[Music] [Olin Moran] Welcome, everybody. Thank you for being here. I know we're probably what standing between you and a long weekend, so we'll make it, maybe get out of here a little bit early, but we're going to talk today about AI-Powered Commerce.

Really, again, we'll talk about a little bit about who we are, we'll share some of the trends that we're seeing in the industry. And finally, we'll share some of the cool things that we're doing with our clients to help them take AI and GenAI into production within the commerce space.

So welcome. For starters, I'm a partner at Credera, Olin Moran. Credera is a management and technology consulting firm. We're part of Omnicom, really do a lot of the digital transformation work for them. I lead our commerce practice for North America, as you can see here, I have over 15 years of experience in the space, across strategy, enablement, and optimization. You can see some of the logos here, really just to give a sampling of the type of companies we deal with, everything from smaller boutique companies to Fortune 50 firms.

Also, another anecdote about myself, right out of college, I actually opened a restaurant, and you might ask yourself, why am I telling you this information? And it really was the juxtaposition of running a restaurant and then going into the e-commerce space. That made me fall in love with the analytics that exists in the commerce space. If you think about it, it's like into the restaurant I would make decisions about the menu and things like that based on observations, some data points about what are people buying, but really all driven out of gut feel and business sense. After two years, if you don't know about the restaurant industry, it's pretty cut throat. So got burned out, sold the restaurant, decided to give e-commerce a try. And the amount of data that was available to me and to the business was amazing, right? And that's probably why a lot of you like e-commerce, like the space. But really, I was now able to make decisions based on really a lot of data points, right? I could see people interacting with the website, how they're interacting with it, the products that they're looking at, where they're leaving, really allowing me and empowering me to make data-driven decisions, testing those hypothesis, and then actually knowing with statistical significance what are the things that are driving value to the business. So the juxtaposition between that and the restaurant business was just one of the things that made me really fall in love with the industry, and I'm sure, again, a lot of you probably have similar stories and backgrounds. So, with that, I'll pass it over to David. [David Battrick] Thanks, Olin. Yeah. Great. Hi, everyone. I'm David Battrick. It's great to be here. I know we're almost at the end of this wonderful conference. I have been in digital marketing and e-commerce roles for about 20 years. I've worked with some wonderful blue chip clients, including Starbucks, Prologis , P&G, Molson Coors, Royal Mail, to name a few. Part of my role is helping marketing leaders overcome common e-commerce challenges, so whether that's creating brilliant digital experiences, leveraging innovative solutions with the right technologies to build long-term customer value and relationships, and to help navigate a complex landscape of technological and competitive challenges, and ultimately, to succeed in a dynamic digital marketplace. I am originally from South Africa. I spent many years in London with multiple digital agencies, and I now reside in Colorado. Before we get going and get into the heart of our presentation and before I hand over to Olin, I do just want to take a second to introduce you all to Omnicom Commerce. You may not be aware of what we do, or what we stand for, and some of our AI-powered commerce solutions. So Omnicom Commerce provides leading edge commerce capabilities at a global scale. We help clients orchestrate intelligent outcomes across all commerce channels. We are the world's largest end-to-end commerce experience company. We manage over $10 billion in retail media. We partner with over 50 of the top 100 CPG publicly listed companies. We also have over 400 digital marketplace partnerships, and we have thousands of commerce specialists across the globe.

