Using AI for marketing, with Clayton McLaughlin
Group: Business Schooled.
Daryl Pereira: Welcome, everybody. This is the Business Schooled podcast. My name's Daryl Pereira. I'm a senior brand and content strategist here at IBM, and this is the Business Schooled podcast, where we cover some of the material that you may not find in a textbook, some of what's going on in the business world as it's being currently redefined, and what's happening in terms of emerging trends. We're going to dive in, continuing on our series looking at business and AI. Really fortunate today to be joined by Clayton McLaughlin. Clayton, I'll turn it over to you and ask you just to do a quick intro. Let us know who you are and how you got to where you are today.
Clayton McLaughlin: I appreciate you having me on. I'm looking forward to the conversation. So as you mentioned, I'm Clayton McLaughlin. I'm now on my 18th year inside of the marketing ecosystem. I've spent that entire time inside of ad agencies, so I'm a lifer at this point. But very recently started independent consulting within the entire marketing ecosystem, focusing on brands, agencies, as well as the ad tech and MarTech side of the business, which is the fun part and the interesting part for me for sure. But throughout my career, I've been at giant agencies, the global holding companies. I've run small agencies and then medium- size agencies, but always working in digital media. Started in performance media, and that's progressed into broader tech and interests that continue to evolve, for sure. But I think that the core of where I came from in performance and search has definitely, has done me well, as I believe that all media now is performance media. That includes everything that we do within the digital world and offline. Again, that's progressed into starting to utilize AI, so I'm excited about the conversation.
Daryl Pereira: OH. Just to set the stage, especially for those that might be starting out in their career, in terms of what it means when you work in an agency and the kind of campaigns, are there any, just, maybe a couple of examples of some campaigns that you've worked on and the kind of work that you've done?
Clayton McLaughlin: Early in my career in search, we were working on General Motors. We were working on Walmart. We were working on Microsoft stores. And so the early, that was a lot of location- based work. It was still very heavily in- store. E- commerce was still pretty new, so a lot of what we were doing was just driving to physical locations and using the online world to drive into those, into store, and then started to be able to track some of that, which was an interesting time early in that career, as well. Then, again, I progressed and started to work on things like Dish Network, Reynolds Wrap. We did Fidelity. We did LVMH, and in that time, we started to focus a lot more on e- retail, as well. So that's when e- commerce was blowing up. Amazon started launching some of their products. And so we were really focused on, how do we drive online sales or online activities of different kind? Then, we're certainly working throughout the funnel as well, so building brands and those sort of pieces, but it was less big idea, splashy, and much more big idea with results sort of approach, which, again, I think that performance background certainly helped me in it. But yeah. I mean, I could throw a lot of brand names out there at this point, but all of them were with that idea of like, " Let's look at results." I think that's a big shift in marketing over the last 20 years for sure. Continues to hone in more and more on that performance, as well. So it's been a fun ride, but certainly looking forward to what the future has, as it seems to be progressing faster than some of us can even keep up.
Daryl Pereira: Glad that you can get to have your perspective given just the number of brands and the different industries that you've worked on and in terms of, also, like you say, just what you've seen in terms of how the space, the marketing space has evolved, especially in digital marketing and what that brings to the table. Then, let's just do, if we can, just talk a few minutes on this, this piece of every media is performance media. When we look at things like marketing, and you mentioned the funnel, where you start thinking, " Okay. If we're looking at maybe an ad on a page that somebody click on this... that's for a product, then takes you direct to a site, and you buy that product, that stuff can be measured." I think that's understandable. I know that I've worked on different sides of marketing during my career, and when you start getting into brand marketing and some of these areas where you say the bigger splashy stuff, where you're talking things, I don't know ... I know at some level, here in the US, you think about things like Super Bowl ads and everything down from that in terms of... or it could even be just large brand campaigns, just to talk through in terms of... I'll say I've seen, also, there's, definitely seems to be more pressure these days in terms of proving the results of every dollar that's being spent on marketing regardless. Some ways, it seems that it can be easier to track when you're at that point where you're much closer to sale, when you do start getting up into the brand space, and when you start looking at, how do you start thinking about performance and tracking performance where you may have some of these campaigns that you might be looking at brand- related?
