Experts of Experience - #44 Implementing AI in Customer Experience

Episode Date: August 21, 2024

 On this episode, Meghan Hatalla, Senior Customer Experience Researcher at Veritas Technologies, discusses the use of machine learning and natural language processing to enhance user research and improve customer experience. She highlights the importance of data analysis, sentiment analysis, and personalization in understanding user behavior and needs, and talks about the challenges of implementing AI in customer experience.The role of machine learning and AI in processing large data sets and identifying patterns in user behaviorThe importance of qualitative research in understanding user experiences and problem-solvingHow to Implementing AI in customer experienceHow to build trust and emotional connections for a positive customer experienceKey Components of Effective Customer InterviewsThe Role of User Research in AI–How can you bring all your disconnected, enterprise data into Salesforce to deliver a 360-degree view of your customer? The answer is Data Cloud. With more than 200 implementations completed globally, the leading Salesforce experts from Professional Services can help you realize value quickly with Data Cloud. To learn more, visit salesforce.com/products/data to learn more. Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org.

How do we move out of like this kind of data swamp into a beautiful data lake house where everything's organized and beautiful and accessible? Trying to figure out how to put a dollar sign on the cost of frustration is hard. That's probably the biggest thing people hate, right? Is when you're talking to a chatbot and you're frustrated and you're mad and the chatbot's like, oh, I'm so sorry to hear that, right? Yeah. One of the things that I'm the most excited about when it comes to bringing AI into the customer experience fold is how we can really deliver exactly what that customer needs in that moment. Hello, everyone, and welcome to Experts of Experience. I'm your host, Lauren Wood. Today, I'm excited to have Megan Hatala, Senior Customer Experience Researcher at Veritas Technologies. We are going to dive into Megan's experience in leveraging machine learning and natural language processing to enhance user

research and, of course, the end customer experience. Megan, how are you? I'm great. How are you? I'm doing well. So in reading a little bit of your background, you have extensive experience in using machine learning algorithms for UX research. I'm really curious to dive into that and understand how the integration of AI has really transformed the way that you approach UX research? Yeah, there's, I mean, it's a wide spectrum of applications that we can talk about. I think the most appropriate approach is probably involved data analysis. So using machine learning to process large data sets, I think that's probably the most common thing that people are doing everywhere,

not just at Veritas. And that's really key in just being able to identify patterns in user behavior, identify any sort of like sentiments that's coming back from feedback, from reviews within the context of help or in chatbots.

That's really important too, because that's probably the biggest thing people hate, right? Is when you're talking to a chatbot and you're frustrated and you're mad and the chatbot's like, oh, I'm so sorry to hear that, right? Yeah. Definitely personalization. We're hearing a lot about that from our users. And that kind of dovetails in a little bit with predictive analytics too, right? So within personalization, people are, you people are at a very basic level looking for things they don't know that they need. Does that make sense? So they're analyzing an issue that's

happening within their system or something like that. And it's, I don't know, it's an interesting thing to watch users process things or try to talk about how they problem solve. Because a lot of it just goes back to that kind of key component of like, you don't know what you don't know. They don't know what they're looking for. But you can see, not to be like simplistic, you see like the hamster wheel like running in their head as they're trying to think about it. Personalization feeds into that because it helps them adapt in real time, right? And ideally, as it learns a little bit more about them and the predictive modeling piece, it'll give them the information that they need without them really realizing that they need it.

Does that make sense? Or they get it before they know they need it? Completely. I mean, that's something, one of the things that I'm the most excited about when it comes to bringing AI into the customer experience fold is how we can really deliver exactly what that customer needs in that moment. And it's not just what the segment needs or what a lookalike needs. It's what that person is actually needing. And so I'm curious to just dive a little bit deeper because I would imagine Veritas is a quite complex system. And could you maybe give an example of one of those like hamster wheels that someone might be on as it relates to Veritas' technology? So I, in particular, work on their flagship product, which is called NetBackup.

