[Music] [Jay Proulx] All right. Thanks everybody for coming and joining our session on Leveraging Generative AI to Deliver Personalization at Scale. I think this is a extremely hot topic at the moment, and I think we've got a lot to share.

Lots of announcements for us to make use of this week, from GenStudio, Firefly APIs, you know, looking at, content supply chain across our enterprises. So we've got a lot of things we want to share, particularly our investments in our design systems, in our frameworks for adopting enterprise content supply chain, and data, particularly leveraging first-party data.

So we'll be talking today about ArtBot. We'll be talking today about Content Factory. We'll be talking today about Omni and how we are looking at leveraging these investments to bring generative AI into enterprise content supply chain, make sure that it's adoptable and allows us to do hyper-personalization, a one-to-one personalization that we've been expecting for years, and now with generative AI have the ability to fill that funnel. So we're going to have some fun today. You know, I think there's some interesting content for us to make use of, and we'll get started.

[Alissa Hansen] Just kidding.

Only we had GenAI to make the mouse work, right? There we go. All right. So my name is Jay Proulx. I'm the Global Adobe Platform Lead at Credera, Omnicom's Digital Transformation Center of Excellence, and we focus extensively on the Adobe Platform holistically.

We've got experience across the industry. I've been working with major brands for the last 25 years or so with Adobe Technologies and now leading the Enterprise CoE over here at Credera. And I'm Alissa Hansen. Nice to meet everybody. Thanks for coming. We're at 4 o'clock on a long Summit day. So hopefully, we'll move at pace here and have a nice accelerant as you move into the evening festivities of Vegas. A little bit about me, I'm Chief Production Officer at Critical Mass. I'm a Digital Subject Matter Expert for 20 plus years, mainly in production. I now represent content transformation at an Omnicom level as a practice lead.

So at Omnicom, our joint ambition, relentless focus on customer outcomes, democratized open operating system for marketing teams, and investment fueled customer-centricity.

I'm going to take that from you. Okay, so when we talk about content, we are focused 100% on our clients, unlocking value for our clients. You can see here that we are building solutions to last across many verticals within the marketing sector, and we're seen very much as a transformation partner. So very proud and sometimes humbled by the clientele we carry on our Rolodex. And they really look at us as a trusted partner for all things transformation. And right now, content in particular is transforming the way we work. We're hearing that today. I think we're at, sort of, the epitome of where, sort of, creative excellence is meeting within the Adobe Summit. So at Omnicom, when we think about content, we're going to talk very much through the content lifecycle, everything from origination and development all the way through to creation. We're going to introduce to you content engineering, a creative engineering. It's a practice that really starts to take the big idea and shape it for all of the different spaces and places. Content needs to now move. There's obviously adaptation that comes along the game and there's optimization drivers. So it's a complicated but energetic place to be, and we're very fortunate to be top right quadrant. Top, yep, right quadrant of the Forrester. This is something that for those of you who have ever worked at Forrester, you can't fake your way. It's a very, you know, rigorous study, deep analytics study working with, sort of, our consultancy to make sure that we very much have the capabilities to claim the space that we do today. Awesome. So Credera is, as I mentioned, Omnicom's digital transformation center of excellence. We are the most specialized, Adobe partner with specializations across Adobe Experience Cloud, Adobe Experience Platform. We were the second to achieve our Real-Time Customer Data Platform specialization, and we've been focusing on content supply chain for the last couple of years. This has been a major area of focus for us, and we're implementing the world's largest content supply chain implementation across the 700 agencies at Omnicom.

If you want to hear more about that one, you can join Dr. Ali and Cleve at a session tomorrow 1pm around maximizing creativity and scaling content.

So consumers today are presented with a vast amount of content. Not all of it is relevant. We know that that's an area that we want to focus on, and in order to do that, we need to start with the beginning ideation and brief.

We are working with our creative teams to focus on these categories of audience insights, creative strategy, localization, regionalization, measurement and optimization, and cross-channel integration to ensure that when the creative process kicks off, we're focusing on the results that are necessary for achieving personalization at scale. If you don't start at the beginning, the assets aren't available for that personalization, so it's imperative that we start at the beginning.

So I'm sure everybody has seen this slide. I think this is, you know, speaks directly to the problem for customers that have products and need to have various artifacts for each product and work in global regions, and then are trying to personalize at scale the number of creative assets that are necessary to fill that content supply chain adequately is essentially infinite, especially given that customer journeys are constantly changing and in flux. It's not enough to just have enough assets now. You have to keep the assets flowing, right? I'll hand over to you.