We pride ourselves on simplifying the complex by seamlessly integrating retail and brand media, direct, and third-party media, digital and in-store experiences, precision marketing, and CRM. Our aim is to create seamless, consistent experiences where every touchpoint is tailored based on where the customer is in their decision journey, and in doing so, generating the best outcomes for our clients. Our capabilities cover the end-to-end commerce ecosystem, so from strategy to enablement to optimization. So whether it be omnichannel strategy or all things direct to consumer, leading edge commerce technology services, retail media, or marketplace activation, we have all bases covered. We're also continuously investing in the space to power even more connected commerce solutions. So in January, this year, Omnicom acquired Flywheel for just under a billion dollars. This was a significant acquisition for Omnicom. As with the addition of Flywheel, we're now the most advanced end-to-end commerce experience agency. Flywheel has helped us close the loop in commerce experience through scaled marketplace activation. So think Amazon, Walmart, Target, etcetera, where many commerce transactions are happening today. We're also powered by the industry's biggest marketing data asset, Omni. Omni provides advanced analytics and insights into market trends, consumer behavior, and competitor analysis, 360 cross channel media planning and optimization, and enables data-driven decision making. Omni is used in over 100 markets, leverages data from over 50 data partners, and works with hundreds of marketplaces. And with our acquisition of Flywheel that I just mentioned, we've combined Omni and Flywheel Commerce Cloud. These integrated platforms enable brands to build and execute digital commerce strategies to drive sales and share across the full e-commerce ecosystem. We have, I think, over 30,000 power users of these platforms across the company. The last thing I wanted to highlight before I hand over to Olin about our capabilities is Credera, which is part of Omnicom. Credera is one of the selective agencies in the world that hold Adobe Global Platinum Solution Partner status. And we have achieved specializations across all Adobe solutions. This high level of proven Adobe expertise empowers Omnicom clients with tailored, scalable solutions that address the unique requirements of their respective organizations. So that is a very quick overview of Omnicom Commerce. I'm now going to hand over to Olin, who's going to share with you some of our AI-powered commerce solutions, using Adobe and Omni that I just mentioned, and share quite a few really cool demos with you as well. Thank you, David. As David mentioned, again, we're going to share some demos, but, really, before diving into those, wanted to share some of the things that we're seeing in the industry. These are some of the stats that we have been looking that really leveraging and dictating why we're doing the things that we're doing. So before we dive into that, everybody here knows, AI is a hot topic. I'm sure you've been hearing about it all week. You're probably sick of it a little bit right now. But the reality is that 83% of companies consider AI a top priority. We also know that in the commerce space, AI has really been at the forefront, right? That's a historic for the longest time a commerce has been really leveraging AI, enhancing it. And a matter of fact, a study shows that, the e-commerce space is number one, when it comes to early adoption. So they're really leading the way. However, there is still a lot of opportunity. Again, going back to my career, so go back. I'm an analyst as in e-commerce, and there was a lot of data available to us. However, it still took us days and weeks to extract the data and get insights out of it, usually involving multiple systems, putting them together into a spreadsheet, and then once we had those insights, we had to work with building out experiences across multiple channels. Again, my experience, I had to work with five different agencies in order to execute on these campaigns. And by the time they were out in production, I usually would have lost thousands, right? With some of the brands here in this building, that probably is more in the millions of side of things. Again, I worked for a smaller company.

So what is that we're seeing in this space that's really helping, and where are people investing from an e-commerce standpoint? As an overall theme, it's customer journey, right, and that comes with no surprise. Everybody wants to improve the customer experience. However, it's no longer our typical e-commerce funnel, right, where we land on a page, we want to get them to a category page, and then a product and checkout. It's really grown into an omnichannel experience where we really want to affect and influence the experience across the entire journey. Even though, 76% of transactions are still going to be happening in physical locations, we now know that all of the digital touchpoints that happen in order to get customers in are super important. And having a seamless experience from touchpoint to touchpoint continues to be a huge priority. So how are we going to get there, right? And where are people investing in this space? As a super high level, chat continues to be the number one area of investment when it comes to GenAI and AI. However we see, personalization, content generation, and improved analytics, quickly gaining traction. And those are the areas, that today, we want to focus on, and are investing heavily in this space.

So diving into analytics, right? What we're seeing is really a shift in companies that are moving from siloed manual data analysis into predictive analytics driven by data science. The reality is that...