NetBackup is used by 80% of the top Fortune 500 companies. So it's very highly regarded, very common, right? In one way or another. But on that same level, it's very complex. Like you need a computer science degree to work this thing. And well, not just like the product itself, but like all the other systems that feed into it, right? So it's data protection, like on the highest level. So we have our net backup users and they have a lot going on, right? There has been a really interesting move, I think, in IT environments toward more generalist positions, right? So,

I mean, obviously, there's still areas of specialization, but everywhere, and obviously, this isn't just in IT, but companies everywhere want to do more with less and whatever that entails, right? So, admins are being tasked with knowing or having a wider range of responsibilities. And to respond to that net backup is kind of a started and forget it product, right? It's not something that they're going to be in theoretically. They're not going to be in it every single day unless there's big issues or they just have really big environments or a lot of complexity involved. One of the ways that we've helped them simplify how they can manage their environment is this thing called activity monitor.

So activity monitor is kind of a dashboard that shows them everything that's going on in their environment. So how many backup jobs are processing if there's other things that are running and it gives them a status that shows them like, is it the most simplified version, right? Like, is it running? Is it complete or did it fail? So most of the time that they care about is when it fails. And that's where, you know, from a user experience research perspective, getting to watch them address like the fail job is interesting because they can open

up the job details. They can look at everything that happened within the job and what they're looking for is what we're interested in. And that's really where we can't see inside their brain, right? Except for the hamster wheel that's running and turning. You can see everything that they're doing and probably make some good assumptions around what they're trying to get to. Yep. And, you know, they're looking, they're looking for patterns. They're looking for whatever led to the failure. So they're opening other details. They're looking at whatever's happening. They want to find the details. They want to find more insight. And we can help them a little bit from a research standpoint, right? Or we can report back to our PMs, to our stakeholders, to our engineers and say, this is what they want to be doing.

How do we code that information for them? How do we consolidate those sorts of things? So being able to somehow translate the hamster on the wheel running into something actionable has been probably one of the bigger parts of my job this year as AI has really come to the forefront. Well, it's bringing so many opportunities for us to be able to take these large data sets that even a couple of years ago, I know for myself running customer success teams and customer service teams, we have so much data, but it wasn't very easy to put all the pieces together. And I think that's something that's really changing quickly. And I'm curious to understand, like, what tools are you using to process that? Is it something that you've built? Is it something third party? I'm like,

always looking for tips on, you know, even on the lightest approach of UX research. Like, how can I take these datasets and put them together so I can really see what's going on for the customer? As far as datasets sets go, we use full story at Veritas. And I personally have not been like fully hands on with that piece. My job is much more on the qualitative side of things. But from what I've seen, it's a little bit of refining work. But we're getting there. I mean, like with pretty much all ai technology these days it's like whatever company has been implementing ai it's still we're still so early and there's a there's more to come i'm sure i hope yep yep i think you're exactly right we're also looking

into using um hotjar and pundo for data analysis as well or for that like big like getting through the big data sets. Yeah. And yeah, I mean, it's so difficult. Like at the end of the day, how do we move out of like this kind of data swamp into a beautiful data lake house where everything's organized and beautiful and accessible, right? A data library. I don't know.

I love that analogy. Let's get out of the swamp and into the lake house. Everything's organized. It's interior designed. It's like picture perfect. Everything is where everything's just where we need it. We know where to find it. That's what we really want. Data pantry. I could think of a lot of analysis probably. Well, I mean, I think it's something that we can definitely be looking forward into the future to have some more organized systems that really allow us to do this. I think it's something that we can definitely be looking forward into the future to have some more organized systems that really allow us to do this.

