Using AI to Predict Needs and Deliver a Fluid CX

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I am going to talk about two things that, at first, may seem unrelated: Artificial intelligence (AI) and empathy. To some, they might even seem at odds with each other. How can AI—which is engineered around logistics, data, algorithms, and machine learning—be empathetic? Alone, it can’t. But with thoughtful strategy and attentive execution, it can inject great amounts of empathy into your customer and employee experiences.

Successful organizations use AI to predict customers’ needs, both collectively and individually. By better understanding our customers’ needs, we can deliver a better experience, and in turn, make that experience better for employees as well. Have you ever had a problem with a product or service and needed to make a phone call for customer support? Did they make you start from the very beginning? Is it plugged in? Have you tried turning it off and then back on?

You likely attempted the common-sense steps before you called. Now you are wasting time being asked to try things that you have already attempted—and the experience is off to a poor start. This is where empathy and AI come in. At Clearlink, we can see exactly what page a customer is calling from, any keywords they searched while on the site, and relevant clickstream data. We can then predict the issue they are calling about before they say a word, route their call to an agent we know has been successful with that particular issue, and arm that agent with a dynamic three-step solution process for how to address that specific issue.

As you can imagine, that experience is much better for the customer than the first scenario, which means it would be far more positive for the agent as well. That is one of the many ways we are using AI-fueled empathy (one of the five stages to the Design Thinking process) to improve the customer and employee experiences.

Good CX doesn’t start when the customer picks up the phone. It should start before that.

Part of having a truly intelligent CX means fusing AI into interactive voice response (IVR) technology. This involves identifying multiple factors that have already occurred before the customer picks up their phone in order to predict how we can best serve them.

Say we have a customer who is interested in one of our home services. We can identify where the customer is located, and, using that geolocation, we can identify which offerings or types of services are available in their area. We can see they came to our site and searched for a specific term or keyword. We can also see where they have navigated on the site and what page they were on when they picked up the phone to call.

We can surmise a few things about this customer before a word has been spoken. For example, they are in an area with fiber-optic availability, they searched for “fiber internet connection,” and they clicked on packages featuring fiber-optic internet coupled with TV service.

We can then serve an IVR that is tailored to fit this specific scenario and deliver the customer to an agent that has been identified as knowledgeable about fiber-optic services. We can set that agent up for success by giving them recommended packages, offerings, and resources before they even greet the customer.

We are saving that customer time, easing friction by not talking about products that aren’t available or of interest to them, and helping them choose the packages and products that best suit their needs and wants. All of those things converge to help us deliver an enhanced CX from the very beginning.

Correct use of AI to improve CX creates ripples that reach and benefit multiple areas across your business.

One of the best things about using AI is that the more you use it, the more effective it becomes. Each and every interaction with a customer is a chance to prove or disprove a theory, add more understanding to a set of circumstances, and create a clearer picture of what your customer base wants. It is like a blade that gets sharper every time you use it.

Plus, every interaction stretches your database. You can begin to put together sets of circumstances that, at first glance, seem irrelevant to one another but through repetition illuminate a solution to a problem you didn’t know existed. These findings help you improve your organizational processes, both online and offline.

AI helps your company and your individual employees be more agile as well. Where data sets that were too massive to be effectively deciphered may have been dismissed before, we can now use AI to paint a picture of where our customers are experiencing friction—and we can adapt our approaches in multiple ways to respond to those pain points. Are we getting a lot of calls about taxes and surcharges on customers’ bills? Are they pervasive in one particular state? We can assign an agent with a strong understanding of taxes from that state and explain why they are seeing higher totals on their bill than when they lived in another state.

Are we often getting the same question about a product or service? AI can help us provide our agents with the necessary resources the instant those calls come in. But we can also take these learnings further and address this particular confusion in the content on our website. We can deflect a percentage of these support calls by addressing the need before the customer has to pick up a phone.

Companies can now take these learnings from AI and improved how we position our offerings, match customers with agents, expand the resources those agents are using, and refine the content on our websites and ads. Through empathetic use of AI, we can deliver a better customer experience and save money along the way, through increased efficiency, allowing us to maximize our resources.

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