So you heard Jay mention earlier we wanted to talk to you a bit about ArtBot. So ArtBot is a center of excellence within Omnicom. And it really is the precipice of where a lot of this is coming together. So you're going to start to talk about content. We've already said it 100 times plus, and we're only 6 minutes into the conversation here. But content needs to be dynamic, right? It needs to be targeted. It needs to be personalized. It needs to be optimized. You have to have measurable KPIs. The basically, there's this whole ecosystem of, sort of, everything we're doing today, and everything is centralizing within that center of excellence. A big part of the conversation is infusing GenAI solutions into everything we do, right? So when we bring in GenAI solutions to our clients, we want to make sure that we're not just kind of throwing tools and tactics. Everything starts to move towards a holistic conversation. So even here, you know, why all of these things? Why do they matter? Well, if you start looking at language modeling, we can, sort of, mobilize insights. We can get to connectivity quicker. You know, you start looking at modular systems. We're going to talk about that a bit more. So using, you know, creative platforms that allow us to, kind of, dissect creative and rebuild it in a way that truly is omnichannel. We start bringing in creative intelligence, that rigor of performance, and start looking at that very much at an atomic level. So using creative taxonomy to, sort of, really understand and dissect what our creative is telling us today. There's some really interesting things happening in AI with neurotesting, so for those of us on the media side, we've been doing attention recall for years now. We can now use predictability modeling, AI-trained models to better understand what you as humans are most likely to receive as you're engaging with our assets and with the content we're putting out. So all of that kind of becomes-- Yes, it's a bit of a smorgasbord, if you will of AI, but it all serves a larger purpose, which is infusing AI across everything it is we do. Awesome. So let's talk a little bit about connected collaboration.

You know, we see the need for not only filling that content supply chain, but connecting it to our digital experiences.

It's not enough that we have our, you know, technology teams implementing first-party experiences. We also need to make sure that, that matches up with the creative pipeline. So what we've done is developed a framework called Content Factory, which allows us to take an iterative approach to content supply chain, and allows us to ensure that we're leveraging Adobe's Technologies end-to-end with ideal workflows for enterprise marketing teams so that we're maximizing the efficiency and spend on production.

We like to think of this as a data-driven approach because we're constantly iterating on customer performance, and media performance, and want that to inspire the next round of creative ideation.

So when we look at this, we're really focusing, Content Factory on an adoption model that supports, marketing agility for our marketing teams, right? Everything from visual design and content engineering through data analysis, you know, personalization, it's necessary for us to look across marketing operations and make sure that everybody is, kind of, working in lockstep towards that goal of hyper-personalization at scale. We know that all marketing team members are, you know, work in particular tools and want to be able to focus on delivering within their space. And so we've come up with a model of operations that allows those teams to really focus on what they really need to, but also leverage all of the tight integration between Adobe products to simplify those workflows and make sure that we have that constant flow of content. So when we look at content supply chain, this has obviously been a topic that we've been discussing for the last couple of years, you know, particularly where it focuses on Workfront, and Creative Cloud, and AEM assets and Frame.io. We've expanded upon this with our own tool, Omni, which I'll introduce later on. But we also want to expand upon that and make sure that it's connected to all of the rest of the enterprise tools that we've adopted across the industries. So being able to produce that content, but then also make use of it, you know, in modular content approaches, sharing that content across all of the Adobe Digital Experience products, and then using tools like Customer Journey Analytics and Real-Time Customer Data Platform to personalize and measure those experiences and make sure that they're effective in the way that they need to be.

