[Music] [Daniel Rios] Let's go ahead and get started here and let's start talking about this. So my name is Daniel Rios. I'm going to have Alex Jose also along with me. We're both on the Commerce product team, and we're here today to talk about Commerce Optimizer. Leave now, if that's not what you wanted to talk about, it's okay. It won't hurt my feelings. But let's go ahead and get started in itself.
Oh, feedback, sorry about that. All right, Commerce Optimizer, how do we even come up with this idea of Commerce Optimizer overall? What really is it? Before we really talk about this and really go into this, how many people attended the roadmap session? All right, almost everyone. Almost. How many people attended the lab on Commerce Optimizer for data modeling? Awesome. Awesome. So you are going to know a lot of this stuff that we're going to talk about, but you're not going to know everything, but you'll know a lot about what we're going to talk about today in itself. So before we get started, let's set up some background. Let's really talk about what it is with these experiences and what it is that really made us think about Commerce Optimizer as a product.
So merchandising, the future of merchandising and experiences that you have. Imagine walking into a store and every product in that store is made for you. Every shelf, every product, every experience is catered directly to you to what you really wanted to find. Imagine that happening for you. Why not? Why can't it happen? Why shouldn't it happen? But imagine also that you have customers, and the customer has a preference for, let's say, hiking. Why do you not give their experiences all around hiking and all around outdoors? Or why do you not, if you know someone likes blue shoes or just likes blue in general, why do you not give all of their experiences with blue? Or with orange or with every color. That really is the power of personalization. But it really centers down onto merchandising and the power of merchandising. And really what we drive to in Commerce Optimizer is really being able to say, "What does the merchandiser do, and how can we work with the merchandiser to drive that?" So when we first started this product, we started thinking, "Okay, what is it that our customers are having problems with? What is it they're really trying to solve here?" We listened and we heard our customers. We can't re-platform. We spend millions of dollars in doing our current Commerce platform right now. We have too much connection, we have too much connectivity, we have too many integrations. It's very complex. It's tied to our ERP, it's tied to our CRM. We've heard everything. We can't afford to do it right now, but we still want that optimal storefront. That's what, hopefully, Commerce Optimizer will solve for that customer. Or we wanted to drive higher traffic and improve conversion rates on our storefront. Well, again, that's what Commerce Optimizer is here to really drive you towards. Or you want to grow faster and we really need to support more complex use cases. For those of us who went to the data model, to the composable catalog data model lab, or heard about it, you know that we've driven this data model to really be the heart of Commerce Optimizer and be the backend. So we can say Commerce Optimizer will help you drive and be able to utilize those and solve for those complex use cases themselves.
But let's take a look at what Commerce Optimizer is from a high-end level. Let's look at it from a 10,000-foot level. You can leverage your existing commerce solution backend. No need to re-platform. Take what you have and we'll utilize it. Instead, we'll ingest your product data and your catalog data and bring that into Commerce Optimizer. That could be wherever you're storing that data. One system, multiple systems, doesn't matter. We'll ingest that data into Commerce Optimizer, ERP, PIM, commerce system, we can bring it back to it. You can drive revenue growth via highly performing personalized storefront because included within Commerce Optimizer is our Edge Delivery storefront. And then you can drive those personalizations and those personalized experiences that I talked about at the beginning utilizing AI generative functionality within Commerce Optimizer itself. That's Commerce Optimizer a really, really, really high level. Let's drive down and see what are the components of Commerce Optimizer. All right, so everything you see in the middle, that is Commerce Optimizer and it's architecture and all the components that are within itself. So everything within the white box is Commerce Optimizer. On the left is your existing commerce platform. No need to touch that, no need to do that. And on the right, on the top right, is your existing cart and checkout. We're not going to mess anything with those two functionalities. Instead, we're going to take your experience layer and drive towards your experience layer and change that, and let us use the best of Adobe to drive that experience for you. So let's start at the bottom left hand side. We can just catalog from multiple sources, whether that be your existing commerce system, a PIM, an ERP. Hey, we've even seen spreadsheets where people store all of their data in it. We can ingest that into our catalog services. Our catalog service is powered now by our new compostable catalog data model utilizing catalog channels and policies. Alex will go into more detail about what are catalog channels and policies and into more detail into merchandising just in general overall. Also within this area for our merchandising are product discovery and product recommendations both being powered by Sensei. We utilized these before. We've proven out the value that they drive and we continue to drive value from them. The one key difference now is that those product discovery and product recommendations are going to be utilizing the new data model. So if you think about this, you can talk about channels, and those channels being able to utilize, to personalize, to drive it all the ways to search and recommendations.
