#1: Dan Morrill on the Content Supply Chain, B2B Buying Frameworks, and First-Party Data Competitive Advantages

Dan, we are so grateful to have you as our first guest on our podcast. One of the reasons we're especially excited to talk to you is because of both one, your role as a B2B buyer as a head of marketing technology at LinkedIn, and then two, your expertise as a marketing manager, you know, managing huge budgets of ad spend to reach prospective buyers. So I would love to hear from you if you could share a little bit about your role as a marketing technology leader. - Yeah, great, thanks for having me. So I think at the highest level, if you ask what my job is at LinkedIn, it's really to enable marketing to do its best work. And the way I think about that is we have a number of different tools, technologies across our internal ecosystem that's going to enable and empower our marketing teams to launch, go to market campaigns across a number of different channels. So the quick way that I think about my role, my team's role is we organize it almost by the stage of what happens around campaign creation. So we have a team that's dedicated first and foremost within LinkedIn, everything really starts with our data. It's reviewed as our first party competitive advantage. And so we make sure that we are making investments there and having the most robust stable and honestly innovative targeting ecosystem possible. So, you know, a lot of companies refer to this as your CDP or customer data platform. Internally within LinkedIn, it's something that we've built up and is really critical to really any key go-to-market campaign where we're able to look across our entire member dataset and target based on a number of different dimensions, whether that's demographic, psychographic, or even if we get into a predictive type of behavior. From there, we take that data and then we want to activate it across a number of different channels. And so the team governs and oversees tools or technology that covers things like email channel, we use our own internal advertising platform, which we have cleverly named LinkedIn on LinkedIn or LOL. And then of course, paid media, huge, huge channel for us. So in addition to being our own customer zero or launching campaigns across networks and working closely with partners like Google, Meta, et cetera. From there, that data has been targeted, it's been activated across all these channels. We've got a bunch of clicks and traffic and excited customers and members. We're directing that traffic either into our flagship, linkedin.com, to identify the right product or service that our potential customers or existing customers are looking for or into our own marketing web ecosystem. So we have a team that's dedicated on building out that to really try to give the right information at the right time that's going to match with the types of messages that we're sending and to also continue that first party competitive advantage of LinkedIn's data and really deliver an experience on the web that really only LinkedIn can based on the things that we know about our members, our customers, our company, their industry. And depending on, we are a very complex and fun ecosystem where we are not just B to C, but we're B to B or some folks like the framing as B to C to B. So for our B to B side of the house and enterprise, we have a pretty robust lead management technology engine that is taking any foreign fills that we have, enriching that data, making sure that we're identifying what's the right potential solution for our members, for our customers, for our prospects and routing that in close partnership with our sales team. So we do that, we do it a couple of thousand times, a quarter, tens of thousand times a year. And yeah, it's fun and continually evolving in an interesting landscape. - So Dan, we've often been thinking about buyers and the buyer's journey. And you alluded to it earlier, but when you think about yourself as managing, you know, your own large budget, how would you describe your role as a buyer and how you go through the buyer's journey process? - Yeah, so I think it's gonna depend on the channel and the ecosystem. So I would say, first and foremost, a lot of it's gonna start with looking at our internal data and what it's telling us. And especially with the way that the industry and the landscape is shifting and changing with a really hyper focus on data protection and compliance, particularly in the EU, as well as things like the upcoming deprecation for Chrome and Apple's, you know, really ushering into this, you know, really more user focused compliance standpoint, it's reinforced the need to have a really robust internal first party data set. So really that starts from our end, where we'll look and start first in what is our data telling us, where are the opportunities, be it by vertical region product, and using that to then guide what we're looking to compliment that we go out to make purchases on outside networks. So no, it's really, I think a combination of that sort of gives us that set direction. And then when we go into those different environments, it's how do we continue that extension and identify where are some of the audience or where are some of those gaps that we can fill? You know, with all the different features of functionality with first party data, you know, you could say at a, you know, really greater level, hey, if I can know that I can advertise and target and bid just on the audience that I want to, phenomenal. Like I'm willing to bid what I want, I'm willing to focus there. But we know in the realities of that, like it's never going to be that, that strict of that one to one. So it's really about taking that, establishing that core, identifying where you can get that first party data, ahead of advantage, extended understanding how you want to tailor your strategy and bidding to that segment. Then looking when you want to expand, identifying, okay, are there lookalike audiences? Are there folks that obviously that aren't going to match one to one? How do we