How AI is redefining the future of customer service

No matter what industry you’re in, customer service is key. Luckily, there are lots of great new methods to improve the overall customer experience—including the use of AI.

One problem with AI (that’s artificial intelligence, if you were wondering), though, is that people get really excited and try to implement it, without understanding what AI is or how to use it.

Rather than try something completely new, AI often works best when it’s brought into existing real world applications by supplementing and making those experiences better.

What do I mean by that? Quite simply, the best application of AI is to improve your existing interactions with customers. But how can you incorporate tools with AI functionality into your contact centre or call centre?

Having worked in customer experience for most of my career, let me walk you through AI customer service, from its impact to the concrete ways in which we can use it—including how we use it here at Dialpad.

Why use AI in customer service? What are the benefits?

Customer service used to be limited to a phone line (or an in-person visit at your store). Now, customers can contact service teams on their own terms, anytime, anywhere, and on whatever channel they prefer.

That’s a large reason why the use of AI has become so popular. Everything from Interactive Voice Response (IVR) to AI chatbots (even though in my opinion, chatbots are better used as a last line of defence rather than a staple of your strategy, unless you've got excellent chatbot software), to conversational AI and sentiment analysis tools—these are all examples of how you can use AI to improve customer engagement and customer satisfaction.

Uninterrupted (or fewer interruptions in) service

Artificial intelligence tools are a fantastic way to ensure that your service operations go more smoothly—day in, day out.

One of the main use cases for AI is to automate repetitive tasks. That way, contact centre teams can save time, help customers solve problems more efficiently, and maintain momentum. For customer service that means faster response times and increased customer satisfaction.

Lower operating costs

Implementing AI-enabled tools can help businesses reduce customer service costs substantially.

Why? Because AI allows your agents to focus on more complex questions and automates those easy-to-solve repeatable issues that come up in high volumes every day.

That means you’ll need fewer agents on the floor over time to deliver the same (if not better) service, with better response times.

Speedier interactions

There are some AI tools that empower contact centre agents to be more effective in customer service interactions. (Which ultimately leads to improvements in areas like wait times and on-hold times).

For example, Dialpad Ai Contact Centre not only transcribes conversations in real time (more accurately than almost all leading competitors) thanks to its AI technology, it can also automatically pop up RTA, or Real-time Assist, cards on agents’ screens when it detects that certain keywords are spoken on the call.

For example, a contact centre supervisor can create an RTA card with notes on how to port a phone number, and set it to trigger for agents whenever “port” or “porting” is spoken on a call:

This is a great example of how artificial intelligence can help with coaching and training at scale—without requiring supervisors to personally help answer customer questions on every call with their agents.

Higher agent and customer satisfaction

Both agents and customers can benefit from AI technologies. For agents, AI can help them streamline their workflows and eliminate those repetitive everyday tasks.

AI might also help employees find the information they need much more quickly (especially when used together with a CRM like Salesforce), which leads to quicker resolutions for customers.

An example of how contact centres can use AI to help both agents and customers is live sentiment analysis. For example, Dialpad’s contact centre platform can show a supervisor all the calls their agents are on—and the sentiment of each one:

This allows supervisors to quickly scan ongoing calls to see if any agents need help and even read the transcripts (which get updated in real time) to get more context before deciding whether or not they need to jump on the call. Agents get timely help when they need it, and customers get faster resolutions to tricky problems because managers can be proactive about joining these conversations. A win for everyone..

4 creative AI customer service examples

The exciting thing about AI is that it has so many different implementation possibilities. Here are some common—and not-so-common—use cases for AI that I’ve seen.

1. Natural Language Processing (NLP)

NLP is a way of teaching computers to understand human language. The significance of NLP is huge for customer service. For instance, it’s the brains behind Dialpad Ai, our artificial intelligence technology. As I mentioned earlier, Dialpad Ai can transcribe calls—in real time—with extreme accuracy, as well as track certain keywords and how frequently they come up in customer conversations.

If you run a customer service team and want to see how often customers are asking for refunds (and why!), you can create a “Custom Moment” in Dialpad that’ll track every time “refunds” or “money back” is spoken on a call:

From there, you can see in Dialpad’s dashboard how frequently this shows up in calls over a period of time, then dig into the transcripts and recordings to get more context. With AI helping you track these frequently recurring topics, you can use this data to create FAQ or knowledge base articles and improve training for your agents.

2. Chatbots

Chatbots sound good on paper. What’s not to like about offering customers 24/7 instant answers to their basic requests, while asking for customer info before passing a ticket onto a human customer agent?

Well, in practice, it depends on the type of chatbot or conversational AI solution you're using. Historically, chatbots aren’t the best example of AI customer service. Imagine this: you’re interacting (I’d say talking, but you’re not really talking to it because it’s usually so rigid and scripted) with the chatbot—and then ultimately, it says “You have to call.”

