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At the core of digital transformation is customer experience.

Yes, massive amounts of data, combined with cloud computing, can catapult your business beyond your competitors. But only if you create a better customer experience to match. Customer experience is the next competitive battleground.

AI is transforming the customer experience, and the agent experience for that matter, at contact centers. Being placed on hold is slowly becoming as outdated as floppy disks and fax machines.

You have likely proved this truth yourself. After all, when you are on a website and hit a snag, what’s the first thing you look for, a toll-free customer support number or a “chat now” button? Chat bots are growing in popularity with frustrated website visitors and customers because they answer questions and solve many common problems quickly—even if they are powered by AI robots.

When AI is done right, today’s busy consumers won’t notice the difference between AI interactions and human interactions. A key piece of this is successfully providing customers with an answer or solution as efficiently as possible.

And that is the main reason AI is transforming the customer experience at call centers around the world. AI is fast and accurate. It frees up call center agents to work on more difficult issues.

Here are eight ways that AI is enhancing customer experience at call centers.

1. AI customizes the user experience

Gartner predicts that, by 2025, 85% of consumers’ relationships with businesses will not involve a human being. The customer experience will involve a DIY customer service model, often involving chatbots and virtual agents.

Chatbots are proving to be better at crafting personalized content than humans are. From fashion to health to insurance, chatbots are customizing the customer experience and delivering smarter customer support.

Chatbots deliver better customer engagement than humans do because they are always more knowledgeable than their human customer-service colleagues. Chatbots have access to every customer data point ever created in real time. They easily detect common issues, detect patterns and predict issues that are causing problems for specific users.

The result is that chatbots and virtual assistants often resolve customer issues more quickly than humans can. That means a better customer experience for consumers and call center agents alike.

2. AI delivers more-accurate and more-efficient call routing

Artificial intelligence and machine learning are revolutionizing contact centers by making skills-based routing more accurate and efficient.

Skill-based routing was introduced into call centers in the 1990s. Call centers created a number of profiles to represent their typical customers. Software then paired customers who matched one of the profiles with agents who had the product knowledge or skills needed to help the customer. Remember being asked to press 1 for support?

Today, contact centers are making this process more accurate and efficient with predictive behavioral call routing, which uses AI and analytics to match callers with customer personality models. Predictive behavioral call routing, also called intelligent routing, takes place regardless of how customers contact the company. It works for phone, email and chat inquiries. 

Predictive behavioral call routing uses AI to compare caller data with agent data to route callers to the optimal agent. 

Caller data includes:

  • Caller’s priority
  • Caller’s previous inquiries
  • Caller’s value as a customer
  • Caller’s personality (based on their customer profile)

Agent data includes:

  • Agent’s personality
  • Agent’s current task
  • Agent’s skills 
  • Agent’s product knowledge

3. AI identifies patterns and predicts behavior

Dave Waters of Paetoro Consulting once remarked that “predicting the future isn’t magic—it’s artificial intelligence.” AI is enhancing the customer experience by identifying patterns in customer actions and then predicting their behavior.

AI predicts customer behavior by using machine learning algorithms that interpret billions of customer data points to develop customer personas. AI engines gather customer data from multiple sources and touchpoints. These include:

  • Previous customer communications with the company
  • Geo-specific events, such as purchases made with mobile devices
  • Purchase behaviors, including time and frequency of purchases, as well which products were purchased, and in what quantities
  • Customer interactions on the company website, including pages visited, time spent on pages, and links clicked
  • Psychographic factors, such as customer preferences, values, lifestyle and opinions
  • Source of customer referral

AI then uses these customer personas to analyze call center analytics,  detecting customers who are about to churn, predict the emotional triggers that generate bad service interactions in call centers, and thus improve call center KPIs, such as:

  • Average Speed of Answer
  • Average Handle Time
  • First Call Resolution
  • Customer Satisfaction

4. AI makes automation intelligent

Many of the activities that happen once a customer hangs up the phone are predictable. And, unfortunately, they are often done manually.

Consider the typical insurance claims process, for example. When a policyholder calls a contact center and initiates a claim, the steps that the insurance company takes following that call are predictable, and in most cases, even mandated by law. Today, call centers that handle insurance claims are using AI bots behind the scenes to initiate and automate many of these processes and to keep the claims process moving towards a resolution.

AI speeds up the process by eliminating human, manual effort. It also makes the process more efficient and accurate, because AI catches spelling mistakes, incomplete claim forms and other errors.

5. AI re-skills agents on the fly

Contact centers typically have teams of agents who are trained to handle a range of customer calls. Some agents handle only sales inquiries, for example. Other agents handle only technical questions. While other agents only field calls from customers with billing issues. 