then expand that scope and really look to, you know, those different platforms targeting capabilities to provide really crisp, clear, concise details of information to make that decision ideally as seamless as possible. Once that's then set an activated importance of continuous feedback and continuous optimization, again, it's going to vary by different channel, by different strategy, by different product, something like if we're selling an online product or something that's more of a direct B to C transaction, you're obviously going to want to have a more iterative and as close to real time, near real time, optimization capabilities. Whereas the longer buying cycle, the more enterprise products, recognize these, of course, like search, where it's going to take you that time to build up that intelligence, to build up those optimizations, you're going to have patience by default. At the same time, you're going to want to understand when it is time to make those decisions, report back, whether it's back to your line of business, whether it's back to your CMO, or even as, almost as a part as a CMO, the CFO, what the returns are seeing, you want to make sure that those insights and those things are going to be easily identified so you can continue to make the proper optimizations allocations. - A quick follow-up to one of the points you mentioned around starting with the data you have and really learning from it, is that the act of actually making a purchase as a buyer happens along this journey of learning and researching. And whether or not you feel that to be true or slightly reframed. - Yeah, I would say that's, I say it again, like depending, it's going to depend on the product or the campaign that you're running, but I would think that that's, yeah, that absolutely runs out more often than not, that as you're going through and identifying this, you're one, looking at the information that you have at your disposal to make those different investments or those different decisions. And the more granular and the more clear, like insights and confirmation, you can have to build more conviction over that strategy that purchase, like is going to continually like reinforce that, enforce that decision and reinforce that investment. - Dan, do you purchase marketing technology as well? Is that a big part of your role? - Yeah, yeah, absolutely. - If we were to take you as like a buyer, could you just step us through, like not as the marketer, but as the buyer, like some big purchase that you've made and how, what was your journey? - Gotcha, okay. - Because I think like a lot of this is like, a lot of what at least I'm trying to learn is what is the buyer's journey today and like what will that look like in the future, especially with this disruptive AI technology and like taking like the marketer hat off and then the buyer. - Thank you, yeah, yeah, yeah, okay, perfect. Yes, so absolutely. It is a very intensive process, but I think there's some common, I'm gonna approach it as a common frameworks that we apply when we're looking at it. So I've got a lot of guiding principles or a lot of strong opinions strongly held around this. So first and foremost, like the number one thing when we're going in and making a decision on getting a new product, getting a new feature within our go-to-market tech stack is, okay, is it build versus buy? The first thing I'm gonna point out is, there's a word in that that gives me an allergic reaction and that's verse, right? Any good go-to-market technology stack and any good purchase ecosystem that you're gonna have, it's not build versus buy, it's build and buy because when you make that purchase, even though when you buy something, whether it's a technology from Adobe or Oracle, you're going to need to build it. You're gonna have to build the integrations, you're gonna have to build the infrastructure, you're gonna have to build the processes around it. And there's still gonna be a dependency and a need to work closely with internal engineering or depending on your organization, IT teams. And so I think that's a common misconception that really gets at the really most important stage in that ideation and identification stage to really set that grounding of, hey, this is gonna be, whether we choose to purchase something or whether we choose to build it, we need to make sure that it's gonna work within our ecosystem and have integration. And even if we are making a purchase and buying something, it doesn't come off the shelf with out-of-the-box features and it's like plug and play and like push or go, there's still an intensive, there's still an intensive investment there. And so when we're looking at this, we have a common framework, we're asking a consistent set of questions. First and foremost is, is this problem unique to insert your company? Is this problem unique to LinkedIn? And has the industry solve this at large? This really helps us identify where there are, there's a lot of intensive investment, let's use like a content management solution or CMS as an example, it's not common to LinkedIn and the industry is absolutely, by opinion, solved it at large. So that's helped us sort of think through like, okay, do we really want to get, if we're going to build something, do we want to get into this, to the CMS business, so to speak. It comes clear to us that yes, we are, no, we don't want to and yes, we want to go down a buying path. Next element too is, in my previous roles or in previous time, you jump into the state of like, okay, it's time for RFP, Request for Proposal, right? You know your Gartner, you know your, for a box like let's go out and let's like, get them in and start identifying, you know, who's how we can get, you know, the most company friendly in the right or product, as well as, you know, the right fit for us. We've shifted that to like more of an RFI, really honing and focused there, Request for Information. We really want to go and expand out and understand that like, yes, there's going to be the key players that, you know, have a pretty large share, but we also want