Okay. So now limitless creativity. So I don't know if that's a good thing or a bad thing if I'm being totally honest. I think limitless is in itself, obviously, shows great potential, but we need some ground rules, right? So when we start to scale assets, when we start to bring, kind of, the potential where we could go, we still have to remember that, you know, we are representing global brands with high integrity. There needs to be a level of excellence. And I would say maybe for the last four or five years, we've kind of got oversaturated with AdTech, and everybody got really excited about, sort of, where DCO and different tools could take us. And by no means are we saying those are not part of the game, but we kind of lost track of the fact that we're storytellers or brand builders. So there will be a lot of emphasis now, and as we move into these new spaces, we really want to make sure that we're supercharging the creative process, and we're enabling creators to do just that. But just much the slide is a juxtaposition intentionally because as we start to elevate and integrate, sort of, that design thinking those design systems, we have to bring a level of transformation and technology to enable, sort of, that speed and that scale that we're here to talk about today. So we break this down really into kind of three areas of focus. So we have creative engineering. I mentioned that earlier. It really is if you think about a big idea, traditional creative agency, a traditional creative director, that's a platform, and now that platform needs to travel. It needs to travel anywhere the media buy goes. It needs to travel anywhere your consumer goes. We all know the funnel is somewhat, let's just say, evolving, right? So the area of, kind of, engineering creative to be modular and its ability to travel truly omnichannel and directly connect to the media is a big part of that practice. Then, of course, we move into generative services. That's bringing any of the Adobe suite of tools we have today along with amplifiers that allow us to scale, and then, of course, technology enablement, things like taxonomy, digital plumbing, API connectivity, really just, you know, mechanisms, if you will, digital plumbing to make sure that we're moving from point A to point B. A big part of our presentation today is actually to bring this to life, to show you in real case how this works. We're going to look at some work from AT&T, and they're a really good example of a client that's a early adopter in moving into what we call the adaptive system. So any piece of content can be, sort of, you know, reverse engineered, if you will to its key componentry. And then that is that practice of creative engineering. And then once we have that in play, and that should be very much at a holistic design system. It has the playbooks. It has the brand guidelines. Now you can take that same modularity approach, and you can start to get very utilitarian, pragmatic of how you carry that across the channels. These now become shells, right? So how do you make sure that these have that creative fidelity? They're just available to us to dynamically assemble assets and this is where our generative powers become very, very focused, right? So looking at something like AT&T, you can see here Lily. Hello, Lily, anyone who has a TV has seen her a handful of times. She's a great brand ambassador. She's also everywhere. Intentionally, she's everywhere because we built the program to do just that, to travel very effectively and efficiently across the channels. The nice thing about moving into a system, and AT&T has found this, you start to get horizontal efficiencies of scale, right, because that exact same framework that we move from one campaign can be lifted and shifted, and it can move immediately to another campaign. And you start to realize that neither one of those look identical because it's the assets themselves that bring that layer of originality, but we have production efficiencies and scalability to start personalizing content of speed. The green screen's making a comeback for anyone who's been in production as long as I have. We shunned away from that for a very long time. We're now gratefully bringing it back. Of course, virtual production becomes a play here. Why? Because if you do something like this, you can start to go to large, you know, live action shoots. You shoot the storyboard for a linear work. You shoot the constructs for a long tail work. Someone like Lily, she can be pointing up, she can be clapping, she can be doing all of these generic gestures, and, of course, we can bring that into an asset repository. She's pointing at a CTA, she's pointing at you, she's pointing at a call to action. It's just, sort of, materials that we now have available within the system. And then when you look at something like a live action shoot with a green screen, as utilitarian as that is, you power that with GenFill, GenAI, now you can start to have that limitless less creativity in terms of how these assets can scale. So really good example of a brand that's really fully leaned in to the adaptive system automation and generative services. Another one we wanted to talk to you today about is BMW. So BMW is doing everything that we just saw from AT&T, and they're going one step further because they're now leveraging, the Unreal Engine, if you will, the render engine. So this create once, publish everywhere. It's kind of that idea. So when you get into, kind of, rendering, especially with automotive, you have to make sure that you're pixel perfect. So there's some things that generative tools will allow us to do. I don't see any future where we're going to use GenAI to replicate the next model release for any automotive client or the next device for any telco client. So you can start to, kind of, see how we bring this to life. You bring in, sort of, environmentals. You can start to bring in let's see if we could click through this virtual productions. These are co-collaboration tools that allow us, allow you to, kind of, see the creation process. And ultimately, we're getting to omnichannel frameworks. So here we go. You can, kind of, play this out here.

We see the automation process. We now see, sort of, the generative services. You can start to see how this renders can, kind of, move to ultimate scalability. And then we can, kind of, build that omnichannel consistency from a user perspective, how they engage with content across the channels using both render engine and automation tools.

Well, this is supposed to have audio, but it does not.

So here's a good example of we have our Super Bowl spot, right? We have one vehicle. The vehicle was very embargoed. So a good example of taking a Super Bowl opportunity where our traditional teams are working on creating, kind of, the linear TV campaign, our digital creative engineering teams are now moving that into render engines. We're now giving ourselves the ability to do the long tail, you know, content automation, and you can start to see how we can bring this now into different in situation opportunities. So landing page, social opportunities, another good example of a client that's now scaling. So two really good examples, slightly different KPIs, more personalization, more creativity, but both are leveraging a lot of the things we're talking about right now. All right. So we've looked at modular design systems, the opportunity to introduce generative AI into every element of those modular design systems so that we're able to tailor messages and create variations of messages so we can test their effectiveness.