I think it's something I speak to a lot of folks about and we can see it's coming. It's not quite there yet, but we know this is an amazing application for AI and really helping us to personalize better for our customers and understand the pain points that they're seeing day to day within our tools. We can see it much more clearly. We can communicate it to our teams much more clearly. And yeah, in my opinion, the future is bright, but not quite there yet. Yeah. So you mentioned that you focus more on the qualitative side of things. Tell us a little bit about what that looks like and how you approach really having those

qualitative conversations to understand what is going on for your users. I feel like I'm a little bit more of like a qualitative researcher, but I don't think you can ever completely divorce quantitative from qualitative, right? Like quantitative gives us like a starting point, right? And I know you could probably argue either way, but for me, qualitative helps us narrow down the field into like, what do we, what do we want to focus on? Right. What do we want to like drive these interviews or these sessions or these feedback forums, whatever it is that we're running. And at least for us in Veritas, we're in an interesting place, not just with like the

advent of AI, but we're trying to align with our customers' needs and expectations, like all businesses, right? And for a lot of our customers, that's in the cloud. So gaining an understanding of what their needs are in terms of what the future of their IT environment looks like, first of all, and just what their propensity is for these different aspects that they can be doing, right? And so from a qualitative perspective, what I've been working a lot on is working on building some sort of feedback loop into all of our research. Well, I say all of our research into my research and then helping others develop their own feedback loops, which, you know, the idea is that if we have these mechanisms in place, these customer support channels, social media, whatever it is that we can get like more

information through, it'll help us just receive and address things appropriately, if we can route them as necessary. And the other kind of like, I don't know, tentacle that that reaches into that I mentioned a little bit is that cross-functional collaboration. So we want to promote that collaboration, not just between on the research team, but being able to get that feedback out to the engineers that need it, to the PMs that are making the decisions, to any stakeholders, just to make sure that it's all, you know, being integrated back into the product and it's just informing decision-making at every level. I can imagine one of the more challenging aspects of your role is making sure that the information that you're gathering is really being, like you said, collaboratively shared and acted on. Is that true? Is my assumption correct?

What are some of the ways? Totally. And I mean, I've experienced that in the customer success and customer service seats that I've sat in where I'm like, I'm sitting on so much data and so much user insight and information that I know the rest of our teams will be able to use and implement to create a great customer experience. But it's not always top priority to take the insights that I'm finding and actually put them into the tool in the system. So I'm curious to know if you have any thoughts. What tactics do you use to really get the attention of other functions so that they want to implement the insights that you're finding. Yeah, that's interesting. And like you've hit on, it's not a unique problem. I would venture to say that it's something that all research teams or customer experience teams in general deal with on some level. And I don't think that it's ever, you know, that it's going to sound like so, I don't know, not crass, I guess, but it's not like people don't care. Totally.

Or that they don't want to know what customers think or that they don't think customers have value. But it's just, I think it's a matter of time. I think it's a matter of budget. There's just a lot that goes into how we make product decisions in general. What do we have time for? Right. What are we promising versus what are we delivering? Classic, classic thing.

Right. Yeah. And I think goals is a huge element to that as well. Like, what are my goals and how do they relate to the product and the engineering team's goals? Are we both marching in the same direction or am I finding things that are just not on their roadmap for the foreseeable future? And that means maybe I need to be looking in different directions or maybe they need to have their goals aligned because really at the end of

the day, in my belief, as a customer experience leader, we should always be putting the customer's experience as a top priority, but not everyone always agrees. And so it's a matter of negotiating what are those key goals? What is that core direction that everyone is marching in together? It's something that I say to every CEO and COO that I meet, like, are you making sure that everyone is marching the same beat and the same direction? Exactly what you said, right? And I think everybody's focused on kind of the obvious things, right? Everybody wants to be more efficient. They want more functionality. They want to solve problems, but they're kind of ignoring the emotional aspect of that. And I think a lot of that is because like, it's hard to quantify,

right? Like how do you say that you're hitting your goals because of emotional experience? Yeah, totally. But it's so important, right? Like creating a positive emotional experiences, whether you're, you know, fostering these personalized interactions or displaying empathy or trust, all of it just helps customers feel valued and understood and fosters more long-term loyalty and advocacy, all those good things that we say we want. And it also, on some level, gives them a little more of a stake in the product

and makes them, I think, just slightly more forgiving of the other areas where you're falling short, right? We have a product that is focused on, well, it's called IT Analytics. And our customers aren't loving it, to put it mildly. And it's been, I'm not sure how long it's been, a little over a year now. And they've done some iterative updates and things like that. And I've done like a handful of studies, like tangentially related to it.

