What if one of your most valuable CX analysts isn’t even a person?
Agility requires not just collecting data, but closing the gap between insight and action at scale. This means empowering every level of the organization, from the frontline to the C-suite, with the right information at the right time to make smarter, faster decisions.
Today, we’re going to talk about moving beyond the score. We’ll explore how Generative AI is shifting the discipline of customer experience from a reactive, score-chasing exercise to a proactive, problem-solving engine. We’ll get practical about how a very lean team at a massive organization can leverage these tools to drive real business impact, especially when faced with an overwhelming amount of data.
We are here in Las Vegas at Medallia Experience 2026 and to help me discuss this topic, I’d like to welcome, Alyse Fuller, Customer Experience Program Manager at United Rentals.
About Alyse Fuller
Alyse specializes in seeking solutions that elevate the customer journey across every touchpoint. She believes in coordinating insights from customers with ideas from internal teams to drive CX improvements that align with broader organizational goals. By translating real-world experiences into actionable strategies, she contributes to the responsive, efficient, and customer-centric culture at United Rentals.
Alyse Fuller on LinkedIn: https://www.linkedin.com/in/alysefuller/
Resources
United Rentals: https://www.unitedrentals.com/
Medallia: https://www.medallia.com
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Transcript
Greg Kilhstrom: What if one of your most valuable CX analysts isn’t even a person? Agility requires not just collecting data, but closing the gap between insight and action at scale. This means empowering every level of the organization, from the frontline to the C-suite, with the right information at the right time to make smarter, faster decisions. Today we’re going to talk about moving beyond the score. We’ll explore how generative AI is shifting the discipline of customer experience from a reactive, score-chasing exercise, to a proactive, problem-solving engine. We’re going to get practical about how a very lean team at a massive organization can leverage these tools to drive real business impact, especially when faced with an overwhelming amount of data. We are here in Las Vegas at Medallia Experience 2026, and to help me discuss this topic, I’d like to welcome Alyse Fuller, Customer Experience Program Manager at United Rentals. Alyse, welcome to the show.
Alyse Fuller: Thank you very much. Happy to be here.
Greg Kilhstrom: Yeah, looking forward to talking about this with you. Before we dive in though, why don’t you give a little background on yourself and your role at United Rentals?
Alyse Fuller: Sure. So I am our Customer Experience Program Manager, working with United Rentals. I’ve been there, I think this is my 19th year now. So prior to this, I was in our contact center environment and then we decided to make our CX efforts a little more formal and get a proper CX program in place and that was eight or nine years ago now. So it’s been a, a journey developing it over time.
Greg Kilhstrom: Definitely. And um, for those that are not as familiar with what United Rentals does, can you give us a little background on what you do?
Alyse Fuller: Yes. Yes, absolutely. So United Rentals is an international construction equipment rental company. We do have some other lines of business as well. We do things like safety training for OSHA compliance or equipment operation. We sell new and used equipment. So we’ve got a few things going on, but the bulk of the business is equipment rental.
Greg Kilhstrom: Great, great. So, yeah, let’s, let’s start at the strategic level here and want to go back to what I teed up in the intro and really just this evolution from chasing good scores. I mean, numbers matter, but you know, simply chasing a score and, you know, not, not so much all the time. So evolving from that to root cause analysis. And so, um, your, your case study mentions a shift in NPS in 2023 that really acted as a catalyst here. So before that, you had high scores in the upper 70s. Can you talk about the danger of a high, high score sounds good, right? But you know, can you talk about the, the danger of a high and stable score and how it can maybe mask underlying issues that only become visible when that score finally changes?
Alyse Fuller: Yeah, absolutely. So we had, I mean, it’s such a success story. We implemented our program, we’re measuring NPS, we’re watching our scores go up over time, which is exactly what you want to see. We’d have periods of consistency, a little bit of cycles here and there, but they were explainable. We knew what was causing them or what they were related to. Not always even something going wrong, just part of the business. When it changed the pattern that we were seeing, the challenge is when your scores have been very good, you’re working with a really high-performing team for a really long time. There had been such consistency there, it makes it difficult to really pressure test your insights engine. You need some variety and you need some new signals and things to really see, will all these rules and all these conditions hold up no matter what the circumstances are? Or is it possible there’s something lurking under the surface that I haven’t been measuring all this time? So being able to get a little bit of variety was actually a really interesting moment and a really compelling moment because there was a lot of attention on it and it gave me that opportunity to really pressure test to make sure we were measuring all the right things and had been for a long time.
Greg Kilhstrom: Yeah, yeah. And so, as a, as a small team in, in a very large organization or a team of one, as the case may be, um, as you just said, you know, you’re facing a, a lot of data to, to need to pour through. What was the biggest strategic challenge that you faced in trying to move the organization’s focus from just that NPS number to the why behind it?