Now we need to connect those experiences with one-to-one personalization. When we say one-to-one personalization, what might that look like? So when we look at personalization, as I mentioned, there's lots of opportunities for us to bring in the whole lifecycle of our tools to provide personalization across both paid and earned channels, but also owned channels, particularly with a need to focus on first-party data strategies. And our tool, Omni, is able to integrate across Adobe's ecosystem for campaign orchestration that we integrate with Workfront. We integrate Omni Assist with Adobe Experience Manager for generative variation, and profile enrichment data signals through Adobe Experience Platform. So we'll get into this in a little bit more detail here. So when we look at connecting first-party data and we're looking at all of the different opportunities to connect on different touchpoints with customers. We need to make sure that we're leveraging all of those facets with the intent to understand how the customers are interacting with the brands, and then respond to them with personalized experiences that make them feel like they're the only customer.

So through Omni and through Adobe Experience Platform, we have the opportunity to create identities that allow us to create identity namespaces that allow us to stitch all of these data signals from different platforms and consolidate those so we can create that single view of the customer. And leveraging Omni, we can enrich that further with person-specific IDs in order to expand on that beyond first-party data. That way, every time we have a customer interacting with the brand, we're really not just leveraging our own data, but we're leveraging the data through our Omni Platform. What this might look like in the automotive sector is to be able to provide personalization where, you know, we're not just connecting to customers through customer journeys, we're being specific down to the make and model and year of the vehicle that they've bought, that we're able to make sure that we're leveraging, you know, our connected vehicle platforms to better leverage those, you know, in car solutions to personalized experiences.

We need to mix a combination of orchestrated customer journeys. So we do this through Adobe Journey Optimizer, has the ability to allow digital marketers to create orchestrated experiences through segmentation, but also through real-time events and time-based signals. And what this allows us to do is make sure that marketers are, kind of, in control of that flow of messaging, particularly when we want to be able to provide that personal connection to the customer.

What this doesn't necessarily reflect and where we need to move beyond is in advanced decision-- Sorry. Advanced decision management. So Adobe Journey Optimizer also has the ability to leverage machine learning models and artificial intelligence to provide ranking for offers. And using some of these tools, we're able to go beyond those orchestrated experiences to personalization across all of the different media that we produce for our first-party experience. That goes beyond to allow us to make sure that every offer on every channel is consistent by leveraging that unified customer profile and leveraging it across our omnichannel experiences. Plug that into the orchestrated experiences, and now we have the ability to both work directly with the customer, but also leverage all of those variations of media to further augment that with a much more personalized view of that customer's relationship with the brand.

So we can make this possible by leveraging Omni, which is the largest marketing dataset ever. And substantially, in terms of first-party data is a billion people-based identifiers that we can leverage to enrich profiles and go beyond the first-party data that we've collected through all of the customers' interactions. We do this through a network of 50 global data partners that allow us to share signals and data through a data clean room approach. We'll be further optimizing this in the future with the federation capabilities of Adobe Experience Platform, which will make it that much easier to integrate with over time. So I think that's, like, going to be really exciting. It'll very much speed up the adoption of our additional data signals.

Oh, so we're now going to move on to-- So we've delivered all of these personalized experiences, we've orchestrated journeys, we've made sure that they're consistent across channels, we've produced all of the media. How do we know that it works? So we were actually, you know, it's how do you bring home a presentation better than data optimization? So bear with us here. This is somewhat exciting. I promise, says the creator, not the analyst in the room.