That makes up the components of merchandising and our merchandising services powered by catalog channels and policies. Well, next, let's go up to the storefront. And in the storefront, you get storefront powered by Edge Delivery. So last year, it was introduced Edge Delivery. We've proven out over the year about what's going on with Edge Delivery and how we drive power and derive that performance storefront. We'll go into a little bit more detail about that. But it comes also within Commerce Optimizer. One key thing is this is one SKU, one product. You get all that is in this white box. It isn't separate products. You don't have to go and get another contract. There's none of that. It comes within Commerce Optimizer itself as a single SKU, no integrations. It's already fully integrated with each other, so that we took care of for you in that respect. So Commerce Optimizer, as we see it, really drives that value. But we didn't stop just at the storefront. We went ahead and added the powerful, the product visuals powered by AEM assets. So really bringing in the best of assets, bringing that into Commerce Optimizer also. And then we took the site performance agent. How many people were there for the introduction of Site Optimizer...
Powered by AEM? All right, so I'll talk a little bit about it. So Site Optimizer powered by AEM is a new product that's been due. And what this product does is it identifies and then gives you suggestions on issues to your storefront. So if you have broken links, if you have things that aren't for accessibility, those are all identified for you via an AI agent. And then it gives you suggestions in order how to fix it. What we've done here, if we take in the site performance AI agent and introduce it into Commerce Optimizer also, so that it powers into the storefront and helps you find those issues that you have with your storefront itself. Then the last piece of Commerce Optimizer is reporting. Everyone forgets about reporting. Everyone always tries reporting and they never think about it, but at the end of the day, we need to know what's happening. And that's where we give you before and after metrics. And really, this is reporting to drive what you need at the time that you first started utilizing it and at any point in time after that. You can see what's going on with important KPIs and metrics that you want to drive towards. All of this is what makes up Adobe Commerce Optimizer without touching the backend or really touching the existing cart and checkout because all we are driving towards is the experience layer at this point.
So this is just another view of how Commerce Optimizer looks. You'll continue to see this out today throughout the presentation as we hit different sections as we deep dive into each one of these. So as you see, everything I talked about is here with the existing commerce backend. And now let's go and deep dive a little bit further into the product. And to that, I'm going to hand it over to Alex so he can talk to you about merchandising services powered by catalog channels and policies.
[Alex Jose] Thanks, Daniel. And it's really nice to see a lot of familiar faces here from the lab yesterday. So you're going to hear a lot of information that you heard yesterday plus new items. And for the folks who are hearing this net new, you're in for a ride because you're going to hear net new concepts which we are introducing as a part of Commerce and we are really, really proud of our new catalog data model. But before we jump into that, it's really quite important for us to understand the journey that they made over the past five to seven years. In many ways, we started off with the foundations of catalog modeling in 2017 and 2018, and this was introduced in Commerce Core, and this really laid the foundations of everything that has come since then.
After this is where we wanted to really supercharge the experience for our merchants and increase the speed with which we are able to really deliver these experiences, and that's where catalog service really went cloud, all of it, all the data was delivered under 200 milliseconds. And this is where our catalog services really expanded on to absorb and inculcate the world of AI using Adobe Sensei. And this is where we introduced Live Search and Product Recommendations which are really behavioral data-driven experiences from a shopper all the way from top of the funnel till the shopping cart in itself. This is the world that we live in right now, but when we look into the future, we want to build our catalog for the next generation. We are thinking far into the future about scale, orchestrating it, keeping things simple while making it business friendly for all of our users. And that is where we are looking at when we look at 2025 and beyond. And this is where we are introducing our new catalog data model, which is called as catalog policies and channels because the world that we live in is really expanding very fast, significantly enough such that every single day, there's increasing customer expectation, there's a lot of pressure from all of our competitors from an e-commerce space. At the same time, the technology is moving very fast. Now what happens in space such as this is that if a merchant is not able to adopt to the latest e-commerce platforms and technologies, they literally just fall behind and lose revenue. It is very important for us to really empower our merchants to grow across geographies, unlock new markets, and do this in a simple way. Now when we speak about going across geographies, we are looking at a macro level. But when we expand in a macro level, we also need to think at a micro shopping experience for each single user and shopper. And that is where we really look at localized shopping experiences, and we want to do this at scale without duplicating our catalog data and making sure that the experience is same across all of our channels, making it simple. But while those are the goals, there are a lot of challenges which are faced by our merchants. In order to implement this today, growing across geographies and across spaces, there is highly customized implementation which are available. The total cost of ownership increases. There's rigid catalog modeling concepts. We no longer want to put a square peg in a round hole. That's the analogy I always go with when I speak about our new catalog modeling concepts. Instead, we want to give you building blocks. And soon, as a part of our demo, you're going to be seeing what we mean by these building blocks and how you're really able to scale beyond what is available today.