to understand what are some of these other, what are the other companies, what are the other offerings and by doing that process and getting that set of information back and flipping the conversation and the objective a little bit with the providers, you begin to unpack more like, where do they land on the sense of, what their existing offerings as well as their roadmap? How do they think about innovation? How do they think about craftsmanship? How do they think about sort of the roadmap in the future? And so that's really helped us both refine and confirm what's the right selection for us from a vendor. And it's also helped influence and think about how we develop our own internal roadmaps and features and functionality and things that we hear from a company that maybe it doesn't feel like the right thing to purchase or buy from them. Maybe it's a feature or capability on there. We've seen that in turn come back and influence the types of things that we potentially build internally. And then what it really comes down to the, you know, selection and the key criteria for us at LinkedIn, you know, first and foremost, it's going to be that can they meet the really high and intentionally high standards of trust and compliance at LinkedIn. And so we want to ensure that they are not only able to meet the clear, the high bar we set, whether it's industry standards and things of certification around ISO or SOC2, but also to what are the SLAs that they offer, you know, we never want to envision or see an issue or problem, but like how, how do we have a lot of confidence and conviction in the offering or the product that they have that they'll be able to meet that high standard. And then, you know, next to that is really around scale and, you know, given our, you know, data set over a billion members, the complexity of B2C and B2B, as well as the types of personalization, automation that we look to do across our go-to-market motions, being able to do that, not just, you know, a couple of hundred times a week, but thousands and thousands and thousands of times compiled against the really set or large data set, scale becomes a really, really, really key sticking point for us. - That was amazing. I really appreciate you going deeper there. If you were to think about how your buyer's journey, how buyer's journeys are going to be changing over the next three to five years with the emergence of GAI and, you know, the technologies, like how do you think that is going to be changed? What's, I guess, maybe what's going to stay the same change? And then what do you think will change over, say, five-year time horizon, especially, you know, with these emerging technologies? - Yeah, I don't know what's going to stay the same. Then, and it's already seen like what's changed already is one, during that RFI, RFP process, I've really, you know, made sure that buyers know coming in that really do your homework and ask a lot of questions of us because we want to make the time useful in coming in. Because with GAI, I can think back to a couple really, you know, funny, funny exchanges where we were going through and getting an overview of a product of the company and we're about 10 or 15 minutes in, and they're kind of like dancing around and giving sort of the setup. And I went to GPT, I wrote the prompt, explain this to me as if, you know, I'm getting this pitch and give it to me in a TLDR in five bullets. And I got it and I paused the meeting and I pasted it in the chat and I'm like, hey, is this accurate? 'Cause this is like where there's two or three bullets in here we want to get to. And it got a good laugh, it got a good chuckle and it actually like pivoted the meeting a bit, but it really kind of struck home with me that like, A, the amount of information you're able to attain up throughout a company, I think it's really going to need that if you're selling a product to really just cut to the chase. We understand that there's a relationship element and there's a setup, but being able to really distill down to your homework on the customer that you're pitching proposing to. One, you know, really cut to the value statement in what this product offering is going to deliver. And two, what it means for that line of business. Like I actually, if I'm spending more time in a pre-call or filling out some center information, it's going to make the time that we come together during that, this process, like I would air really, you know, I like to see companies doing more of that 'cause, you know, there's enough information that can go and capture publicly around, you know, what, you know, your potential customers looking to go and do, but really, you know, spending that time doing that homework and tying back to, you know, those, the value offering and the benefits that the product's going to deliver and cutting down to that. I think what's, you know, moving forward and you know, down kind of that buying process of that buying journey. I'm already seeing it now with Gen AI, like the need for compliance, trust, security is going to only increase and really having confidence and conviction in that this company, this product is going to not just meet but compliment our own guiding principles and across, you know, LinkedIn where we have some pretty standardized ones and one specific to MNC around, you know, we're never going to look for this to be a full replacement, right, as an assistant, not an associate. When we think about content creation, us needs to aid and accelerate and optimize. It's not going to replace, right, creating content from scratch, images from scratch from that end. We're really looking to make sure that any of those offerings are going to meet those. And it's going to have, you know, ideally none, no unintended bias around it and the types of, if we're asking it to create variations of content or copy variations that those things are going to as closely to our guiding principles as well as our cultural values internally. And then I think that then if we're thinking about the last piece of once you've done a good job and