We're getting better at collecting and centralizing data. We use things like data lakes, CDPs, and other technologies to get faster, cheaper, and easier to use. 25% of people are saying that, companies are saying that they're wanting to invest more into this space, and that's on top of 20% of those, that think, that they already have a great foundation. That means that, about half of the people in this room, think that AI and Analytics is a huge opportunity. However, there's still some concern, right, and that is data quality. 31% of companies list data quality as a big concern. Since we're wanting to gain insights from unstructured, semi-structured, and structured data that lived in silo systems. It is imperative that companies build a solid strategy around data pipelines and execution. If you put bad data in, you get bad insights out. So who's doing this well? We actually partner with Contentsquare. They're using AI and GenAI to really help companies identify insights, identify opportunities, right? They're collecting a lot of insights, and they use that information and AI to make recommendations on how to improve your business, right? Another solution, Adobe is again, I'm sure you're hearing a lot of it, how they're building AI into their analytics package. So those are two great examples. As we're moving into personalization, what we're seeing really is a shift going from sales-driven product recommendations to omnichannel one-to-one personalization. Historically, personalization has really been driving out of product recommendations, right? This is you take some web signals like page views, product data, and transaction data, and you build out a recommendation engine that basically tells you people that bought this product, also bought this product. But we're really seeing in this space is people getting more sophisticated and companies leveraging additional data sources to help optimize their algorithms, becoming better at improving their personalized content and targeted promotions. Sephora is a great company that comes to mind when we're talking about best-in-class personalization, right? They offer users tailored experience in exchange for customer data, and that tailored experience results in higher customer satisfaction, and increased revenue. So it's a win-win.

Finally, from a content generation standpoint, as there's increase in personalization, the need for content grows at an exponential rate. And we're seeing the change where companies are going from manual content generation to automating content generation. That's been a big theme today and this week, and I'm sure you'll continue to hear the advancements that's happening in the space specifically with Adobe. However, there's still a huge gap. Not a lot of companies are able to leverage this successfully, and are going to have to figure it out because in order to ensure, that your content does not come at an exponential cost as well.

Some of the areas that we're seeing here is Firefly, like we talked about, and then also leveraging solutions like OpenAI to build, copy variations and send translations. There are also companies that are building specific models for SEO or language translation, right? As you can see on the board, a survey indicated that 61% of digital experienced professionals are actively looking to use generative AI to generate images. A great case study is, Colgate, who's working on releasing a GenAI tool to help them optimize product detail pages across different retail channels. They recognize that the different types of customers, right? The Target shopper is different than the Amazon shopper, different than the Walmart shopper. So this GenAI tool helps them better get their target their customers, at the source of interaction.

So again, I'm sure a lot of you have seen this. I'm sure a lot of you are actively working on this. So you're act like, asking why we're here. But the reality is that taking that the industry-- Sorry. The reality is that, although AI has created a lot of excitement, only a quarter of businesses will benefit from GenAI-powered digital commerce. That is a big star contrast of the 28 of the 83% of companies that we shared earlier that stated that AI is a top priority for them. And the reason is that, taking AI into production is hard. AI is coming up with ideas is not very difficult, right? A lot of us probably have tons of ideas. Building prototypes, also not super difficult. But really building, the connectivity between taking something from prototype into production is very difficult. It requires a lot of operations. It requires a lot of DevOps and large investments in order to make sure that you create an ecosystem that is going to support AI in production and be successful with your customers. We have a quote from our CTO that basically says that, "In AI there is more engineering and development, than there is AI." So the companies who's doing this well, right? It's companies that are, again, investing heavily to build that pipeline, it's companies that are partnering with consultancies like us or Omnicom, and then there's companies that are buying products like Adobe that have that built into their ecosystem and allow you to do these things, right? So our hope is that, our solution in partnering with you will allow our clients to be one of the 25% that are able to leverage GenAI and AI in commerce. And that's the reason that we developed the concept of intelligent commerce.

So what is intelligent commerce? You see that these align to the trends that we've identified.

And it really is an offering that we do to help our clients get AI and GenAI into production. Starting from the left, analytics, again, data is distributed across many different systems. It's a herculean effort, I was explaining earlier to really understand how customers are interacting across all touchpoints. We have products like AEP, this has become easier. And by leveraging AI, we were able to further empower marketers to gather better insights, faster. We then take those insights, and we're empowering our customers to create more and better personalized experience across the ecosystem with solutions like RTCDP and AJO, we're able to orchestrate personalization at scale. What happens when you orchestrate personalization at scale? You require more content, right, in order to create all of those variations, of personalized experiences. So we've built a plug-in that allows us to quickly create variations of that content within the product pages, right? In e-commerce, product content is king. So this solution, really, we believe that it will help our clients get to market faster and at a lower cost.