So with that is where we introduce our new catalog modeling concept, which is catalog channels and policies. And this is really built for scale for B2B for you to expand in a very simple and easy fashion. And we are very excited to position this as the future of catalog modeling in Adobe Commerce. When we speak about making B2B simple, what we are really looking at is unlocking catalog syndication, removing data duplication when we grow across geographies and business units. We have also introduced the concepts of dedicated price books. Now what does that mean? We have decoupled the concept of prices from a SKU. So one SKU can now be associated with multiple price books, which can be expanded at scale and across geography, so you're really able to deliver those unique catalog level personalized prices per shopper, per geography.
All of this is great. We have promotions at scale, we have unlock catalog syndication, but what does this mean at day zero when you implement your new catalog? And that is what this is. This is really the slide that you're looking at with respect to the impact that you will have in your catalog. We now have the ability to scale up to more than 250 million SKUs in one single catalog instance. All of this is supported with 30,000 prices per SKU, which is incredible because previously, this needed a high level of customization for you to implement.
All of this is, again, supported with extremely fast promotions across all of your SKUs for millions of SKUs per hour. And we are really happy to say that these are not testing numbers, these are not theoretical numbers, these are numbers that we have implemented with some of our strategic clients to ensure and validate what we are building out over here. And this is really the new catalog modeling that we are introducing today.
So let's take a closer look at what this means in the real world and understand how we are able to really incorporate this from that perspective and also take a closer look at the building blocks that I spoke about.
So this is a slide you would've probably seen yesterday in the roadmap session. Let's dig deeper into it. Let's understand this a little better and really see how we are playing around with our new catalog concepts. Now consider this as Carvelo Automobiles, which is a large conglomerate. Think of them in the real world to be something like a Toyota or a General Motors. Now General Motors typically has several different brands within it, maybe Buick, Cadillac, Chevrolet. All of these brands have different cars, which means they have different parts, and different parts are shared across all of these brands in itself. Typically, a Chevrolet as well as a Buick shares the engine but they have different suspensions and brakes. That is a layer of complexity in the real world. Let's take it a step further. We have multiple dealerships which sell all of these SKUs, which Chevrolet or, in this case, Carvelo really wants to sell.
All of these dealerships will have different licensing agreements. They will probably have access to sell parts from 2015 to 2022. They will have different prices available to it and, in this case, in the LA dealership, you see all three brands A, B, C is allowed to be sold, whereas Houston and Portland can sell only two SKUs. All of this is what we want to normalize, make it simple and take away the operational challenges behind it because we want you to really focus on the shopper and not on your internal operations while setting up your catalog. That is our intention behind simplifying our catalog management. Let's take a look at what this would mean. Once again, this was covered yesterday, but let's take a closer look at the complexities here. Ultimately, let's take a simple example of six million SKUs. In this case, Carvelo has a national storefront, which you see right on top, and then you have a dealership level storefront which is available at each of these different areas. In the traditional classic world that we live in, in order to accomplish this, you would duplicate your SKUs and this would lead to 20 million SKUs. We are getting rid of that. We are going away from the concept of effective SKUs, and you will just always use a single base catalog to deliver all of your experiences.