pitching and going through and making that purchase and you've brought on new customers or you purchased a new product, I think the speed and the pace at which things are innovating and changing is pretty phenomenal and incredible. And I actually just had this conversation this morning where I won't name the longstanding vendor with us offering different types of AI capabilities or functionality with what we felt, you know, was a pretty aggressive price tag to it. This was, you know, let's say six weeks back to where I fast forward to today with the release of different, you know, like free open source uses. It's like, why would we pay for this? Versus where you can really, you can attain it and have it essentially for free. And well, essentially for free and nothing's free, but having that ability to go in. So I think just making sure that as, you know, it's a really difficult development ecosystem to keep up with. But if you're going to put something out there, if you're going to put a price tag on it, you better have conviction that this thing is going to want to add value, add benefit of your customers. And two, if it changes, you need to change with it and change fast. And, you know, to see this sort of things that went up publicly and them to have a follow-up to us on like, hey, if you know any further consideration or you're looking to make this purchase, we're the ones kind of pointing to the things that have changed in the ecosystem, saying that like, there's absolutely no use for this. So I think that's just going to be a challenge for every company that has types of data offerings on top of it. And really, I think the transparency conviction and the ability to quickly respond to it is going to be really, really, really critical. Else you're potentially erode maybe some well-established and solid trust that this really has. And so it's potentially altering the other parts of the relationship that don't have a tendency or need. - Dan, you basically are going exactly where we're hoping the conversation would go, which is if you can put your sort of B2B marketer hat on for a second. One topic that I think I'm particularly fascinated about is where we sort of see B2B marketing technology headed in the next three or five years. You mentioned a few use cases around content generation and the like, but I'm wondering, especially given how quickly things are unfolding, where do you see some of the sort of coolest innovations coming from? - Yes, I think it really revolves around what's a steal a company's name but it's framing around it, but it's kind of becoming industry standard now. Is this notion of the content supply chain? So maybe what I'll do is I'll present the problem or the, you know, try to frame it as the job to be done. So when you work within a go-to-market function, particularly if you're doing across multiple lines of business, which if you're doing multiple lines of business, you've got multiple strategies, which means you have multiple creatives, we've got multiple messages, right? And the last thing that you want to happen is your org chart to show up in your members' screens, whether it's their inbox or whether it's their TVs or whether it's their site, right? Fragmented value messaging, et cetera. And so when you look at the diversity of types of content that you can create out there, or that are needed for, let's say, multi-channel or omnichannel campaign, you're having an email creative, you have website, you have paid media, you have copy across all those, and then if you're really good multi-channel, connect to TV, et cetera. And so what I've seen is like really one of the biggest challenges historically, and this is very much true for LinkedIn, is how are we identifying and tracking that sort of content supply chain from ideation creation, whether it be from your internal agency or outside agency. And that's the other wrinkle to this. You've got a lot of different teams of people's and function building underneath a common goal objective and a common brand. And there you then need to format that content, activate across a number of different channels. And then by the way, okay, now the other challenge is measure this for me and tell me what's the most effective. So there are so many different points around that assembly line where things break down or honestly have never been connected altogether. And so what, there's been the high level architecture solve around this and different products that have promised to address this, right? You have digital asset management or dams. You have content management systems, right? That then activate and publish that content. You've got the channel delivery, you've got analytics, you've got all these different companies that are providing these like, we're going to give you every multi-touch attribution and every stage of the customer journey insight. But at the core of it, regardless, you can bring on those great pieces of products or technology that can absolutely offer, design what they can deliver, what they promise. If you don't have that supply chain, if you don't have that process down end to end, it's going to break down and fail. And so what's been interesting as we've continued to address this and spent time on it, particularly over the last six months after seeing the explosion of LLMs and GenAI, is a lot of these types of everything down to content creation and content like taxonomy, that in the past was a really strenuous, toil-ridden, redundant task. You're looking at like, oh, wait a minute, like there's actually the ability to like, if we're using models, if we're using different automated processes to create this, we can also use that to add a clean and clear taxonomy. If you begin to solve that and identify that at those various different stages, now as you move along that different activation chain or that supply chain and you push those things out, we're beginning to see like, oh, wait a minute, like this isn't going to take five years and 50 people, full time just in developing this taxonomy and these processes around it, we