Deep Learning vs. Machine Learning and How Brands Use Both in Customer Service

How can artificial intelligence make life better for human customers? Glad you asked! Actually, cold, unfeeling machines can make your customers feel all warm and fuzzy inside.

Using AI, customer service reps can improve and scale their customer service efforts. But you should also be aware that AI is a reflection of how the customer service game is changing before our eyes.

In this post, learn about the applications of AI in customer service, deep learning and machine learning CS processes, and examples of brands that use technology to improve the customer experience.

AI in Customer Service

Artificial intelligence is becoming a prominent part of customer service operations. Processes like machine learning, natural language processing, and speech recognition are proving to be assets in customer service — enabling seamless customer experiences and taking stress off customer support reps.

As time goes on, artificial intelligence will continue to become more prevalent in the context of digital customer service. These kinds of resources are becoming ubiquitous in any aspect of business that relies on modern technology, and customer service is no exception.

There are different subsets of artificial intelligence, and we’ll discuss them below.

What is machine learning?

Machine learning applies artificial intelligence with algorithms that sort through sets of data and learn from data to make predictions. Algorithms improve at tasks with experience but usually need initial human input to begin sorting through data.

What is deep learning?

Deep learning is a process that uses algorithms called neural networks that mimic the human brain to learn from data and make informed decisions and predictions. Neural networks rely on a significant amount of data to begin learning and aren’t reliant on human input to start the process of learning.

What is a neural network?

As mentioned above, deep learning is reliant on neural networks. In the human brain, these networks are interconnected neurons that process input, learn from input, and can make decisions based on hundreds of neural connections.

In computers, neural networks mimic the connections between neurons in a human brain and learn from hundreds of different data points to begin making connections and making decisions based on what they’ve learned.

Deep learning and machine learning are sometimes used interchangeably, but there are critical differences between each model.

Deep Learning vs. Machine Learning

Deep learning is a form of machine learning, but they are different processes. Most significantly, machine learning often begins with human input that helps algorithms learn the distinction between data points. As time goes on, the machine becomes more experienced at identifying differences without human input.

On the other hand, deep learning does not need human input and learns from data on its own, which is why it requires significantly more data to begin learning and processing and takes longer than machine learning. A great way to understand the difference between deep learning and machine learning is image processing.

Say you’re hoping to teach a machine the difference between four different animals so it can learn to make the distinction on its own. With machine learning, you’d need to teach the computer about the distinguishing features that differentiate each animal. The computer then uses that human input to begin learning the difference and becomes better at identifying each animal over time.

With deep learning, the computer doesn’t need you to tell it the distinguishing features, as it can sort through the different data points and learn the differences on its own. However, the machine would require significantly more data points to begin understanding the differences.

If you’re anything like me, understanding these concepts is rather challenging, especially when it comes to applying them to customer service teams, especially since  Having that understanding might mean the difference between your customer service efforts keeping pace with digital transformation or becoming outdated and insufficient.

Below, we’ll better understand how deep learning and machine learning processes are changing the landscape of customer service.

How AI Is Changing Customer Service

Speech Recognition

Your customer service operations today probably generate a lot of data. Audio calls, text transcriptions of those calls, text chats, live chats — you name it. A recent McKinsey study sees this as rich material for AI systems to process. Done right, this can produce some profitable machine-enabled customer service outcomes.

The study notes:

"Improved speech recognition in call center management and call routing by applying AI techniques allow a more seamless experience for customers — and more efficient processing,"

And it doesn't stop there. Using AI called deep learning, customer service operations are getting more sophisticated.

"For example, deep learning analysis of audio allows systems to assess customers' emotional tone; if a customer is responding negatively to an automated system, the call can be rerouted to human operators and managers."

Personalization

Emotion recognition is one area where AI can help. Another is personalization.

In this way, AI is pushing the boundaries of what customer service is. It's not just about customer satisfaction after the sale (though that's important). It's about creating incredible experiences and offers — time and time again.