Previously, promotions used to take weeks in order to load them up across all of your SKUs and making sure all of them are available across different geographies. We are getting rid of that. We are doing this at scale. We are able to deliver incredible speeds through our entire ecosystem because data is now going directly into our catalog services, skipping the entire core commerce because, like Daniel mentioned, we really are the storefront level which supports top of the experience all the way to the checkout. Lastly, I would add on is where, again, the unique prices per SKU come into picture. And this is again interesting to see. We are able to do this with the same six million SKUs without duplicating our catalog with the help of price books. Price books is a concept that we are introducing at the catalog ingestion level. So you will ingest all of your price books from your existing commerce system and you will have a copy of those in Commerce Optimizer. And at the point of the storefront is where you really kick in the respective price book, which is relevant to that particular product or SKU. So with that, let's take a closer look at this demo. And we'll go through this very slowly unlike yesterday, so you get a very good idea of the product that is being built. On the extreme left is where you see our three main sections, which is really merchandising, catalog, and data insights. Now under merchandising is where we have really merchandiser specific services which is built around product discovery, which is an amalgamated and a higher level implementation of what was previously called Live Search. Then we have Product Recommendations. Then comes the area of catalog management, which is channels and policies and we'll take a closer look at this. I also want to highlight very importantly the Data Insights tab which you see on the left. We'll come to this in a moment towards the end of the slide of how it really helps you out because ultimately, data insights helps you understand what is your status of data when you're ingesting it from multiple different sources. This is going to be very important because, for Commerce Optimizer, that is the world we are entering where your source of catalog is not just one catalog but many different sources. All of it get ingested into Commerce Optimizer and then syndicated onto all of your storefronts which are available. So let's take a closer look at catalog in itself. We go into Policies, and let's say you want to launch a new dealership. You want to launch the Houston dealership. So for this, you go ahead, click on Create a New Policy. But like you remember, the Houston dealership has access only to two brands among the three brands which are available. So Houston has availability only to brand B and C...
And it is replaying right now, so we'll just give it a moment.
At this point when you add the filter is when the magic really starts. You can see that we are using a SKU attribute to really build out this experience. And let's understand this a little better. Our catalog modeling capabilities are really attribute-driven catalog modeling. Every single SKU attribute that you see is what you essentially see on this page over here. So you have an attribute called as brand, and every SKU which has this attribute and has a value of either brand B or brand C is automatically added to this policy. So like you can imagine, policies essentially are of filtration concept which keeps things simple.
At this point, we have created one specific policy for the different car parts. Now we wanted to create one for the part categories in itself. That is where we use another attribute called a spot category. We create the filter around it and we want to give the licensing availability only to brakes and suspension for this particular dealership. This is where things start getting interesting because we can create multiple different policies and really understand and create those visibility rules specific to a geography, business unit, or any other entity that you might have.
At this point, you have the ability to create multiple different policies, but now we move on to the channel, which is really our highest level abstraction, which is available in this new modeling concept. In this case, it is a specific geography which is Houston and you go ahead and create the part category around it. So you've added two policies to this specific channel. You really have the ability to blow it up and add as many channels or other policies to one specific channel when you're creating your catalog model. And that is where things get interesting. If you want to expand to new geographies, you want to build out specific rules or access rules which are only available to that specific area to ensure that you're meeting the geographic requirements.
Now with this, we have launched or created a new channel and this is where we go into the document-based authoring, which is supported by our commerce storefront powered by Edge Delivery Services. We have kept things very simple. We have a dealership name, a channel ID, and this is an existing dealership. We are looking at Los Angeles right now. We search for brakes, we see the SKU experience, we see products across all three brands which are available, the different brands that are available in different colors, which is purple, green, and red. And you can see a total of 179 products.
Our intention over here really is now to launch a net new channel or a dealership using that same base catalog without duplicating the experience or the catalog when you're launching that new experience.
So this is where we continue with our video and we go ahead and update our configuration for the Houston channel.
At this point, we go ahead and update the name of the dealership, call it Houston, and this is as simple as picking up the channel ID from the configuration that we created in Commerce Optimizer, changing the channel ID, executing a new price book. This is where the price books and the SKUs come together. We publish it. You see a new experience, but what we really care about is the catalog over here. When you search for brakes, you see a big difference in the catalog. You see a total of 59 products as compared to 179 we've just previously seen. Here is where the different prices and the different SKU visibility come together, and you see the different experiences using that single base catalog. This is once again why we say we are getting rid of the concept of effective SKUs. Instead, we are going to use a single base catalog. Make your catalog management at scale scalable and keep things simple. For the same experience, if you really want to change the price book over here and change it to a VIP price book, it is as simple as implementing that at a price book level. And the correct SKUs get picked up with the correct prices. As you can imagine here, a very important fact over here is that for promotions at scale, you can always keep all your prices ready just before the promotion. And if you have a sensitive promotion which is available just for three hours, you just need to kick the correct price book in. Everything works as expected, and at the end of the three hours, there is no processing required. You kick back and use the old price book which is available in your system. That is how we are able to support enterprise scale clients in this architecture.