I think, you know, what do we mean when we think about optimization? So we talked about DCO earlier. There's very much of, like, on our heels, kind of, when we think about creative insights where we've all seen a version of this learning cycle, right, where you, kind of, have an idea hypothesis, you create multi-variant testing, maybe an A, maybe a B, maybe a lot of As and Bs depending on how much scale you have in the system. You put that, sort of, into the media plan. You see what works. And then if you're lucky, you mobilize that insight all the way back to the creation process, and here we go. But it is a very, kind of, reactionary place to be. And as we know, consumers don't really allow for us to find the time for their insights. We need to move much more, I would say much more at speed. So the optimization game is really interesting right now because we are, kind of, breaking that cycle to be more of a snake. No more circles, right? We want to, kind of, ebb and flow with where content is going. And when you think about the content lifecycle back at the earlier part of the presentation, we talked about origination all the way to optimization. Now think about it through the lifecycle of how we publish content. So there's, kind of, the pre-production, pre-publishing stage. There's the moment to be when we're actually starting to target and speak to our consumers and our clients within, sort of, the decisioning stage. And then, of course, we just talked about that, sort of, post-reactionary stage. And all throughout that lifecycle, we now have the ability to bring on AI, machine learning models of predictability, of performance behaviors. So even before we put an asset into market, we can test it, right? We can start to look at different insight tools, we can look at those cognitive tools like we mentioned. We're much more granular now at a targeting level. We talked about Omni as a first-party data. We have more data insight than we ever have before. So we're not just, kind of, what was that saying? Spray and pray. Like, we shouldn't be spraying and praying for anything we put in market because we have way too much firepower and, sort of, full force multipliers before we ever even send an asset into the market to have a better understanding of how that will exactly perform. And then it becomes that, sort of, last cycle, that, sort of, post-reactionary, that's re calibrating. That's pivoting. That's finding perpetual performance drivers so we can fine-tune along the way.

A great example of a client that's doing a lot of this with us today is Nike. So you're going to start to see some of that design elegance that we talked about that has to come first. You know, we have to make sure that, that, sort of, brand language is in place. So really kind of moving from where they were to where they are now today. We've brought this across both product, apparel. It's in brand ambassador work on, sort of, a social level. So you, kind of, start to understand like that design language has to feel very much inherent to the brand. It is the, sort of, universal design systems at play. That design system is built to be modular and dynamic and fashion. So now when we get to that, we start to think about how are we going to bring all of this together? So really this is a nice, sort of, illustrative example of performance and production going hand in hand. So something like Nike, we have a core KPI we're driving to. We have a better understanding of the data signals. We know our audiences. We have the media buy. We know the experiences. We know the channels they're engaged on. So everything, kind of, thereafter, if you, sort of, look at this orange layer, these are levers that we now have available to pull, so, sort of, bring that, sort of, dynamic creativity. And as this wheel grows out, now you can start to see where generative becomes very powerful because everything here now becomes variable and matrix, right? So you can-- A really good example is copy, you know, a pragmatic headline versus an emotive headline because production barriers have now been removed because we're now moving into automation because we have generative tools, copy tools that can put more, sort of, permutations into the system, we don't have to be held back the way we were before. So you, kind of, run through this, right? We have, sort of, the idea of the assembly line that's creative modulation in full fashion. You have dynamic assembly in terms of how different assets are being pulled in. If you get really smart, you have API connectivity into feeds. So you can go right from product feed into, sort of, marketing application. And now you bring in that, sort of, creative taxonomy. This is the most pivotal part to make sure this actually works. That has to be, kind of, your digital DNA on the asset to make sure that not only do we know this one particular asset is performing, but most importantly, we know why. Because we've now dissected this asset to a, sort of, individual configuration, and that DNA taxonomy is now triggered. So often I get this, well, why would I ever look at one ad? Of course, you wouldn't. There's no significance in looking at one in particular asset. But when you start looking at it systemically across all of your channels, this is how you can very quickly start to identify learns, trends, and that does feed into your learning library of opportunities. Nike also brings in the cognitive testing. We already spoke on this. So really cool things here happening. It's a great opportunity to throw something like this when you're generating a design system that you want to now run hundreds if not thousands of variants against that design system. So before we launch something like a global template, we're going to test the hell out of it to make sure it really works and we pressure tested it. And then, of course, you can kind of get the idea here. Everything then goes into market. We're now in that post-published cycle of the lifecycle, so we're refining. We're looking for those, sort of, ultimate, you know, drivers of success. And then one last, kind of, visual here. I think this does a really nice job. We've got creative engineering in play. We now have those KPI drivers to understand what we're tracking against. We've got AI vision in terms of, sort of, the metadata level of what this asset looks like, contextual tagging, time, day, personalizing our audience cohorts and segments, ensuring that is, sort of, well developed, well liked, appreciated across the content journey. All of these content data points can now be cross-pollinated to what to give us a better view of the consumer. So this is why we're really excited at Omnicom because we're really having a lot of creative passion and how creative can drive the data conversation, which traditionally has, sort of, been, you know, maybe not as involved in some of the metrics when we think about performance KPIs.