Clayton McLaughlin: Well, I think the important thing is recognizing what you're trying to accomplish, so recognizing that big splashy ad, and I think of likes of digital out of home, CTV, or even digital TV now. They're just different metrics. You're trying to do something different, and so you need an entire organization to align around what the intent actually is with what you got into marketing. Then, from there, you can track everything. Right? Everything has numbers. Everything has something that you can capture, and I think that's what I think of when I say all media's performance media, in the sense that if I'm going to buy a giant YouTube front page for however many, or I'm ... even a Super Bowl ad, right, you still understand how many people have seen that. You still understand what that, overall, looks like, and then, in certain technologies, you can retarget. Digital at home, I think, is a great example of that, where you can actually use mobile devices to recognize where people are physically at, and were they exposed to an ad as it was displayed on a digital screen in whatever capacity they're at? You can retarget them. Right? So the entire ecosystem, at least from a paid advertising perspective, there's always something that you can track, and as long as everybody recognizes it. That may not be the perfect metric, but it's a metric, and you can align to those and understand that if there's a direction that we're all going in to understand these pieces, not everything is going to be that direct click to a direct sale that's perfectly linear and understandable and those sort of pieces. In fact, it's never that way, and it's more complicated than that. That's fine, but I think there's a sort of an 80/20 principle in the sense of... I think of the way that the military uses it. Right? If we're going to plan for something, as long as we're 80% there, that 20%, we'll figure out as we go. Because to use another quote, like the Mike Tyson thing, you're going to get punched in the mouth, and then your plan goes out the window. That's fine as long as you're prepared and ready for that, and I think that's the important part. So that's the way that I think of media in that general sense that all media's performance media. But it requires an alignment and a communication, and it requires an infrastructure for you to be able to track these pieces appropriately, too. So if you don't have those things, you either have to go back and start over and focus on those pieces, or you have to align and accept that we don't have everything that we need, but we have what we have, and we're going to go and move on those pieces. I think there's enough there for a lot of organizations to do that these days.
Daryl Pereira: That old Pareto principle, right, the 80/ 20 rule. Well, it shows up in so many parts of business, but like you say, it definitely comes into this world, as well, when we think about marketing and ad spend. We'll get into the AI discussion, but obviously, a lot of AI is predicated on what you've touched on here, I think, in terms of data. When you think about things like customer data, when you think about things like how businesses can use the data that they gain from, say, from running a marketing campaign or even just from running their business, how useful is that to a business, and how should it think about that and approach that?
Clayton McLaughlin: I think it's critical. I don't think it's just useful. I think it's critical, and I think there's a point of every business that's trying to create some differentiation. I think an entire businesses can do that based on their ability to collect and utilize data appropriately. That's a very broad sort of approach, but I think of like, you don't have to create an entirely new industry to be able to use data appropriately and to use as a differentiator. I mean, I know organizations and companies that have started. They're car washes, or they're dry cleaning. But they're able to collect data, utilize that not just for organizational efficiency purposes, right, operational efficiencies, which certainly is a big key to that. But for any business to grow, you have to find incremental new audiences, and that's what marketing is. If you're able to do that more effectively than your competitors because you have built that infrastructure, you understand how to collect data, you understand how to organize, cleanse data, you understand, then, how to pull insights and intelligence from that, you can literally disrupt an industry that is as long and as sort of boring as car washes or dry cleaning. And so I think that core of good data infrastructure is not only important. It's required. The organizations that we've seen that have disrupted the world in the last 20 years, Google, Amazon, Apple, they're all based on this data, and they all understand their ecosystem, those sort of pieces. It's just not as easy as we'd all like it to be or as simple as it sounds when you talk on the high level about those sort of pieces, because I think the application of that data, aside from the collection and all of those pieces that are incredibly important and incredibly difficult to do, but there's plenty of use cases of success in those worlds. The application of that data, I think that's the part that can truly differentiate a business, and there's loads of ways to go about that, too. I think we'll probably talk about those pieces, too.
Daryl Pereira: What would you say, then, when you think about the application of data. How can you find those nuggets that can really transform your business? Again, I know you can get caught in, what do you call it, analysis paralysis, right, where you can almost get carried away with data for its own purpose, but how do you keep yourself focused on business outcomes when you think about data?