Let's take a bigger look at how this catalog modeling would really work across geographies. Let's take the example of L'Oreal. Consider your L'Oreal who has multiple different brands like Maybelline and many other brands such as A, B, C as called out over here. You have a single base catalog, but now you want to expand to Canada. You create different channels for that catalog across all your geographies. Then you implement the price book which is relevant to that specific geography for that specific SKU without duplicating it. And the incredible thing that you're going to see over here is that you make the change once in your base catalog and that is syndicated out across to all your destinations. And that's the change that we're trying to make over here.
Let's take another example, and this is going to be refreshing because this is something you did not see yesterday, and this is the example of a marketplace. Consider your Best Buy, consider you have multiple different dealerships or sources of parts which are coming in. Here, company A and company B can really be Sony, Samsung, or a third party reseller. You have the ability to ingest all of that data, aggregate it, normalize it on a base catalog, which is our compostable catalog data model in Commerce Optimizer, and then syndicate that out to all of your different destinations which are available. Now this can really be a marketplace website, it can be a point of sale store, or really your advertising partner. The interesting thing that you see here, is for each of these destinations, you have the ability to execute and implement a unique price book. You do not need to duplicate that SKU, just use a new price book, implement the right policy, make sure your advertising partners have access to the right catalog, and you really syndicate at scale. We want to make this easy, we want to make this simple, and once again, we want to give you the ability to focus on the shopper rather than focusing on the orchestration of catalog modeling when you're really growing across different geographies.
With this, you can really imagine that there is a lot of integrations which are required. Other data ingestion, one, and secondly, while you are handing off the session during the checkout. As you might recall, Daniel had initially showed us one screen where the data ingestion takes place, and during the checkout, there's an experience where it's given back to the whole system. This is where we want to make things simple. We are building out pre-built starter kits with strategic partners such as Salesforce and SAP such that you can have one-on-one interaction with these platforms. So if you have a Salesforce backend system, we will provide these integration starter kits which are available on Adobe Exchange, plug and play, ingest the data, and you're good to go. But consider that you have a homegrown system and you have a system which is very unique in itself. That is where we introduce a development SDKs to really accelerate this entire development process.
Ultimately, during the checkout process is where you have API Mesh and App Builder to really help you normalize and simplify that handover of the session from your cart to the checkout. And all of this is going to be supported with Adobe best practices to make sure that you're really well equipped for this entire process.
The last area that I want to touch upon before I hand it off to Daniel is really the data insights and catalog observability at scale. This is the section on the left which we spoke about a little while early. And the importance over here, if I bring your attention to the left, really is the fact that we have the ability to give you full observability at your storefront of all the catalog that you're ingesting from multiple different sources because, finally, you are going to be using Commerce Optimizer as the source of truth which has normalized all of that data. You'll be able to validate the prices, you'll be able to validate the SKU source and use Commerce Optimizer as that normalizing platform before you syndicate out all the content. Next is personalization intelligence. We find this very interesting because you're all aware of Live Search, which is now called product discovery and Recommendations in itself. And more often than not, we get a lot of questions on, "Why is my personalization recommendations behaving the way they are? What can I do to improve the recommendations? How can I make my recommendations faster?" And that is why we are introducing this observability capability where you really understand what all events are being captured by the system. And more importantly, what is the health of all of these events? Because without really having full observability into that, you cannot be sure if your merchandising strategies are working as expected.
This really brings us to the next section around product discovery, which a lot of you would already be familiar with. This is powered by our Adobe Sensei machine learning platform, which really gives you that personalized experience which is fully driven by our merchandising capabilities and by tracking user behavior. It's very interesting to note that we have added a couple of new capabilities here, which you might not have seen compared to last year, which is really the ability to search between across layers for a particular SKU. So if you're a B2B merchandiser and you want to implement layered search, which is contextual search, we have introduced that, that's currently in beta. Please check out Experience League page. This is a new improvement in product discovery. Next is recommendations which, again, you're familiar with in this space. We have the ability to define 13 unique recommendation types, but what is interesting in this space and very new is your live search and recommendation rules are now going to be driven based on different channels. That is the future that we see for the space, and that's going to be very important where you want geography-specific personalization, driven and making that simple for all of your shoppers.