But what happens when a call center starts receiving more calls of a particular type than their agents can handle? Again, in a typical call center, a supervisor has to put out a request to other supervisors, seeking agents who have secondary skills that might help them handle this sudden influx of calls. The supervisor then has to log into various systems to set up these agents so they can start taking calls. 

AI is transforming this process and shortening the process from an hour or so to a few minutes. Now, a supervisor simply hits a button, selects the call scenario that they want agents for, and AI then takes all the steps needed to re-skill those agents on the fly. If the customer call scenario is predictable, and if the steps needed to resolve the customer concern are known and repeatable, then AI is the perfect solution for re-skilling agents as needed to follow these steps when calls for that scenario go above average call volumes.

6. AI speeds up customer support interactions

AI is even being used to speed up customer support interactions in unexpected scenarios, such as inside brick and mortar stores. Think about your last in-store shopping experience when you were in a hurry, but spent 15 minutes trying to track down a shopping assistant who could answer your question, only to find that the shopping assistant didn’t know how to direct you. This happens all the time! 

Now picture this scenario and let’s pretend you had access to a virtual shopping assistant who was immediately available to answer your question and direct you to the right location in the store.  This is what Macy’s department stores are implementing using cognitive AI technology. Macy’s in-store customers now have the option to access ‘Macy’s On Call’ virtual assistant who can answer customer support questions at a moment’s notice. The virtual assistant is trained to answer questions with relevant customized responses, from where products or brands are located, to what services and facilities can be found in a certain store.

This is a simple solution to a problem that is common in store-fronts of all sizes. By implementing this new level of service for its customers, Macy’s frees up its in-store shopping assistants to help customers with more complex questions, while also eliminating a point of frustration for customers and gets them in and out of the store faster.

7. AI is always available

Want to know one of the quickest ways to lose a customer? Only work during business hours. For the remainder of the time (which is to say, most of the time), have an outgoing message on your answering machine that sounds something like this to callers: “We are closed. Call back when we are open.”

Your customers, of course, won’t call back. They’ll call a competitor and give them their business. If you want to compete in the age of digital transformation, you must deliver an outstanding customer experience 24 hours a day, seven days a week, 365 days a year. AI helps you do this.

AI bots work around the clock without coffee or sleep. AI lets you deliver customer support 24/7. Your customers never have to be placed on hold or hear an answering machine ever again.

8. AI is always happy

There’s a good reason that customers sometimes tell call center agents, “I’d like to speak with your manager.” Call center agents are human. They sometimes fumble a call, lose their composure or have a bad day.

But virtual call center agents rarely upset callers. They never get upset the way human agents do. They are always soft spoken and cordial. They are always patient. They never “lose it.”

AI agents, of course, do not have the range of emotions that humans do. In fact, they have no emotions. Which means they are unable (as of this writing), to detect sarcasm or demonstrate empathy. Yes, that makes them less than human, but it doesn’t make them less than effective. For a growing list of call center scenarios, AI agents are efficient, fast and effective.

Expect a learning curve—for you and your bots

One advantage of implementing AI to enhance your customer experience is that you start small. AI, after all, doesn’t stand for “automatically intelligent.” Any AI solution you buy or build has to be trained and is only as good as the data being fed into it. You have to teach any AI assistant about your business, about your customers, about the common calls you receive.

This means you must be willing to make an investment in implementing AI. There’s a lot of data about your company and your customers out there. You have to format and setup that data in a format that AI tools and solutions can take advantage of.

For example, if you want to create a chatbot, you can’t just buy one off the shelf, turn it on and expect it to work. You’ve got to teach your chatbot about all of the different interactions that might come from your customers, and all of the different terminology that you use in your business. Then you must teach the bot all of the decisions and action steps you want your bot to make and take. 

As you can imagine, this will likely be a long, difficult (and sometimes expensive) process. So, the better-organized your data is and the better the understanding you have of your data, the cheaper and quicker your AI tool will be, not only to implement, but to succeed. 

Success with AI is usually measured in confidence scores. You’ll create a process for having humans review the work of your AI assistants. Humans will examine each scenario and then review how the AI bots responded. You’ll essentially test the bot, asking, “Did you hear the customer? Yes.” “Is the suggestion you made the correct one or not? It was the correct one.”

Over time, you train your AI tools to interact with your customers and solve their issues the way your human agents do. The closer your AI agents get to acting the way you want them to, the higher the confidence score you give them. Once your AI arrives at the 90% level of confidence scores, they start acting faster and more accurately than your agents ever can. That means faster and more accurate customer service—and a better customer experience.

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