Okay, we can accept that bots won’t be able to answer everything, but what you’re wondering now is “Well can you just transfer me?” and of course it’s not possible, so great, now you've wasted this much time with the chatbot already, and you have to call them and start over again.

That’s why AI customer service solutions should be flexible and use some type of deep learning to improve over time and predict customer intent more accurately. This way, they can be a truly helpful supplement for human interactions—and more importantly, resolve a wider range of problems for customers.

For example, Dialpad's omnichannel contact centre platform comes with robust conversational AI functionality that lets customers easily escalate a chatbot conversation to a live agent if needed. And best of all, we can build these flows using the easy drag-and-drop builder—no coding needed:

3. Object detection

Object detection is a type of computer vision technique that locates individual objects within an image or video.

Neat, right? But what does it have to do with the digital customer experience?

Object detection software is a great way to improve your customers’ experiences as people are spending more and more time on mobile devices (and soon, likely VR and other technology).

Even now, its applications are quite widespread. For example, object detection can be used by ecommerce brands to aid image search functionality. With AI-powered software, an online shopper can easily take a snap of a product, and get presented with similar products available to buy.

4. Machine learning models

We hear about machine learning algorithms here, there, and everywhere these days. But how can we use it in customer service?

Firstly, machine learning is an application or subset of AI, and the idea behind machine learning is that computers should be able to “learn” and get smarter by taking in data. (NLP is specifically about interpreting language, so it’s not quite the same as machine learning. You might have some technology that uses both machine learning and NLP, for instance.)

That means that, as a user interacts with your brand, AI can learn more about them—what they like, what they don’t like, and how they engage. This data then means the AI can personalise its approach, with targeted offers or custom recommendations. Neat, right?

A few building blocks to help you successfully apply AI to your customer experience

Data unification

First thing’s first, you’ll need unified data. Unified data is essential for achieving a single customer view that encompasses your entire operation.

Data unification tools pull together multiple disparate data sources and turn that raw data into one centralised view of your operations.

Once your data is unified, you’ll be able to incorporate data sets collected by different teams, departments, or even companies, and process that data for improved organisational alignment.

Real-time insights delivery

Contact centres need to be able to generate actionable insights in real-time, across departments. An AI platform that unifies your data across workflows and helps you derive real-time insights from it is a tremendous asset.

4 best practices for AI-powered customer service

1. Research should always be the first step

When it comes to AI in customer service, research is your most important step.

Think about these questions as a start, and use them to vet any potential platforms accordingly:

• Which channels do your customers prefer to use for customer support? (For example, messaging, SMS, Facebook Messenger or other social media platforms, or phone calls?)

• What are your customers’ principal pain points and how could AI customer service help answer them?

• Where are the weaknesses in your current customer service provision? Do your agents struggle with certain types of customer queries or does your contact centre as a whole suffer from high turnover? Why?

• How much time do agents spend actually interacting with customers, and how much is used up by post-call activity?

Once you have those answers, you’ll have a better idea of the AI-powered customer service features that may be most important to you.

For instance, if you do have high staff turnover and find it’s because agents aren’t well coached or supported, then it’s safe to say that any AI customer service platform you consider has to have some kind of AI-powered coaching.

(With Dialpad, you’ll get Dialpad Ai, post call automation, live coaching and sentiment analysis, and real-time assistance designed for customer service agents, all in one platform.)

2. Build on your customer feedback

Your customers are your most valuable assets. Make sure that you’re regularly incorporating customer feedback into your contact centre decision making. After all, customer feedback is a direct representation of the customer or user experience.

Do they want self-service options? Do they prefer to interact on certain channels over others?

One simple way to start collecting feedback is through a customer satisfaction (CSAT) survey. Your contact centre CSAT score measures how satisfied your customers are with the service you’re providing.

We can create CSAT surveys to automatically play after a customer call in Dialpad, and even set them to automatically ask customers to elaborate on the score they gave us:

3. Personalise the experience across multiple channels

We’re in the personalisation era. Customers expect their conversations with us to be tailored automatically, and for us to understand customers’ needs without making them repeat themselves every time they talk to a different agent.

Just having real-time customer data isn’t enough—you have to be able to use it and make it accessible to everyone on your contact centre team.

4. Consider having a monthly/quarterly/yearly performance analysis

Nothing ever stays the same for long. So make sure that you’re constantly reassessing your customer service processes.

The best way to do this is to schedule periodic performance analyses and reviews. You’ll be able to stay on top of what’s going well and what’s not, then make any necessary changes based on the data at hand.

This can include metrics like churn and customer retention rates. Consider looking at a before-and-after of these metrics: how did your team score before incorporating AI into your customer service, and how did you score after having used it for some time?

AI-powered support is the future of customer service

For some businesses, AI is the future of customer service. For others, it’s a future that has already started.

Your customers expect a lot from their contact centre experiences—personalised, real-time, flexible communications, and fast resolutions to their problems.

AI-powered customer service can be a key asset in helping you achieve that.