So to dive a little bit deeper, our offering is-- Oops, sorry. I'm getting ahead of myself.

Okay.

All right, forget that that had happened. Our solution is made out of three key elements. Omni, which David explained earlier, is the largest data asset, one of the largest data asset that provides analytics insights into marketing trends, consumer behavior, and competitive analysis, and 360 cross channel media optimization. We all know about Adobe and all of its capabilities, so I'll spare you a little bit right now. I did want to talk about our Marketing Analytics Platform, which is an accelerator that quickly deploys core infrastructure, data sources, and AI models into our customers' environments. So diving into a little bit more what it does is, it really unifies first, second, and third-party data to conduct analysis, identify opportunity, and build next best action recommendation.

From a first-party data standpoint, you see the Adobe ecosystem, really capturing a lot of that, first-party data, right? You talk about web interactions, views, email opens, all of that first-party data. We then marry it with the second-party data that's coming from Omni, right? So we talked about marketplaces like Amazon, right? So we get product views within Amazon, product purchases within Amazon, and are able to take all of that information, and stitch it together. And then finally, we bring in third-party data, such as Acxiom or LiveRamp that are pulled in through our pre-built integration points.

Our Marketing Analytics Platform then leverages the pre-built models in order to create insights, generate anomalies, and give us next best action, decisions and recommendations. One of the core things to point out here is that our Marketing Analytics Platform is deployed within our client's ecosystem, right? It is very important for us to make sure that they own their own data. It's your data, your insights.

So diving into the three key use cases, we created a few a series of videos to demonstrate those uses, right? Starting with the advanced analytics piece, this capability is focused on taking disparate data sources, like marketplaces like we just talked about, and combining them into this ecosystem. So I want to set the scene before we start the video. Imagine a DTC health and beauty company can quickly analyze data and identify an opportunity, which is driven by a social event, where a football Super Bowl winning star and his pop star girlfriend are spotted on a beach vacation using a particular video, a particular product. You'll see the marketer interact with our chatbot to quickly identify the impact this celebrity sighting has had, and understand the best way to make the best out of this social trend. Let's have a look.

[Man] Imagine everything you need as a marketer right at your fingertips. Our tools give you the power to search complex data in seconds, implement key insight, and get your product out there with full integration across the Adobe ecosystem. Here's how. Let's say you run a website selling skin care products. As customers browse, AEP collects their data and sends it to Omni Commerce where it gets added to data from sites selling similar products like Amazon. This data is then sent to MAP, where it's used to build a customer profile and create valuable insight that can be accessed quickly and easily with our chatbot. For example, here we see a marketer using our chatbot to look up sales for Aquaphor. They find a surprising uptick and learn that the product has gone viral, after being spotted on a celebrity social media account. From here, we can see where Aquaphor is popular and use that data to focus marketing efforts, where customers live, work, and spend time online.

Pretty cool, I think, at least. So just we sharing some of the stories that we've done from Credera stand point. Really wanted to bring together a case study about one of our clients, a large public, pharmaceutical manufacturer, where we really helped them move from focusing on primary care providers to individual consumers. And instead of me draining this slide and because I love, movies, I will share another live case study video.

[Woman] It was a massive challenge. Leaders at a Fortune 200 pharmaceutical manufacturing company with products in 120 countries and 60 unique brands had a vision to transform their marketing approach. How can each of their millions of marketing touchpoints become remarkable, personalized, and connected? The company's CMO realized this meant shifting company's interactions with people, processes, data, and technology. So they engaged a global boutique consulting firm, Credera. To effectively market personally and at the desired scale, Credera partnered with them on two critical work streams. First, we helped the company create a MAP or Marketing Analytics Platform. MAP provided data pipelines for a constant pulse of real-time customer insights and data reporting to drive customer engagement, conversion, and lifetime value. Second, Credera then implemented a platform for dynamic content and layouts, which integrated into a headless CMS and enabled personalized landing pages. So customers became the center of every experience. Soon, this partnership generated tens of millions of dollars per quarter inefficiencies, such as retargeting spend. Credera's MarTech expertise and flexible data-first approach continue to power dynamic customer experiences that create a staggering measurable impact on client's businesses. To learn more about Credera, let's start a conversation today.

