Glia

Transcription:

David Taliancich (00:11):

Thank you. At Glia, we're in the business of automating and elevating interactions. That's our core business. We do it across a multitude of channels, whether it's voice, digital, video, or social media. That's our primary focus. Today, we're going to talk about how AI is infused across the platform, but more importantly, how AI can really help you to elevate and automate those interactions. We'll talk about it from a consumer perspective, then from the agent perspective, from a manager's perspective, and then finally, from the executive perspective.

At the end of the day, what I hope you walk away with is a really strong understanding of how Glia has helped to support financial institutions, banks, and credit unions over the last 13 years in driving really superior customer experiences. A lot of what we're talking about today, particularly over that back quarter, is focused on consumer experiences. I want to start, though, with that consumer side.

Now, we've heard about different IVAs (intelligent virtual agents). I'm going to spend a little bit of time starting the conversation off here. Oops, there we go.

(01:12):

So imagine that you're a consumer and you have different questions. Maybe I want to come in and find out, "Do you have credit cards?" Oops, typo. And of course, it'll come back and say, "We have many different cards and different options to choose." But maybe I want something even a little more complex to ask about. In this case, I'm going to ask, "Hey, I tried to make a payment at my car dealership, the transaction was declined. Can you help me?" A much more sophisticated, complex response. And as I ask that question, it's going to come back and give me a response that makes sense.

Now, everything you're seeing here is an unauthenticated view that the consumer has. Obviously, if we were behind the firewall, behind the PIN, there would be much richer information that can be shared. And I could talk to an agent or get more dialogue.

(02:02):

But even here, if I want to come in and say, "I lost my credit card," I'm going to get a response back that says, "Hey, can you help me either freeze my card, or in this case I want to talk to an agent." And this is where I'm going to transfer to that agent experience. What I want you to walk away with is knowing that from an IVA perspective, Glia can handle this from a voice intelligent virtual agent, or of course, from the digital standpoint.

But in this case, now we're going to transfer to an actual agent, and I'm going to bring you over to that agent's experience from an AI perspective. I'm going to answer this interaction, and a couple of things I want you to note as it comes up. The first thing is a transfer. The IVA was handling all of the work, but now a human agent, it's been elevated to help them out.

(02:52):

So the first thing you see down here is a transfer message leveraging our proprietary LLMs that are tuned for financial institutions. We can see that in this case, the customer inquired about a credit card and then they had something declined at their dealership, and then they ultimately transferred this call to the agent. So that ability to understand context of why that consumer is calling and talking to us now is here and available.

The other part that I want to point out is what Glia is doing: we are saving all of the consumer interactions for an individual historically, and we can go back and see what's called a heads-up. The AI is going back and looking at prior transactions to understand, "Hey, what was the last interaction? Why did this customer contact us?" And it was about an auto loan, other recent transactions, or asking about credit cards, other information, and if there are any unresolved issues, those will be noted as well.

(03:41):

This is an incredible accelerator for your agents to not only understand what this consumer was calling and contacting you about previously, but it gives them a heads-up to know how to extend that conversation going forward.

What you're seeing in part here too is our entire platform. If I wanted to transfer to a video call, I could do that. If I wanted to start a screen share, I could. In this case, what you're seeing is context live observation. I can see where the individual consumer is. If they jump to a different page, I can see that they move to that page over here. I can also initiate a co-browsing session so I can work with them in order to better understand what they're doing and help them through that transaction by taking control of that application. So all that's empowered within the Glia platform to have your agents help to serve those consumers with AI enrichment throughout the entire process. I'm going to end this transaction.

(04:34):

The other part now becomes the entire dialogue back and forth between the agent and the consumer was captured and a set, we call it an engagement overview, I think it was called wrap-up notes, where I am seeing that AI, the little blocker AI, has told me what the topic was, what the disposition was, a variety of information about that interaction and what we might have handled on the call. It's rating that consumer. If I wanted to change this as the agent before I post this to my CRM, you'll note that I'm tracking not only what the AI told me, but also what the user did. I can submit this in an incredible time saver, but also creates historical context that we later reference for that heads-up capability you saw a moment ago.

If I'm a manager, maybe I want to come in and be able to look at prior interactions.

(05:16):

And of course, any of us who have dealt with any quality assurance processes know it's very hard and long to listen to these calls. What Glia does is takes the entire transcript of a conversation and we're able to synthesize this, leveraging AI, into a variety of categories and questions that it would be very easy then to understand what that interaction was about and how to leverage it.

Also, if the manager wanted to, using our LLM technology (we call it Ask Cortex), can ask any question about this individual transaction to find out what's going on. This is the way a manager ultimately can really leverage AI very easily to understand and coach and develop their team members who are servicing your consumers.

I'll wrap up with the final of the four areas of AI that I wanted to focus on, and this is really our executive AI feature. I'm going to focus only today on our quality analyst, but imagine, for example, if you wanted to be able to ask a question that you would normally ask a business analyst, but you needed a faster answer and a richer answer. For example, "What are my upsell opportunities in my customer base?"

(06:27):

As it's going and creating this answer, what you need to understand is that our AI has been built from the ground up for financial institutions. We understand the lingo, the language, the dialogue, because our models are constantly being tuned. Oops, let me re-ask this question here. "What upsell opportunities do I have with my customer base?" Sometimes I type things.

The LLMs are able to look at all of the interactions. One of the most powerful and unharnessed areas that you have within your entire organization is... I'm typing the question wrong, and that's my problem here.

(07:17):

Ah, here we go. This might be easier if I do it this way. It just doesn't work. We'll move on.

But the amount of data and those interactions you have with your consumers is incredibly valuable. So the ability to harness all of that information over weeks, months, and quarters and analyze that becomes incredibly powerful from a business analyst standpoint. In this case, we're asking about upsell opportunities across this, and it's coming back with an answer. Oops. This is what happens when you do a live demo with a live system. We'll try one more time.

But all that unstructured data that gives you incredible insights into what your consumers are thinking and feeling can be harnessed in a very easy way, assuming that we ask the question in the right way in this particular case.

As it's thinking, it's going to come back with the insights that it would take your analysts hours or days potentially to come back with, providing you some interesting information that can help you drive the business decisions within your organization.

So in this case, it comes back with home equity lines of credit, reward credit cards, high-interest savings accounts, and CDs. This is the kind of information that it's looking at based upon what your consumers are telling you.

And with that, thank you very much and join us over at our demonstration stage. Thank you.