can actually accelerate this. And what we thought would take us five weeks can actually, we can do it in five minutes if we've set up and write the right process, the right creation and the right sort of hagging and taxonomy. So it's really exciting and we've already like, one, we're approaching it in two ways, right? We're thinking about long-term vision of like, okay, now let's start thinking about this ecosystem and what this looks like end to end or identifying what are those different products that we need to plug the different gaps that we have today, just because it historically hasn't been an area we invested in. And then two, short-term execution. We're building and learning and iterating and we're using GenAI to automate hundreds, thousands of content pages, literally in matters of minutes that used to take hours, right? And seeing that like, hey, we can do this and we can actually build this and scale it. And if we can do it in this controlled experiment, proof of concept, from creation to tagging to tracking to then optimization, it's giving us a lot of conviction and excitement to say, okay, we can actually address or solve this up to end. And so I think that's just for if you're thinking of a beauty marketer or any marketer, honestly, like that to me feels like the really horizontal piece to like whether you're a small startup company and you're really just honed in and focused on, you know, your budgets allocated to SEM and email, regardless, you're gonna have a need on content creation, content acceleration, content optimization and having that clear tagging throughout that process and being able to automate and use, yeah, whether it's AIML, GenAI, I helped do this at scale, I think is where in my opinion, you're gonna see a lot of the explosion of the, you know, evolution and transformation. And on the other flip side, like it has to happen or it really needs to happen because on that note, when I mentioned, you know, before the ability of us to create more of this problem, you know, pages so easily and so effortlessly as we have in the past, it's only gonna exacerbate that problem before if you don't have a really end-to-end clear solution. - That's fascinating, it sounds like it's gonna just drive tremendous productivity and efficiency. We had, like if we had, you know, 10X the number of people, like we own content creators or, you know, all of that, we would have been, now we would be able to build all this extra content, we'd be able to measure it more effectively. I love that content supply chain idea. And that was you wearing that marketer hat. And then before you were wearing the buyer hat and you were kind of talking about using GPT to like summarize or synthesize or just give me my top three. I'm wondering, is there something else that is like beyond productivity? Like is this going to enable some net new innovation? Like it's like, yes, I get five people to do this. - Yeah, but that's almost like somewhat incremental on this is the way we've done it. And now we're doing what we've done, but like more efficiently and like 5X more than we would have done. Is there some leap that you could imagine the future? Maybe even wearing both your hats now, like wearing your buyer hat and what you're expecting as a wanting, as a buyer, in a buyer of the future, you're wearing your marketer hat and how we want to be like building market campaigns or whatever formats to connect with buyers where they are in their journeys. So could you imagine some leap beyond even what you've been talking about? - Yeah, no, I think it is like, it's funny. Like I'm thinking back to funny infographic I saw where there's a lot of stuff around with the more tech stack around the amount of different offerings and services there are out there. And there's a funny infographic that gets updated every year where it's like, you know, 30,000 applications or 60,000 applications. This is intentionally this eye chart infographic of all the various different logos and products and services that they offer. There was a funny like, you know, twist of that where somebody updated that and it was just all open AI logos across the board. And it's like, that's it. Like it's done, it's set around it. So I don't think it's funny, but it does open up that notion of like kind of what you're hitting at where I do feel like as we bring these, as you bring that, if we saw that content supply chain, if you begin to like hone and bring those things together and you see that like, hey, what used to take 50 hours takes five minutes and what used to take 50 people takes five. To me, you start seeing reduction, contraction in the amount of different logos or things that you'll have to plug and play across the board. Because really, you know, back to what I even started with with the build and buy, like our tech schematic looks like many large companies. It's a mix of third party and first party technologies where ideally we've made, you know, very thoughtful strategic investment decisions on what we chose to purchase or what we chose to build internally. That you start to see that contraction down to like, if I can have less, that's a huge upside benefit, right? It's, that means those are less, that's as both the product buyer and the marketer, those are less tabs as a marketer. I need to open up to get my job done or to find my report or find my insights because those things are being offered and those types of capabilities are more comprehensive across the tools that we have. As the buyer, you know, obviously from a cost perspective, of course, all right about that. But also from an integration standpoint, right? The cost of, you know, integrating, building these different types of technology into our stack is pretty significant. And so by bringing those things all in-house, I'm not in-house but if I can track them down, you can understand like there'll definitely be some efficiency gains. And then I think the other thing that, you know, I talked a lot about the content and the impact that Denai and LLM on that. And we looked internally at like where we could see some of the biggest upside or like we really, we went on like a toil hunting mission and we went to all of our marketing teams to understand what were the redundant, repetitive, painful tasks that were happening across the board. The number one category thing that went or came up was data. And it was about access to data, insights to data. And we really like seek to understand more, right? 