So moving on, we've gotten a lot of really good insights. So how do we activate it, right? So leveraging the same data sources we described earlier, we're now wanting to produce next best action recommendation, right? That next best action recommendation includes the product, the geography, and the channel, right? We saw that in the previous video. The next best action decision can then generate segments with an AEP that can be leveraged for activation across multiple channels. So going back to our story, in the video, you will see, how the same marketer, that is hoping to take advantage of said Football Super Bowl winning star and his pop star girlfriend, siding can quickly activate key campaigns and personalization use case across, before it's too late.

[Man] In our last video, we saw how to use our platform to gather insight. In this video, we'll take that insight, and use it to create a targeted and personalized customer experience. So now we know people in the great state of Michigan are buying the most Aquaphor and that Instagram is their preferred social media platform. We can take that information and create a segment in AEP targeting customers in that state across multiple touchpoints such as Instagram and web. Then, activate that audience with Adobe Target to create banners aimed at increasing conversion. In other words, the next time Joe Michigan lender goes online, the system recognizes him as a member of the target audience and serves him an Aquaphor banner. And when he clicks the banner and lands on your site, we can boost Aquaphor to the top of the skin care category.

All right. So our next case study is with a large auto manufacturer based out of Europe, and they were really having a lot of trouble looking to optimize their media spend, right, and drive next best action modeling. So we build these ecosystem that allowed them to take those insights and models and activate them from a real-time personalization leveraging the Adobe ecosystem. From results, you can see on the screen, it increased 65% in media conversion rates, lower cost to 30% per conversion, it increased digital engagement by 200%, and created 5:1 ROI.

Another plug, we took this solution and submitted it into the WIN with PLATFORM, which is an Adobe-led competition focused on AEP innovation, and that won second place. Again, it was an industry-specific vertical where we built models that are specific to the auto industry, and deployed it into our ecosystem, which is the same ecosystem we've been talking about today.

Okay, so now we've created a lot of these personalization experiences. We mentioned earlier, with that comes the need for additional content, right? So again, leveraging back, going back to our story, we now have to quickly generate copy variations for the personalized experience that is targeting the fans of the affirmation football Super Bowl winning star. But in reality, the fans of his pop star girlfriend, an author can leverage our AI plug-in to quickly build variation that can be leveraged on the site, as well on the social media campaigns. Check it out.

[Man] As you use the tools in the last two videos to drive users to your site and serve them personalized experiences, you're going to need something for them to actually look at. This demo focuses on how to use generative AI to quickly and easily create new content as traffic ramps up. Doing things like creating and updating product descriptions takes a significant amount of time and effort, especially when you consider things like different tones and languages for different audiences. We've created a way to streamline this process using generative AI that leverages product content such as specifications and user behavior and integrated this functionality into AEM. Variations of this content can then be published to different channels such as email and social. So that content creators can get their message out as efficiently as possible across multiple platforms.

All right. So this plug-in is actually something that is fairly new, so we haven't actually deployed it to a client. However, did want to share this story that has a similar integration with a leading energy provider where we help them modernize their architecture from a content management standpoint from an on-premise solution to Adobe Experience Manager. Part of their needs was really, improve their translation capabilities. So we integrated with a third-party that focused on translating using AI, and built into their content production so that authors could see the variation in the translation, make changes, and ultimately, deploy them. This result increased sales goals exceeded by 37%, cross-sales goals by 137%, and allowed us to migrate 4 additional brands into this modern tech stack.

So to recap, we have implemented and integrated our solution with the Adobe Experience Cloud to help our customers take AI use cases and help them deploy them online to get better insights, increase revenue, and find operational efficiencies. And I really wanted to bring you back this quote, to say that, we believe that partnering with us and leveraging our intelligent commerce solution can help businesses. Help you be one of the 25% businesses that benefit from GenAI-powered digital commerce. And with that, we'll open it to Q&A.

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