These experiences and offers are then highly personalized using the power of AI. The more personalized the offer, the better chance a customer walks away delighted — and the better chance your brand scores a sale.

That means AI can turn your actual sales process into a valuable customer service tool by giving consumers even more opportunities to spend money on what they already like.

On one hand, AI can make your current customer service operations better, faster, and more effective at scale. On the other, it can personalize your marketing content so well that it delights customers. As a result, content becomes a vehicle for offering consumers the best offer for them at the best time.

Automatic Ticket Tagging

AI can also be an asset for your internal customer service infrastructure. For example, if your business uses a ticketing system, your customer service department is probably inundated with a massive volume of support inquiries every day. Those tickets must be read, analyzed, tagged, and ultimately routed to an appropriate representative or team.

Without AI, the process is tedious and time-consuming. Frankly, it can be a waste of your support team's effort and resources. AI tools — specifically text analysis ones — take the stress, personal effort, and monotony out of that process.

They can analyze text from and automatically tag support tickets — reducing what would be an hours-long process into a matter of seconds.

Chatbots

Another way customer service departments have been leveraging AI to improve customer experiences is through chatbots — bots companies place on web pages to address basic customer support inquiries at any time of day. The efficiency and accessibility these bots offer are redefining customer support.

Chatbots leverage AI and machine learning to understand the fundamentals behind a company's product or service. As a result, they’re able to answer common questions customers might have well beyond operating hours — while actual support reps are offline.

They make customer service simpler for customers and service reps alike. With chatbots, customers with basic questions can have their inquiries addressed easily whenever they need. And reps aren't burdened with constant, monotonous, simple questions — giving them more time to tackle more pressing, significant issues.

Digital Assistants

Yext's Duane Forrester, a voice search expert, says,

"A digital agent will be a game-changing moment in a customer's life, and each company knows they have a small opportunity to get it on the bullseye, and a large opportunity to miss the mark and drive consumers away from their platform. This means these products will be much more advanced than the digital assistants we now live with when introduced."

AI assistants and service tools present huge opportunities to get customer service right. But do them wrong, and you drive consumers into the arms of competing brands. This is all happening because consumer preferences are changing.

Let’s go over some examples of how machine learning, deep learning, and AI are used by businesses to supplement their customer service practices.

How Brands Use Machine Learning in Customer Service

1. Amazon

Amazon uses machine learning to give customers a personalized experience.

Its algorithm learns from customers browsing history and past orders to recommend products that they are likely to enjoy, contributing to a delightful experience where the customer feels as though the brand knows who they are, what they want, and exactly how to help them.

2. Walgreens

Walgreens uses a deep learning virtual assistant to help customers that place calls to the store. When you call the number, the voice assistant picks up the call, and providers caller a list of actions that customers often take when contacting the store.

It usually begins by asking, “How can I help you today?” and, based on customer responses, the virtual assistant can reply with adequate solutions to customer queries. For example, if you speak into the phone and say “Pharmacy,” it knows to respond with options related to Pharmacy needs, like connecting you to a pharmacist or getting the pharmacy hours of operation.

Optimum

Optimum is an internet, tv, and mobile provider that uses a chatbot for customer service. Customers can text the chatbot via mobile phone and explain their issue, as shown in the image below. The chatbot can process the words you’ve sent and extracts key markers that help it understand how to best help you. For example, in the image below, the keyword likely was “reset my password.”

The Changing Landscape of Customer Service in the Age of AI

We're moving to a contextual world, where consumers search online for personally relevant results in real-time. Voice is ascendant, as consumers make more on-the-fly searches, decisions, and purchases. Online reviews generate tons of data that can tell us much about customers if only we had the time and ability to analyze these reviews.

In a world of almost limitless data, AI is helping us leverage that data to improve our existing customer service operations. But AI is also being adopted to help brands cope with a fundamentally changed customer service landscape, where everyone expects one-to-one attention — at scale.

One thing is evident in this brave new world; effective customer service is no longer a job humans can do alone.