'Cause you say insights, it's like, what does that mean? Right? When you click down into it, it was really this notion of it ran the gamut of, hey, I just need to dig in and understand what is happening across my campaign or my ecosystem. How many leads have I driven through an email, through email in this region, in this timeframe? What are the conversion rates of those? Things that feel like a very simple, straightforward type of request, but knowing how complex that means that the very center of different data sets would mean clicking or digging through multiple dashboards or worse, having to pull in somebody that has skill sets like SQL Hype Pig, et cetera, to go and write those queries for you. So we've been able to begin to start to stand up or build out language models that will answer those types of questions with simple language prompts. And we're training it on top of our own internal data. And that's really just the starting point, right? And it's exciting and it's cool to see and it gets a lot of sort of like Gooz and Oz when we demo it or show it first, but really that's just the beginning of being able to answer or get that insight. And so wearing both hats, you're one as a B2B marketer, you're capturing quicker insights, quicker information to make decisions on pivoting your strategy or your approach within your go-to-market campaign. If you're a marketing analytics person that has that skill set and wants to do deep work strategic analysis, you're not having to answer at point what feels like sometimes elementary or repetitive questions, right? You're able to push those types of questions or those types of ask over to the model or over to something that's more self-serve. So, I think that like that gets me really excited not just in the next five years, but in the next five months. And so that's just the beginning with one set data set. We're merging multiple and training on all of the key marketing data sets to begin to answer the types of questions that the prompts we want to do are not just more transactional or straightforward, it's more where should I be investing my budget, right? Or where should I be allocating my, what audience is a potential TAM for this product in this region? What are the different trends that we're seeing around conversion or LTV over the past several weeks, several months? These are questions that are asked and answered and done, but very much through deep analysis and work from our analytics and data science teams. But we're building more and more conviction that as we train or craft these models that we'll be able to answer those using an LLM. And that gets us set more excited because it's, again, back to that notion of is this opening up resources and opening up bandwidth internally for our teams? Is it also giving marketers and folks on the front lines that information, those insights more at their fingertips where they can make those decisions and make those prioritization and allocation adjustments. Love this conversation. We have one more question to wrap up before we sort of close. And it really touches on a couple of the comments you mentioned around research and learning and insights and even some of the more tactical things like content generation of the supply chain you alluded to. But sort of in summary, if you think about B2B marketing and how AI might change that, what is probably your biggest hope for how you think this story will unfold over the next five months or five years? Yeah, I think my biggest hope on that is it's, and honestly it ties back a bit to LinkedIn's own vision mission. It's making people more productive and successful and removing democratizing insights, democratizing, information across the board to enable marketers and enable teams to do their best work. One of the more inspiring aspects of working at LinkedIn and where I love is really being able to understand and tying and bringing together our economic graph and capture those insights and know that one, hey, how can this inform the type of campaign that we're gonna drive or the type of message we're gonna deliver? But now at the end of the day, that means that that's either finding somebody the job that they're looking for, giving somebody the opportunity to grow their business across the board or connect the right product to the right buyer. And so I think what's really long been one of those, things that's held back has been, there's an ever long list of data scientists, marketing analytics, different roles that have specific skill sets, engineering to either A, capture those insights or B, build those experiences. And so by beginning to see that like, we've gone from a, on someone's local machine prototype four months ago to now we're nearing 100 monthly active users on a Gen AI, marketing insights product is super exciting and energizing 'cause it's like, okay, this is like, this is beginning to get us there into that step. And so I think about that very much from what that LinkedIn lens of, if you're truly enabling somebody to do their best work, they're doing their best work because they have all the information at their disposal and they have all those capabilities at their disposal to build a campaign, build a message and achieve the objective that they're looking for. And so I really feel that what we're seeing with AI and Gen AI is just going to ideally accelerate and lower that bar that folks need to clear to be able to capture and gain those insights to just really deliver that best in class, best in mind campaign that they have. - This was an amazing conversation, learned a ton, took a ton of notes and just like, by spinning about what's going to be changing.