AI Investments – Techstrong AI Podcast EP48

Hello, and welcome to the Techstrong AI Podcast. I'm Amanda Ani, and with me today is Chad Donovan. He is the EVP and Chief Product and Strategy Officer at CSG.

How are you doing today? I'm doing well, Amanda. How are you?

Doing well, thank you. So can you share a little bit about your company, CSG, and what services do you provide? Uh, sure, Amanda, happy to, you know, at CSG, we build, uh, software platforms and solutions that really empower some of the leading brands, uh, around the world, uh, to deliver differentiated experiences that make it easier for their end customers, uh, to really do business and connect with.

Think of using paying for services, really the things that they value most. Um, if you ever receive a text message alerting you of a, maybe a, um, a fraud notification or a prescription being ready to be picked up or a technician visit coming to your house, CSG powers, all of those, those interactions and makes sure that we're connecting the brands with the people at those moments that matter that are most critical to, um, how, how they, they wanna do business with. So that's really what we focus on here at CSG specifically.

My role at CSG is, um, really thinking about kind of how we evolve our solutions, um, how we stay ahead of, of the market and how we're, um, really addressing the needs of our customers, um, really in a timely and effective, an effective way going forward. So, um, been here for 25 years. Uh, love what I do and, uh, excited, uh, to share with some of those ideas with, uh, the forum here today.

Wonderful. Happy to have you on our show. So our topic of the day is digital transformation and AI investments to that regard.

So from your experience, you know, we've seen a ton of hype around ai. Now it's kind of starting to settle down, and the big thing is everybody's trying to figure out how to harness AI and integrate it. So what are you seeing from your experience?

Can you share maybe, um, what are business leaders looking at the most and maybe some issues they're encountering? Yeah, I think we've moved past that notion where AI can solve every problem, and we're getting much more focused on what are the, what are the specific area areas that AI can have the biggest impact against. We, we tend to call this pragmatic, um, um, ai, uh, here at CSG.

And, and really what we focus on is trying to deliver, um, you know, end to end use cases. So, um, I think the, the thing that we're seeing and we're most excited about right now is, is kind of moving past the AI for every single use case, and hey, bring us a couple of, of ideas that can actually drive, uh, end consumer value and help us differentiate our services in the market. Um, the other thing I would say is, uh, moving beyond just AI for the sake of cutting costs and, and really thinking about how can AI be used to drive value, uh, within, uh, within the brand or within the engagement with the, uh, the end customer.

Um, I think for a number of years, and, and we have to remember that AI machine learning, these technologies have been around, you know, for the better part of a decade. Um, but now it's, it's the, the time to market, the speed in around innovation, that is a massive change that I think is impacting the industry, and it's up to us, um, companies like CSG, uh, to help unlock the value of the data sets that a lot of these brands are sitting upon and delivering that end, end consumer value through that pragmatic AI lens. Absolutely.

And I'm hearing about this a lot, that as business leaders try to integrate ai, um, what they're finding is maybe they're not getting the most out of their investment as they had anticipated. So what are your tips for, um, them receiving measurable outcomes and in the right direction? Yeah, I think it starts with like identifying the pockets of use cases that have, um, the biggest impact.

You know, one of the things I've noticed is, you know, we can get lost in kind of the perfect architecture, the ideal way to manage data. Uh, we've got silos of data across 15, 20, 30 different application sets. And so let us just get all that data normalized and structured and in a place that allow us to unlock the data first.

And I think that can cause us to then get into this mindset where if the data's not pure, if the data's not perfect, then we can't start to solve real problems. And what I would challenge folks with is, if you start to, to really think about the use cases that have the most impact, you can actually find pockets of information within your data sets that you can, um, leverage today and, and solve problems. Let me give you an example.

Um, one of the, the biggest drivers into the call center still to this day, especially for kind of that subscription level services is, is confusion about the bill. We've all been in, in a world where you receive a bill, you thought you were supposed to pay a certain price, you get the bill and it's, it's more than you expected. You know, maybe you were on a promotion that rolled off, or maybe there was a strange pro rate or a discount that, uh, that got hit.

Maybe there was a new tax change or tax code that impacted you in a certain jurisdiction. And that creates confusion. In fact, almost 50% of calls into a call center today, um, are tied to, um, customers calling in about their bill.

Um, so what we said is why, why can't we kind of turn that on its head if we know that bill confusion is driving, um, calls into the call center? And that then leads to frustration with customers, long call times, um, et cetera. Why don't we be more proactive through generative AI in reaching out to our customers and having personalized conversations?

So if Amanda, your bill cycles, um, and I notice that there's a change month to month, I can just send you an email and that that's very personalized that says, Amanda, your bill changed for these reasons. If you have any questions, please click on this button. That will drive you to a microsite where you can have a real engagement with a, um, you know, with the generative AI chat bot that, um, is looking at your specific details and having conversations with you about why those changes occurred.

Um, and again, we've seen that drive down, um, from that 50% of calls in the call center, almost 75% of those calls are now being addressed through these generative AI channels, which are then driving down those, those bill confusions and those frustrations. So I think just an example of, again, a, a pretty narrow use case that's focused on a huge impact that's driving cost, obviously at the, uh, at the, at the brands and the operators that are dealing with them. Um, but it's also a way in which I think you're engaging with a customer on a much more personalized level.