Alyse Fuller: Yeah. So I think the, you know, we’d understood the why. I mean, when we rolled out Medallia, we spent a lot of time on the change management piece, really training, making sure everybody knew what we’re measuring, why we’re measuring it, why it matters. So we had a pretty good intrinsic understanding of that. But the underlying why of the NPS score itself, again, we had this kind of catalyzing moment where we said, well, why? And when we look at our analytics, we’re trying to do a 5 Why analysis, right? And we can only get so deep with the customer side and then the why start becoming more operational in nature. And that’s when I realized we we might benefit from some additional information to help get to that level of the whys that we could be more strategic in how we’re thinking about different levers that we can pull that would have the most impact. I think we knew what levers existed, but being able to map that back to an NPS impact was, was really helpful.
Greg Kilhstrom: Yeah, yeah. So let’s get a little more um, tactical here and and talk about democratizing insights with, with AI. So a key part of your solution involved synthesizing uh notes from frontline team huddles. How did you bridge the gap between that internal employee voice and the external customer feedback? And why was that connection so critical for identifying some of those real root causes?
Alyse Fuller: Yeah. So because our, again, our our voice of customer is coming directly from our customers, they’re standing on one side of the, I call it the rental counter in our, in our locations, right? They shouldn’t know everything that goes on behind the scenes to deliver them a great experience. They shouldn’t know what metrics we’re measuring or, you know, all the logistics that go into it. That’s where we’re playing superhero in the background to make all of those things happen. So the customers can talk very well about the symptoms that they’re experiencing for better or worse, you know, usually for better. But they, thank goodness. But they, they don’t know all of the logistics. So we really had to look at or get input from people who are already behind the curtain and they know all the details of all of our systems and what it really takes to deliver that kind of experience that we’re trying to make sure we do.
Greg Kilhstrom: Yeah, it’s like you never want your customers to have to understand your org chart or or all of it. Yeah, exactly.
Alyse Fuller: No. Right, exactly. Or they, you know, just different operational teams and how we navigate together, right? Like from sales to a delivery driver, to a service technician. They all have very different roles and have to be well coordinated.
Greg Kilhstrom: Yeah, yeah. So you made a conscious decision to launch Medallia’s AI-powered Smart Response tool with no formal training, just relying kind of on its intuitive value. Can you talk about that utility over novelty approach to rolling out AI tools and how it helped you gain buy-in and and maybe, you know, minimize anxiety on the frontline?
Alyse Fuller: Yeah, sure. So we got, I call it a happy accident. I ended up collecting a lot of commentary from employees internally about how they were thinking about AI usage in our existing tools. I wasn’t trying to talk about that, but it was an interesting topic and I had the information. And then even comparing that with external research on in general public attitudes toward AI. And there’s a range of motivations, usually even role-specific motivations about what would lead to AI adoption. But a big resounding theme is it just has to work. It doesn’t matter what kind of technology it is, but it’s got to be reliable. It’s got to be consistent. It’s got to make sure that I don’t miss an important step or give inaccurate information. So that trustworthiness was huge. But the idea that, you know, it just needs to work, it’s supposed to be easy. It’s supposed to be integrated into what we’re already doing anyway and make things easier and more efficient. So when thinking about smart response as the first one we rolled out, it’s already embedded in a process that we use all the time. It’s an easy button. It should not require a big announcement or too much fanfare, which to your point about anxieties, if we make too much fanfare and, you know, hype up the AI factor, that can be off-putting to some people when really they could benefit from the tool. So let’s just make the tool available and see what happens from there. And the natural adoption of that has been a really beautiful upward line, um, just from making it available and not having to train.
Greg Kilhstrom: Yeah, yeah, it’s a, it’s a counterintuitive principle, but I, I agree.
Alyse Fuller: Yeah. If it’s easy and it works, it will be used. Yeah.
Greg Kilhstrom: Yeah.
Alyse Fuller: I think in general across AI functions, it makes sense to have some training around things like, you know, we would never put customer PII into an unsecure chat GPT or something, right? So it makes sense to have some communication on a broader scale. But for individual features, we don’t always need to hype it.
Greg Kilhstrom: So let’s talk about measuring the impact and even redefining speed and and scale. So you saw a significant and measurable impact as as we’ve talked about a little bit already. Uh, you know, things like frontline managers closing alerts six hours faster. In your view, how does investing in the right data collection and measurement platforms enable customers to get those faster resolutions and and really feel heard?
Alyse Fuller: Yeah, so I mean, the great thing with smart reply in Medallia, right? We’ve got Medallia itself is a really powerful tool with a lot of really great features. So there’s efficiency in, first of all, getting that feedback to the right person to take action on it and having even a a complex setup for who is the right person. We can automate all of that. So the customer feedback gets straight to the right person immediately. And then they can respond. And with smart reply, it’s such a great starting point compared to previously using just canned responses that had to start off kind of generic so they could apply to anything. Even when we’re in a hurry, we’re able to let the AI do some of that foundational thinking first in what is a good response for what this customer has actually just said. And we can tweak it and fine-tune it before we hit send. And it’s, it’s so fast and efficient. And we’re getting to that highly personalized reaction very quickly.