And customers are frustrated that they're not seeing improvements. They're not seeing the feedback that we're giving, right? Like one of the things that we've heard is that it defaults to displaying time in UTC versus just like grabbing time from the computer, like the settings, like most apps do. And that's annoying and that's really frustrating. And it just like chips away at goodwill.

And that's just like, I mean, I can't say that it's like an easy fix or something like that, but it's just that sort of low hanging fruit that you think we can address that and we could like, you And if a customer trusts that not only you have their best interest in mind, that you are listening and you want to create a great experience for them, whether there's a problem or not, just the fact of we care. If that is something that a customer experiences, like you said, they will be much more forgiving. If they see even those small iterative changes that are well-intentioned to make their experience better, they will trust you more. And it is something that cannot truly be quantified. Yes, we can see, okay, maybe we have a high NPS score and that relates to increase in revenue, but there's more to it. And it is a really difficult thing to track. And I think it's

really something that we need to believe in that especially leadership teams need to believe in the value of that emotional, that positive emotional response. People will always remember how you made them feel, not necessarily what you did or said. right? Yeah. And that's true. That feeling sits with us. And I think we can all think about a brand, a vendor, a business that we've had a good, positive emotional experience in our relating to them and a negative emotional experience. And which one do you want to do business with? It's as simple as that. And so it's always a tricky balance to make that argument. And I

think it really is more of a values driven thing more than anything that I see when I speak to companies that are really thinking about the customer's best interest. It's more out of the belief that a good customer experience is revenue generating. And I think most people agree with that in theory, but then when it actually comes down to what decisions are we making, that's where it can get tricky because we're also data-driven and we want to stay doing what the data says, but sometimes there's more to it. I don't know if you have any thoughts on that. I will agree 100% with everything you're saying, of course.

Yeah, like trying to figure out how to put a dollar sign or a dollar amount on the cost of frustration is hard. And I remember reading an article from Jared's school. I don't remember. It was probably five or six years ago where he was just talking through like the process of buying a ticket for you know a train or something like that and like all the poise where he was like ready to give up you know and what the cost of that ticket was like say he was just like you know what i'm gonna fly instead of take this train or something

like that and then if you're able to like multiply that. So for example, maybe you do have, you know, looking at your analytics, the amount of folks who just quit in a process, right? I mean, that's fairly easy to like quantify and figure out like, oh, they're getting frustrated by something, right? When you're using Salesforce to tackle your company's most important goals, failure is not an option. At Salesforce, they get it. They've made their most highly skilled advisors, Salesforce CTOs, available

to help you with expert guidance and implementation support at every step of your journey. Learn more about Salesforce CTOs at sfdc.co slash professional services. experience. And then on the flip side, think of a company, a brand that you've had a great experience with. What do you do? More often than not, people are like, I'm telling all my friends, I'm selling this business for them without any compensation, just purely because I think it's great. And that's where we... You're creating evangelists. Completely. That's where we unlock free marketing, free sales. We unlock long-term retention. And so the long game, if we really play to that trust, if we really play to how can we ensure that our customers trust us, that they're having a good positive experience, that they're happy using our product, what will that, where will that take us? Anyways, I could talk about this for a long time,

obviously. It's something that I teach to many organizations as a facilitator, but we'll put that aside for now. Something that I wanted to also chat with you about is the ethical considerations that we are now needing to implement into our workflows now that AI is able to do so much. And I can imagine in the world of Veritas where you're dealing with a lot of data, how are you really ensuring that you're upholding ethical standards as you're doing user research and how do you maintain trust when doing so? We, I mean, we don't have too many strategies in place, I would say, like from a high level. We really just have like one kind of umbrella strategy that is based on informed consent. So