Um, whereas before everything was just kind of generic. We sent you a message, your bill's ready to be viewed, you didn't really know why, and now we're getting more personalized about. So I think that's just one example of, of how we're really trying to target, uh, use cases and, and leveraging this technology around data sets that are, that are more, um, reliable and, um, accurate that we have access to.

Yeah, absolutely. And just staying in that call center, center realm, I have been hearing about AI tools that on the employee end are helping with the customer service. So AI tools that help, um, research and, and answer a question a lot more quickly.

And then, um, AI tools for, um, uh, even for, uh, flexible scheduling, being able to, um, find a place in the day where you can clock out if you have an emergency, and just various AI tools like that on the employee side too. Yes. I mean, it, let's take that same use case.

So again, all of the different variables that go into creating a bill, right, or a billing experience, can be confusing not just for the CU customer, for the, but maybe for the agent or the call center personnel that's trying to assist in that, you know, in that, in that, uh, inquiry. So one of the things we found too is we can use these same tools to allow a call center agent to actually ask questions about, I don't understand why this bill changed. How should I engage with the customer?

What can I, what can I do to better help them? So the, it's almost, it's almost like a real time coach or real time training tool that they could, they can leverage to help them better engage with customers on that end as well. So I do think it goes both ways, and I think we're gonna continue to see really this blend.

I mean, the notion around, well, hey, the call center's dead. Everything's gonna be automated through generative AI and, and these agents that can then handle these conversations. I do think that there's a blend, and when you start to blend these technologies together, you really unlock the power, um, that it can have to the, for both the brand and the end consumer going forward.

Most definitely. So what are some of the biggest issues you see when it comes to the actual implementation phase? Uh, what are the big roadblocks that businesses encounter?

Yeah, I think, I think the first is, I mean, it goes back to data. Um, you know, people again can get lost in this notion that I've gotta get my data perfect before I can, uh, I can leverage it and start to take advantage of these technologies. Um, I think we we're getting better at being able to use the data that's available versus trying to normalize and structure all the data, uh, upfront.

Um, that's, I think that's an evolution of, of AI as we've seen it over the last decade, is being able to use and leverage different data sets. So that's number one. The second one, um, is really ensuring that I'm, I'm thinking about this on a unique personalized level.

What we wanna avoid is when we're very generic in how we want to engage with, with customers. So for example, Amanda, maybe you only want to be communicated through text message between the hours of five o'clock and eight o'clock. How do I ensure that I'm, I'm, I'm that specific about your preferences, um, and only engaging with you on those terms versus maybe myself, um, I wanna be communicated over email and I wanna have it done in the morning between eight, 8:00 AM and 12:00 PM.

So those are the kind of things that we really look towards is how do we collect the right level of personalized preferences and infuse those into our strategies as we engage with customers going. And as AI advances quite rapidly, what do you envision for the future, um, in, in the telecommunications industry, the call center space? Any, any particular place where you envision AI being used?

Yeah. Um, you know, I think right now we're focused a lot on kind of the how do we support customers better and how do we drive out efficiencies and, and, and how we engage with customers, um, based on what we kind of call post transactional conversation. So things like billing, things like, um, how a technician might go to the home in the case of a, um, a trouble call, um, how you onboard a customer, how you activate a SIM card.

Like all of these use cases are things we've been really focused on. I do think we can start to move further along that spectrum and do, how do I engage on a personal level when it comes to selling products and services? Um, if you think about how we sell products and services for subscriptions today, it's kind of one size fits all, right?

You have these big kind of bundles. The bundles of services are broken into maybe your basic tier, your your premium tier, and then your super tier. Um, and then they might have a couple of adjunct, you know, um, premium offerings on the side of, on the side of them.

I think we can get much more personalized about what are your preferences as a consumer? When do you, like, for example, when, when and what do you wanna watch? What additional, um, you know, uh, subscription services, premium services, do you like, take all of that into account and run that through a generative AI engine to come back with a very personalized offer.

So it's not a $99 bundle where there's a lot of services you might not wanna watch, but hey, it's the cheapest price I'm gonna take it. It's now a $99 offer, but it's very tailored to what Amanda wants to watch. We know you like, um, HGTV, we know you like the Cooking Channel, we know you like HBO, we know you like ESPN, and therefore those are the services that we're gonna package together in a very unique way, and then provide the discounts around 'em.

So I think moving towards kind of that value added sales conversation is something, um, that we we're, we're certainly excited about and I think is a, a huge, uh, advantage of some of these systems going forward. Wonderful. And I do like all those channels.

So what if there was one key takeaway that you could leave with our audience today, what would that be? Yeah, I would say, you know, don't, don't let it, don't let the data, you know, drown you in, um, what could be, um, identify two or three use cases today, um, and get started. And, and leveraging these tools and these technologies to unlock real value in, in how you engage with customers.

Um, there's opportunities that can be done, um, and, and taken advantage of right now. And I would just say, um, there's, there's ways to leverage this technology without having kind of the perfect, um, the perfect, uh, uh, menu of, of data elements, uh, defined for you at the backend. So.

All right. Well, thank you so much for coming on the show and sharing your insights with us today. Great, Amanda, great meeting you.

I appreciate it. Take Care. Yes.

And thank you to our audience. Stay tuned. Theirs more.