When you play with Nike and that, sort of, speed, you need to have a house to organize and aggregate all of those insights. This is a custom build tool for Nike. It allows both their performance teams and our teams to make sure we're all working off, sort of, the same learning library. They can request tests. We can perform tests. It also has large language modeling to make sure that we're mobilizing insights from the test and bring it into, kind of, a key area of exposure.

And then that takes us to...

Our last but nearly final slide 'cause we will not end the presentation on the tool chain is the content supply chain. So this isn't to be confused today with Adobe's content supply chain. This is more of a digital methodology and how we're bringing everything together. So it's really an aggregate of our creators using the Creative Cloud. It's bringing in what we talked about with the render engine of Unreal. It's centralizing all of our learnings, kind of, within an incubation area to throw all of that, kind of, content performance stuff we talked about. We're now moving into how do we amplify. So there are tools in the market, DCO tools, CMP tools. They're not going away. So a big part of us is to understand how the creation cycle and the amplification cycle have to work, and then, of course, those tools now move into owned, earned, shared, channels. And then from there, we have data insights and aggregation of Omni.

So is this easy to do? I don't think any element of this is easy. You know, I think we're, as we start to adopt generative capabilities across the ecosystem from origination through variation. You know, there's a lot of building blocks that need to come into play that will allow us to be successful.

As I mentioned earlier, one of our biggest challenges for the past few years has been not being able to generate enough content. Now we risk generating too much content. So how do we make sure that we have the right amount of content? We still have governance and oversight challenges with generative AI, and how do we build those into our marketing operations in a way that allows us to leverage the capabilities of generative AI, allow it to enhance our productivity, but also, make sure that all of those creative artifacts, modular content, and how those are distributed to channels is what the brand intends. So Adobe's tools allow us to...

Customize the workflows for ourselves and for our customers in many different parts along this ecosystem. When we look at content supply chain in our content factory framework and the operating model for marketing teams that, you know, are looking towards being able to adopt generative AI capabilities.

You know, we want to be able to introduce workflows all along the path that allow us to validate and not just trust the system. So early in the content supply chain, of course, we have our creative processes. We build our artifacts. We do review and approval with tools like Workfront and Frame.io, but we also have the ability to inject generative variation later on in the process. We do this in Adobe Experience Manager using modular content approaches, like, experience fragments with different variations of assets and copy to produce testable and personalizable content that can be distributed across channels. And, of course, Adobe Experience Manager has always had the ability to provide those publishing and distribution workflows as well, so that we're not only handling, you know, review and approval and collaboration for origination, but we're also providing review and approval for generative variation.

You know, when we add on top of this our need to continue to globalize and regionalize content for our global partners, this becomes, you know, a challenge in terms of the translation frameworks and processes that are involved as well. So it's important that given all of the orchestration required that marketing teams have adopted a framework and have integrated their solutions in such a way that their content will flow to meet those needs of personalization at scale. And, of course, we have all of those tools available within Adobe's ecosystem, which is frankly, I feel like it's unheard of, and Adobe has been really, you know, breaking ground on this in the market in ways that I have not seen other vendors, and we're very excited to be able to partner with Adobe on implementing solutions that allow us to achieve these outcomes.

So I think the last-- Oh, did we lose the video? - No, it's next. - Okay. - Can we keep a hand on that? - No. - Okay. - No. It's next.

So key takeaways. So we started with seamless integration of modular design systems and generative AI to produce content in a way that allows us to personalize for customer experiences without creating an unnecessary amount of variation for the sake of variation.

We have discussed enhanced omnichannel personalization through first-party data. I think this is, you know, of all of the topics that we've touched on today, this is no less important given our move away from third-party data and focusing on experiences and collecting metrics and signals from customers through our first-party brand experiences.

And we've talked about data-driven content performance and conversion optimization. So all of this is we're not achieving the results for our businesses that we expected from the outcome.

So all of this, as we've discussed, along with Adobe's tools, our frameworks and systems and capabilities like Omni that can provide significant enrichment for customer profiles and our ability to execute on this end-to-end with Adobe is, you know, I think, has been a really significant area for us to invest ourselves, but also for us to invest in solutions for our customers. Okay. So as promised, we're going to end with a little bit of a razzle-dazzle reel.

And we did very good on timing, so we're coming up to about the 40-minute mark, and would love to, kind of, close out the session with-- I hope to be a bit fluid of a Q&A so-- [Music] It's better than us closing on the content. Thank you.

[Music]