Clayton McLaughlin: I mean, I already talked about this for the operational efficiency stuff, and that's a whole different discussion. Right? I'm a marketing guy, so from my view, my purview of applications of data, I think immediately just towards personalization. That term has been way overused. I laugh that I use these terms all the time, and then I pound people on LinkedIn for using them myself. But personalization has its own level of application, too, in the way that we talk about the customer journey, or the funnel, or the combination of the two, because nothing is linear, and it works in that funnel. But personalize every interaction and every touch point that a consumer has with your brand and your company. So what does your website look like? What does an email communication look like? What does the in- store experience look like? Down to even your customer support conversations, direct mail. What do you know about what you're delivering to their actual physical mailbox? Right? I think understanding how to personalize those pieces, because we are in a world now where that's an expectation. We're seeing consumers, and that's just not just younger generations, older generations, as well, that expect things to be personalized and, in fact, want that. And so I think that's the immediate application that I think of, and that's something that could be differentiating, for sure. But we talk about digital transformation all the time, and it's a trillion dollar industry. But if we think about the application of what digital transformation is supposed to bring to us, think about just an in- store experience where if I start a search on Google, I have no insight into inventory in a physical location close to me. I think one of the biggest examples of relative success when it comes to digital transformation, I think of Home Depot. I think it's used all the time, and in certain applications, yes, they're further ahead than a lot of organizations are. But I can't see live inventory at any point unless I'm on homedepot.com. Then, by the time I get to the store, it may or may not exist. But it drives me nuts, because every single product that goes in and out of those stores has a UPC code on it somehow or another. Why are we not utilizing the obvious scanning of UPC codes in any direction? Again, this is starting to talk a little bit more operational, but it has implications on marketing, as well. Because I think of a digital experience online. If I want to buy something, I want to know if the inventory is on the shelf in the nearest location to me, and that needs to be accurate. As a marketer on the buy side, I'm thinking, " I want to be able to turn ads off and on for specific products," and that includes everything from Google Search Ads to display, retargeting, video, CTV, whatever, wherever I'm going to have a product front and center, which is about everywhere. Why can I not connect that inventory to my ability to perform better from an ad- buying perspective? I just don't think enough of that exists. Right? Again, that's one example. You can go through a whole lot of those opportunities, but again, personalization and the ability to utilize this insane amount of data to create better experiences, because that's what it comes down to. I think that's the biggest application, is experience, and personalization, and how do you make sure that every touch point is one that is personalized for that individual? You'll see a lot more success both from the business and from marketing, for sure.
Clayton McLaughlin: Yeah. I mean, I think there's available, and what's the potential? The potential is the fun part. In the conversations that I've had and the application that I've used in AI, and this goes back a while. I was lucky enough, when I was at Havas, we were working with IBM Watson. This was as far back as 2013, 2015, somewhere along there. That's how old I am. Right? But we were talking about modeling for ad- buying behaviors and things along those lines. So how we applied AI then and now are obviously completely different, and that's changing. But ultimately, I think of it as sort of three categories. For me, there's prediction, there's automation, and then there's generation. So if we think of prediction first, I've used this term a lot. I think we're moving out of the big data era and into the big model era. What that means to me is historically, we've used data in a marketing perspective as a one- to- one application, meaning I have this customer and an ID that comes along with them, whatever it is that helps identify who this is or what they are. Then, I send that to an ad- buying platform, and then I send that to my website for ad personalization or experience personalization, whatever it might be. That's going out the window very quickly. Privacy concerns, we've already seen this in GDPR and then the myriad of individual states in the United States and what we're doing there, plus the cookie deprecation that's coming as soon as, potentially, January of next year courtesy of Google. That one- to- one application is going out the window. But what the data does for us, the big data and big models are still very much connected, because a model is dependent on the data that is given. Right? Shit in, shit out. And so the way that we look at this is the models itself. That's the fun part, because you can create a model for whatever you want the output to be. Again, whether that's ad buying, whether that's website personalization, that's ad personalization, that's email personalization, whatever it might be. And so I think that's sort of the predictive piece. How do I use past experience to be able to predict what's going to happen in the future? I laugh at like... When I'm trying to explain to my mom what I do for a living, and she talks about data, and she's scared about the data that everybody's collecting on her, I laugh. I'm like, " I actually don't care what your name is, or what your email address is, or physically, where you live. There's applications for those, but ultimately, I'm more concerned about your behavior." Everybody thinks that they're unique and special, but realistically, you've got millions of other people that do a lot of things just like you. And so if I can predict what your next step is, I can provide you personalization without ever knowing who you are and never needing to know who you are. And so I think there's the predictive behaviors and those sort of pieces, and a lot of that has been done for a while. It's just continuing to evolve. Right? We've had ad buy models and those sort of things for a long