grasp of data.
A few years from now, we can have Cortana, for instance, answering even the most complicated questions. Answers such as “Did you mean _____?” or “I’m sorry, I can’t do that” may just be a thing of the past. Maybe we can have Cortana type an entire Word document you read out loud? Or one that even has a human body.
But it’s not just machine learning that will evolve. The demand for machine learning by businesses that wish to optimize their customer experience is expected to increase.
Gartner, for instance, predicts that by 2022, 72 percent of customer interactions will involve an emerging technology, such as a machine learning application. This is an increase from the 11 percent recorded in 2017. By 2021, it said 15 percent of customer service interactions are expected to be handled completely by these tools. This is an increase of 400 percent from 2017.
The question is this then. Will machine learning applications replace human customer agents? Like Gartner, P.V. Kannan and Josh Bernoff believe this is not the case. In an article written for the MIT Sloan Management Review, the two said the future of customer service is actually machine-human collaboration.
According to Kannan and Bernoff, it’s not so much about getting rid of workers, but about making them smarter. “When machines handle routine inquiries, customers are happier. And when service staff can concentrate on more complex questions — or on answering questions with a bot making suggestions — they can deliver far better service,” they said.
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