basically, we're just making it really clear that our users understand that their data is being collected, and just really clearly explaining the purpose and the scope of data usage. Even when I'm doing, you know, a 30-minute user interview, like part of the script is always about like we're recording this. This is how we're going to use it. We're not going to use it for marketing or anything like that. So just making that like super duper clear that it's happening. In terms of other things, like I mentioned, we use FullStory, we're looking into using Pendo and some other things. What is really holding up the process for us in adopting a lot of AI tools out there is undergoing

the security review for them. So making sure that there are access controls in place for who can access it, who can access different portions of it, things like that. And then also as we move forward, continuing to do just regular audits to make sure that there aren't issues that are popping up. And especially in places, you know, we have a lot of customers in Europe where GDPR applies and a lot of other places have similar-ish laws. Nothing quite as strict as GDPR. So it's a big concern. And we don't have like a failsafe quite yet. And I don't know what that's going to look like. Right. And as far as the security reviews go, like obviously it's taking into consideration the user's private data and what we're doing with it. But the other piece

of it is, of course, you know, looking at what's happening with Delta, what's happening with the CrowdStrike issue. We don't want that happening either. Definitely not. Oh, my gosh. What a crazy situation to take place. What is, I don't know, we probably can't talk about it for much, but I'm like so curious about your take, like Delta had had like such brand loyalty for so long. Like what's going to happen because of this?

Totally. I mean, I am very Delta loyal myself and thankfully I was not flying. I literally, I know. Right. I mean, who doesn't love that lounge? Right. I'm flying later today. Got an upgrade business. I'm like, yes, Delta, I love you. But I have was the one airline that I'm seeing more and more everywhere about this. And I personally haven't looked into it too much. I do know as a frequent Delta user that their customer service can be kind of frustrating. I think their implementation of AI has not been perfect when it comes to their chatbots. And I was actually trying to change a flight recently and it was just glitchy. And I know the CrowdStrike thing was happening. So I'm like, I have a little bit of empathy, but I am assuming that there's something on the backend that was a sensitive point that just kind of got blown up by the CrowdStrike issue. So that's my assumption. That's my take on it. But I would love to talk to someone at Delta. Just shout out to anyone listening. Not just about this, but about Delta's customer experience

in general. Overall, I think it's great, but there's definitely, you know, I'm feeling for everyone who's working at Delta these days because whatever create better customer experiences, to build trust with their customers and all that fun stuff. And I do a lot of interviews and I'd love to get your take on what makes a good customer interview. What are the core components that you really try to hit when you are doing those interviews so that you're getting the right information that you can bring back to your team? Starting with really clear goals is, you know, the most obvious and probably the most important thing, I think. So knowing, you know, what is it that you want to gather feedback on and how are you going to do it? Right. And I think just being conscientious of time and having that level of intuition about what's happening with your customer. Right. So I'm thinking about interviews that I've been morning, we were looking at a system and the customer

said, you know, I have, you know, some experience with this, but a lot of my focus is really in this other area. And it really, you know, I'm, I'm not super happy with it. He wasn't that blunt about it, but it was definitely something that I picked up on. Exactly. And so at the end of the session, we had had we had like eight minutes left or something like that and normally nobody's mad to have like eight more minutes back in their day right but it just opened the door and said like i noticed that you know it seemed like you had more to say

about this particular aspect of the system do you want to go back to that can we talk about that a little bit more and it was great like he wound up sharing his screen and kind of like bended a little bit i have a former co-worker who she did a lot of customer experience type interactions it wasn't called that at that point um but she was kind of called in to like soothe the customers a little bit when the issues like that came up and so she would call it putting on her mink her mink mittens and just like you know petting them like a cat or something like that and that visual is like always stuck with me whenever i'm in situations like that like oh just put on the mittens and yeah soothes out the prickles yeah make them happy a little bit so just having someone to talk to makes a huge difference for customers in a lot of different venues. And

even, you know, like I can't promise that we're going to make changes immediately or something like that, but I can promise that I'm going to take this up the chain. I'm going to advocate for them. And if nothing else, it's fuel to the fire to make the changes that they're asking for. Right. Because if they're saying it, there's a chance there are a lot of people who aren't saying that. Right. A hundred percent. No, it's something that I, the mink mittens will forever stay in my mind now. I love that. I love that visual so much because it's true. I mean, one, it's like, it's building rapport when you're first starting a conversation with someone so that they feel like they can trust you. They understand why you're there, but really the light is shining on them and you're there to listen.