time. Automation is just economics of scale. So the way that I think of in that sense is there's the obvious automation of just projects you don't want to do. I wrote a script courtesy of AI and ChatGPT recently for Google that automated, " There's an invoice that needs to be sent out. It's an email reminder that's due tomorrow. It hasn't been paid," whatever it might be. I never would've been able to do that on my own, because I can't script. I have no idea how to do that stuff. But ChatGPT wrote that for me, and it worked. Now, I'm applying more and more of those sort of instances. But think of it in a broader marketing sense, that when you're doing big marketing, advertising strategy, you're deciding to start with, where does my budget go throughout a year? Historically, we did that every 12 months. You did that once a year, whether it was fiscal calendar, and you just applied, " Here's where I'm going to distribute the budget." You just, throughout the year, just kind of see how things go, and most of the reaction optimization was done on a keyboard level or an individual channel level. It wasn't done on a much broader strategy sense. In some cases, bigger organizations can pay to have media mix modeling, those kind of things, but that's still not done more than six or 12 months. So I think there's room for us to improve on the big strategy piece, and I call it proactive reactivity, is, how do you build an infrastructure and prepare for big changes, not just small ones? So if I understand that in the marketplace, I've maybe over- invested in brand dollars, that the saturation's really, really high for brand recognition of those sort of pieces, how do I now take advantage of that? So in the middle of the year, instead of continuing to plow money into a brand, I've already reached the threshold that I want to be at, and now, I'm making reactive changes to that. I'm pushing budget down into the low funnel to take advantage of that building that I've already done. I think the intelligence of that, the data collection, the insights, the ability to automate a lot of those pieces at a very broad scale, that can be reliant on AI and automation, as well. Then, the third piece is generation, which is something that we're all kind of familiar with, the DALL·Es of the world, the Midjourneys, whatever it might be. There's some other organizations that are doing some really cool things with the output of... You take customer data, and now I have, let's say, access to a digital twin of my marketing persona or my actual customer. So as opposed to having to go do focus groups and rely on that information, it's a small collection. Now, I've got sort of just synthetic data that has given me access to and a ChatGPT- esque experience to my customers, potentially to my competitor's customers, and to any other cohort that I want to create. So think of all the data that you sit on as an organization. That's everything from sales data to customer support conversations, interactions on a daily basis, et cetera. You can pull all of that in and start to create unique outputs for those pieces, and I think that customer research is a great example of that in addition to creating ad copy, creating ad content, or directing a thousand different potential creative for a single idea, whatever it might be, just the things that we couldn't do manually ourselves. So I think those are kind of the three areas, right, the prediction, automation, generation. At least, that's the use cases as I see them right now. We'll see what that looks like in three months, it feels like.
Daryl Pereira: Love that model, and I know you say three months. It feels like that could last for a lot longer in terms of familiar, as you mentioned, the work that you're doing with Havas back with IBM Watson back in 2015. I also have been around for a little while, and yes. We inaudible had working around the Watson product line at that time. Just like what you say there in terms of obviously, at the moment, with AI, there's a massive focus on generation, and some of that makes sense. Yeah. We can do things that we've never been able to do before. But then, the way that you set this up in terms of having a framework in which that sits within a context at which we've also got the predictive elements to it, that we've also got these other pieces in terms of the automation, that idea that you said that in terms of if you can notice that, " Okay. We're doing marketing. We're building demand, and then, hopefully, that demand turns into something that we can then turn into actual sales and revenue," the way in which you could say that you might automate that, where you start realizing, " Okay. If we're getting to the point that we're getting some, a certain amount of... We've got all these folks interested. Now, let's think about moving them down and getting them further down." The fact that in the past, big organizations would have to do that stuff based on quarterly reporting or maybe monthly reporting, and you'd have to try and turn the team and get them to think about, " No. Hey, brand marketers, now go think about this aspect." In terms of what it means, especially the strategic aspect, this goes way beyond, rarely speaking, just the use of AI just, for instance, to create, say, ads that we haven't been able or to do the kind of ads that we would typically turn to either illustrators or designers and those folks. I think the thing that stands out from what you're saying is this goes way beyond that. This is quite fundamental to marketing, especially at that strategic level. I know, had a little bit of experience also doing things like persona development for... We did it for a non- profit startup that just, for certain reasons, we weren't able to have good access to the customer base. And so just the fact that some of these AI tools are effectively... can slice and dice, basically, the public internet as it's built up over the last 20 years, those personas exist and are there. It's able to bring out, and it was able to, in this case, it was able to bring out personas that when we took them to the stakeholder and to the leader of that organization, they were blown away with how accurate they were. Meanwhile, it saved us having to do any of that work. I'd say, honestly, sometimes, it feels like in marketing, when we can't do that work, we cut corners, and cutting those kind of corners around marketing strategy and about trying to understand your customer feels like that only ever hurts you. Because then what happens is your ads either look like everybody else's or you just don't get the engagement.