Greg Kilhstrom: Yeah, yeah. So I mean the, the, the repetitive kind of rote stuff is, is you get to take it a step further instead of that taking up the time, right?
Alyse Fuller: Right. Yeah. Right. Yeah, and feedback from our managers has been really positive that it is a much better starting point and yeah, they’re not having to spend so much time wordsmithing and Yeah. And really painstakingly crafting what do I think is the exact right response?
Greg Kilhstrom: Yeah. And so data certainly is, is a key part here. We, we’ve touched on this a little bit already, but the efforts that you undertook really democratize data access within the org. How does having a, a measurement platform that empowers senior leaders with AI-driven intelligence summaries as well as frontline managers with those actionable alerts, influence how you talk about CX within United Rentals?
Alyse Fuller: It makes it all natural language, right? It’s whether it’s the voice of customer, which sounds like the customer in the insight summary. It’s in words that our customers use. Makes sense. The internal pieces that we have going, those are in our own lingo. So even things like abbreviations for metrics or process names, we don’t have to have a definition for everything, although it helps to define some stuff with LLMs, but it does such a good job of organizing those things in our own terms. So looking at an insights report now says customers want us to focus on these things. Our teams are talking about these things. It’s all in language that we can just very easily read it, understand it. It doesn’t require as much interpretation or context to, to really get to that place where this is actionable information.
Greg Kilhstrom: Yeah. Well, and you’ve said AI’s greatest value is shortening that distance between listening and taking action. Now that you’ve automated the proving of the problem, how is your role as a CX leader changed? You know, where, where are you able to focus at least some of that energy, if not a lot more of that energy?
Alyse Fuller: It’s getting repurposed into more of the action planning piece. As we’re maturing here, my role, you know, it still has the other elements team of one. So I’m still the administrator and an analyst and a trainer and, you know, all of that still exists. But that time spent on the analyst side is what’s been greatly reduced. I can generate these reports, pop them into a PowerPoint or whatever, and then spend my time more on discussing what we’re seeing here, gathering other context that maybe isn’t included in Medallia, bringing more just a more robust nature to what that conversation can sound like. And then maybe offering almost a kind of consultative approach with the folks in the rest of the organization, those leadership teams to try to decide, you know, what, what can we do with this now? What would you like to try to do with this now? And maybe create some realistic goals based on that.
Greg Kilhstrom: Yeah. Well, and I mean, it also seems back to the data democratization piece because everyone is able to see, you know, it’s maybe a different view, but same data and and and same outcomes and insights, you know, instead of this one person over here talking about, hey, the score is, is XYZ. Now everybody can be speaking the same language to your point.
Alyse Fuller: Exactly. Yep. And it’s definitely, you know, and I’d say a year ago, if somebody wanted a real insights report, they’re waiting for a push report that I’ve automated to come to their inbox once a month or once a quarter. They can’t just on the fly. I’ve got a meeting coming up and I’d like to be able to speak to this. And now because the insights are easier to consume, easier to access, more democratized. There’s more enablement that goes into, can you get to this on the fly in the moments where you need it? And you’re not relying on a report that came to you three weeks ago or whatever, you know.
Greg Kilhstrom: Right, right.
Alyse Fuller: So I don’t have to wait for me to help with that. They can go get it if they want to. I’m happy to help still, of course, but, um,
Greg Kilhstrom: But it’s nice to have the access when you need it.
Alyse Fuller: It’s nice to have that real-time. Yeah, you can do it if you want to. And it’s easy enough that they don’t have to be an analyst themselves in CX data in order to get it and understand it.
Greg Kilhstrom: Yeah, yeah, that’s powerful. Well, Alyse, thanks so much for joining today. A couple of last questions as we wrap up here. You know, we’re here at Medallia Experience. What’s been a highlight so far? I know we’re, we’re early in the conference still, but yeah.
Alyse Fuller: We’re early. So the, the opening, uh, keynote is always so exciting in the, in the grand opening where we get to see a little bit of what the road map looks like for this year. Speaking of action planning, I’m looking forward to some of the new features that we have coming out here around kind of integrating that documentation side of, okay, we’re having these important conversations, we’re talking about actions we can take. Now maybe we can start logging those and and seeing what the outcomes are over time. So that sounds like a really a really cool feature. I’m looking forward to that. Yeah, those opening speeches are always so exciting.
Greg Kilhstrom: Yeah, definitely. Well, uh, last question for you before we wrap up here. What do you do to stay agile in your role and how do you find a way to do it consistently?
Alyse Fuller: I read a lot. I’m constantly looking for the latest industry benchmarking reports or news from Medallia, news from Forester or Gartner, you know, as much as I can. I build time into my calendar to go hunt for that information. I think making the time and having it actually pre-planned means you have that, that space to, like, it’s not something I do when I have time because whoever has time. You have to block it and make sure that you’re kind of getting out for that or I do anyways, to make sure I’m going to look for it proactively.