And listening completely. And like human nature, people like to talk about themselves just like plain and simply. And so you put them on their own little stage and you ask them questions about how they're feeling. And that intuition piece is so important of just picking up on, like you said, I feel like there was something more that you wanted to say about that. Like that's when you just kind of watch someone's body language, it's not as easy in virtual context, but it's not hard either. Like, I think we can still see a lot and just really watching and seeing how do people react when you ask different things? Do they light up or do they kind of shut down? And we can tell so much and we can read between those lines so much and then kind of double click on them and, and pull out those threads so that we can really, like you said,

bring things back to other teams, get it up the chain, have their voice heard. Um, yeah. And I also think like recording conversations, I am, this is one of the things that AI has helped me with so much is being able to record and transcribe these conversations. So I can go back to the product or the engineering team and be like, just watch these 30 seconds of this person talking about this thing. And that's where I think we can kind of, like we've said, data is really important. Research doesn't come without data, but if we can show an emotional experience, that can also act as data in us communicating to other teams and really get them understanding here's what's

happening to our customers as they're using this product. And that's a really important piece of why user research matters, right? Like AI, I know there's a lot of talk about how AI is going to affect like jobs and what jobs is going to be eliminated and things like that. But as far as research goes, I am not saying that it's not impossible. Nothing's ever impossible, right? Maybe highly improbable. Highly improbable, I feel maybe, that AI will be able to really pick up on connotations. And like when a user is experiencing dissonance, right? If their mouse is hovering over here,

and then they move it over here, and then they say something different than what they're obviously doing, right? There's just a lot of things like that that I don't know that AI will be able to pick those up per se. AI can give us trends that come out of transcripts, and it can tell us other things like that in terms of how it'll help or affect user research, I think. And it can give designers best examples your best tips and things like that. Totally. I mean, if anything, I think AI is actually going to help the function of research a lot and almost make it more important because it's less heavy as it used to be. Now we can

actually streamline this process and get to the juicy insights without spending months and months of consistent research. We can kind of speed up the process a little bit. My hope, my hope is that companies see that as an advantage and actually invest in research if they hadn't before, or if they hadn't been investing much before, because now they can actually get a lot more for their money. That's my hot take on it. Don't quote me, but that's the hope. So I always like to ask if you have any favorite resources, blogs, podcasts, tools that you really look to, to educate yourself about how to approach your work. I definitely like to read more than I like

to listen lately just because I work from home. I don't have like my drive time anymore, right? Yeah, totally. I'll listen to podcasts and things like when I'm working out or when I have like the bandwidth, right? Because that's the other piece that I spend my days listening to people and talking with people that I just like, I don't know, I just need to like chill out sometimes. yeah um I really love the people nerds newsletter if you're familiar with that that one's great I have heard of it before yeah yeah they're so good obviously like energy like I don't know Nielsen Norman are always so good right Jeff Soros stuff is always really relevant and always kind of has like something