Clayton McLaughlin: Yeah. Well, I think there's big organizations, small organization advantages of AI in that sense. Right? I mean, there's very large organizations, in the CPG world especially, that have done so much customer work. If you can take years and years worth of that and ingest that into a specific model with the intent of this output, and then you layer on these other available data sources that have just never been part of that process before, what's the cool thing that comes out of that? That's where you can start getting these really unique sort of outputs, and maybe those little nuggets, it's like, " Oh. I haven't thought of that one before. Let's dig into that. Let's apply that and see what happens." I think that's unique for big organizations. For small organizations, maybe they just never had the ability to do that kind of research before, and now it's at their fingertips regardless of how much data they have. Because if you think of just the publicly- available data, like you said, think of Amazon reviews, Walmart reviews, Target reviews, even if you're a brand new product, say I'm creating new headphones because mine went out before we started this. Right? I'm an entry level or a new entry into the market from a headphone perspective. There's millions of reviews, and I can figure out what people like, what they didn't like, and ingest it, and utilize that information to not just make a better product, but market it better, too, in that sense. So just loads of advantages, and obviously, as the cost goes down for AI, it's only going to get more fun, I think.
Daryl Pereira: On that note, in terms of where this is heading, I'd venture to say that I've seen, I think, other organizations, I think Notch is one, in the content space that had recently put out some research that was suggesting that if you took, say, for instance, landing pages, digital landing pages for banks around the US, you scrubbed out the top bar, and you just put them all together, really difficult. So you couldn't really tell which bank was which. There was so much... In some ways, you could argue that as much as we like to think of marketing as a creative field, there is a certain amount of templatization that's happened over the past. In some ways, it's led to this model. Do you think there's opportunities here where... What does AI bring to the table in terms of potentially raising the bar?
Clayton McLaughlin: In that example, specifically, the sort of generative AI, I think the value to me is that's a complement to an existing set of skills that exist for humans. Right? Think of a creative director or a content producer, whatever it might be. What generally happens in those sort of brainstorms is you get a whole bunch of people together, and you come up with six great ideas. Then, you have to make a variation of those ideas, and that takes weeks, and weeks, and weeks. You got to pitch it and get the feedback from the client, and it's, one, it's expensive. One, it's timely, or two, it's timely, I suppose. And so what if I could get thousands of ideas and start to hone in on the ones that make a little bit more sense? What that allows you to be able to do is, one, you're going to get mindset, or because no matter what we do as humans, we're going to come in with our past experience that's going to influence what we're trying to do right now. So to your point, if I'm developing a landing page, I know what's worked in the past, and I know what best practices are. So it's really hard for me to think about something completely different and unique. I think that's incredibly important, particularly in super saturated categories and markets. Right? So you're going to get a whole bunch of these new unique ideas I might not ever have thought of before, and then that gets the juices flowing. It gives you those directions. I think of, from a brand differentiation standpoint, I've seen AI, interestingly enough... There's predictive AI for content performance, and there's, " Hey. I'm in this category. I'm in beauty category. I'm going to be on Facebook. I want to run it during Valentine's Day. What should my ad look like?" The AI is going to go look and say, " This is what, historically, it's looked like, so this is what you should do moving forward." The problem with that, again, is that now you're going to look a lot like everybody else does, because it was best practices. I think the important thing for brands is at some point, you're going to have to think differently than everybody else. AI can help you do that, but I think of an example in my experience. I didn't work on the brand, but I know the guy that ran this whole thing, Old Spice. It's a deodorant category. Everybody talked about smelling like an Irish spring, and everything you saw was the same. Right? It was just a color difference. Were you red like Old Spice or green like Irish Spring or whatever, right, Dove... It all looked the same, and then Old Spice started doing these crazy- ass commercials with people, minotaurs and all of this stuff. All of a sudden, Old Spice sales went through the roof, and it had nothing to do with anything other than they decided to be dramatically different in a category that was overly saturated with all the same stuff. Right? So that's, I think, a good example of where AI may or may not have been able to be there, but it was going to be a compliment. Right? If we think of that scenario moving forward, if you recognize, as a brand, everything looks the same, and I have to differentiate myself, then AI can be used as, " Okay. Give me a thousand ideas. How do we go wild with this?" You're going to get angles, and ideas, and concepts that you never would've been able to do yourself, even if you have 12 of the greatest creative directors in the world sitting in a room, which, by the way, is going to cost you hundreds of thousands of dollars to do, versus pump something in into ChatGPT and get some ideas. Then, you go do your process from there. I just think there's a lot of value in that, as AI or any automation tools in that sense, as a compliment to what we already know and do. I think that's an important piece of how you utilize it, at least for now.