I didn't think about before so even even though, I don't know, even though I might feel like you can only talk about like benchmarking in so many ways, there's always something new that I learned from him. And then I would say UX Matters is something I peruse a lot. And then Reddit. Yeah. I love Reddit. I, you know, I never understood Reddit for like a decade. And recently it clicked. I like had one use case and I would go in there and I was like, oh my gosh, this is like, it's just so many people and so many people willing to help each other. It's so amazing. Like right now I'm learning how to- It's an interesting community. Totally. I'm learning how to DJ and I'm very novice

and I really don't get it. It is very complex to all the DJs out there, like props to you. And I've been using Reddit to like teach me, okay, what is this button for? And how can I use this? And it's just really amazing. And so, yeah, to anyone who has thought

that the user experience of Reddit was just not exciting enough to spend time in there, which was my qualm, there's actually so much juice within there to squeeze and it's really worth spending some time in Reddit. And Reddit is an interesting case study in terms of like AI, right? Because like they're using i think all forums or i'm not sure like how they're limiting it to like help train like different ai models it's returning interesting responses right

because i mean can ai pick up on sarcasm which is like i don't know 90 in reddit see totally yeah i mean that's kind of concerning a little bit only because it's like, like there is a lot of accurate information around it, but it's also more than anything opinions. Jokey. Yeah. Or jokes. And it's not necessarily like validated information. So that's why we matter.

Yeah. That's why you need researchers to validate these things. Exactly. Exactly. Job security. Totally. So I'd love to hear about a recent experience that i'm interacting with a brand you know there's certain ones that do come to mind and it's usually the ones that i'm using the most and i know starbucks isn't the most popular company right now for a lot of reasons but i will be danged if they don't have like an excellent app for ordering your drinks and having every customization possible really available within it much to the consternation of their baristas i'm sure but i mean they just make it so easy to like turn on

your location you find a spot that's close you can send in your favorite orders your previous orders there's just so many ways to like slice and dice it it's interesting because like when you order typically with other companies, you don't really get to customize or you might put in a customization if you're ordering on DoorDash or something, but you don't really know if it's going to happen or not. I'd say more often than not, if I try to customize something, it doesn't really come out like I expected because someone maybe missed that little thing. But Starbucks is known for their customization. That's why so many people go to Starbucks. And so I actually think it's

really wise of them to make that so key to their app experience so that you know, when you go to Starbucks, you're still going to get the exact drink that you want. And it really touches on that personalization piece that we spoke about a little bit earlier. And it's something that I'm just talking to every single guest on the show about lately is that people want something that is personalized to them, whether it is the marketing that they're being sent or the drink that they're buying. And so I think Starbucks is definitely one that we can look to as a really good example of an app who's thought about, this is what our customer truly cares about when they come into our store. one that we can look to as a really good example of an app who's thought about,

this is what our customer truly cares about when they come into our store. And how do we create the same experience in a digital world? So props to Starbucks for that. And then my last question for you is, what is one piece of advice that every customer experience leader should hear? I've thought long and hard about how I could be eloquent and insightful and unique in my response to this, but I just kind of came coming back to the same principle. And I think it's really just kind of like an Occam's razor thing, right? Like the simplest thing is usually the best. And I would say that it's important to always listen to your customers and act on their feedback. Just plain and simple, right? Trust your customers and that what they're telling you is the best way to understand their needs, their pinpoints, their preferences, and that you're just going to be able

to create something that's that much more meaningful for them. And like we said before, you're developing that user evangelist sort of experience for them where they're able to share what they like about your product with somebody else and maybe persuade them to like also use the product or the next time that they're looking for a data protection service. Next time you're, you know, you're thinking about, I don't know where to buy your tires or something, they're going to follow that recommendation that you gave. Yep. Listen to your customers. It is literally as simple as that. Although actually going in and finding out what your customers have to say does take some effort, but it's well worth it.

I know that you, Megan, know that very, very well. And so to all of our listeners, go and speak to your customers, ask them some questions, understand what's going on for them. I promise you, you will not be disappointed. Well, Megan, it's been so wonderful having you on the show. Thank you so much for coming on. And I hope you have a wonderful day. We'll talk to you soon. You too. Thank you so much. You are a business leader with vision. You've seen the future as an AI enterprise thriving with Salesforce's agent force, and it is bright. Getting there? It's a little fuzzier. Don't worry. Salesforce CTOs are here

to work with you side by side and turn your agent force vision into a reality. We're talking expert guidance and implementation support from the best of the best. To learn more about Salesforce CTOs, visit sfdc.co slash professional services.