Daryl Pereira: Interesting thing, what you say there, is this idea. In some respects, though, it's the way we use these tools and that idea of pushing the boundaries and having that desire to, " Well, how do I break the mold? How do I stand out?" It feels like in some ways, I know that's a core principle of marketing, right, differentiation and standing out. That doesn't feel like it's going to go away. What it does happen is that how you get to differentiation when it's human plus AI, I think, will be one of the areas that sort of will be interesting to see how that unfolds.
Clayton McLaughlin: Good AI usage, I think, or any good tool usage is about understanding, first and foremost, what your core problem is. You have to start there, and if you're just expecting a solution to create something for you, then it's, again, shit in, shit out. So if you can think critically about your business or the unique problem that you have, then there's tools that help you get there. Again, that's not just AI- based, and that's any sort of problem that you have. But if we're thinking that AI is, one, going to take our jobs or, two, is just going to be the end all, be all solution, then you're wrong about those scenarios, at least in my career.
Daryl Pereira: On a related no, what advice would you have if there's one piece of advice you would give to either students or young professionals that are just getting started, especially in the marketing space and with digital, with what's happening around AI? What piece of advice would you have for them?
Clayton McLaughlin: First is just never stop learning. I think that's something that I've always been super involved in, MarTech, and AdTech, and just, what's the next thing, and how do I apply something new? Again, I was trying to think critically about the problems that we had as a business, and if we didn't have a solution for that currently within the stable of whatever we had, then who did have that solution, or what was that solution? What did those pieces look like? So you have to continue to look outward. You have to understand what else is influencing you, and your business, and whatever the organization might be that you're involved with, so whatever capacity you're at. But you have to keep learning. You have to understand those pieces. I think another thing is consistency, showing up every single day. You're going to have good days. You're going to have bad days. You're going to have days where you understand stuff, and you're going to have days that you don't. But as long as you continue to show up and provide the effort, because that's the only thing that you can really control, right, especially in a world where things are changing. I mean, I think of digital media when I started in the early 2000s. Google was this new kind of thing, but there was Yahoo and AOL and these behemoths that already existed. The only thing that I could do every single day was come in and apply myself in those worlds, learn about what was happening, and follow the evolution, and try and stay on top of those pieces. Think we've been pretty successful in doing that. So I think there's a lot that you can do just by showing up every day and giving the effort, because that's what you can control. The rest of the world will come to you. The last thing I'd say is sometimes it's the easy things, and what I mean by that is there's that consistency of every single day, sometimes, it's the small things that make big differences. If you think of innovation, it doesn't always mean that you created the next thing in ChatGPT. In some cases, it's, how do you apply that a little bit easier, or how do you improve a process, or, you know. So don't be afraid to speak up with what you're learning and what your experience is, because your experience is always unique. You're always going to have a different perspective than somebody else. And so take stock in that, value that, and sort of bring those things to the table. I think you'll be okay. But it's a weird world right now, but it's a lot of fun. I'll put it that way.
Daryl Pereira: Nice. I love that. A lot of that resonates personally, I'll say. So I really appreciate that and would echo all those sentiments, and I'd love to continue this conversation. The time has just evaporated. We are effectively out of time here, so all I've got left to do, really, is to say thank you, Clayton. Clayton McLaughlin, you can check him out and find him on LinkedIn. Connect. Keep up with the conversation, what's going on, latest in the MarTech, AdTech world, how AI is transforming the way in which brands can build customer experiences. Watch this space is probably one of the best things to do, is to, again, keep learning. Keep learning, as Clayton said. Thank you all for listening. This has been The Business Schooled podcast. My name's Daryl Pereira, and subscribe to get more episodes dealing with some of the emerging trends around business. Thank you all.
In this podcast episode, digital media and ad agency veteran Clayton McLaughlin discusses how data and artificial intelligence (AI) are transforming marketing, focusing on areas like performance media, personalization, and content generation. He explains how businesses both large and small can leverage data infrastructure and AI tools to gain strategic advantages and create differentiated customer experiences. Clayton also provides advice for students and young professionals.
Your host: Daryl Pereira, IBM Senior Content Strategist