With that, we come to the next area, which is really our storefront and all of the APS services and the catalogs and channels that you just saw feed into the storefront layer. And for that, we have Daniel coming back on stage to cover this section. All right. Thank you, Alex. So great stuff. And now you know what kind of is the heart back of Commerce Optimizer itself is really that data model and really the catalog and merchandising services. That's really where we start from. But really, that's the backend. That doesn't do much for your experience layer. It helps to drive your experience layer, but it really doesn't give that story and doesn't really reach to where your customer's at. And that is why in Commerce Optimizer, we also have commerce storefront powered by Edge Delivery and it'll be included within that single SKU itself. Included within that is both the visual editor and the doc editor for efficiently managing content in itself. Also included within that is our boilerplate, which is basically a templated experience so that you can start to drive all those personalized experiences within that. Within that boilerplate are different drop-ins or components, which will be the PDP, the Product Recommendation, search, account end user, or PL and also the PLP. Or if you need to build your own customized drop-ins, we also have an SDK to help you to drive those drop-ins directly for you in itself. So all of this packaged within Commerce Optimizer to really drive that experience layer and drive that experiences to your end customers so that you can drive that personalization, and at the end of the day, being able to drive conversion in itself. Also within this is the experimentation. So within its delivery, we'll also bring across the experimentation so you can drive and set up within minutes some experiments, really utilizing the power of Sensei, again, in order to be able to drive recommendation, drive experimentation driving through. You can drive your experiments through pre-consented traffic in itself because it gives you the capabilities of doing that. You can arrive audiences with different audiences, whether that be location, devices, new visitors versus returning visitors, segmentations, any way you want to cement and drive that experiment. It also allows you to allocate traffic. So you can allocate traffic driven evenly or unevenly across and really being able to drive those experiences. So an important part of that experience there being brought into Adobe Commerce Optimizer with being powered by the Edge Delivery.
So Edge Delivery has been around for the past year and we've been able to see what is the power behind it and what really drives it. We've already seen 4X faster page loads, and utilization of Edge Delivery. We've seen 15% increases in organic traffic and, of course, those Lighthouse scores of 90 and above, all that we've already proven now in over the past year with Edge Delivery, and that is packaged directly with Commerce Optimizer's itself asset. But the question is, do I have to use Edge Delivery? No, you don't. Commerce Optimizer also comes headless. So you can use whatever storefront that you really want to utilize and utilize the power of our catalog and our merchandising services along with Commerce Optimizer and you don't have to use Edge Delivery, but you won't necessarily get the benefits that our Edge Delivery storefront comes with in these concepts.
So I touched briefly on Edge Delivery in itself. There's a lot of talk and there's a lot of conversations going to Edge Delivery, there's a lot of sessions that cover it. So if you want to learn, deep dive more, I would advise going to those sessions and being able to really get more understanding into that. But the next part about that I want to talk about that's included in Adobe Commerce Optimizer is bringing parts of assets, AEM assets, into Commerce Optimizer. And that's product visuals powered by AEM assets. This is bringing really the power of Gen AI and bringing that into it so you can personalize utilizing variations so you can easily generate variations using the Gen AI with Adobe Express and Firefly. This is already fully integrated within the product and is part of Adobe Commerce Optimizer. You can associate images with products inside of Adobe Commerce Optimizer and then drive that personalization. So in the use case that we're seeing running in the background, you can drive orange leaves. By typing in orange leaves, we were able to change the overall experience and drive different variations of that image to have orange leaves and then drive that directly into Commerce Optimizer in just minutes. So you can out front-- You can then update your storefront assets in minutes itself. And being able to drive those personalized experience, which once again, when we come back to conversion, drives conversion because we know from our previous experiences that driving a personalized experience helps to increase your conversion overall.
So that's really where we say we're the storefront in itself, and I talked earlier when we first started about site performance AI agent. Site performance AI agent is really one of the newest products that was introduced by AEM and it's Site Optimizer. So Site Optimizer, like I said earlier, really is to drive and find those issues with your storefront. But it doesn't just look and find those issues, the AI agent, but it also turns around and gives you suggestions on how to fix those issues. Later versions of Site Optimizer AI agents will be that it'll actually fix those issues for you. At this point in time, well, we've brought to Adobe Commerce Optimizer is identify those issues and suggest how to fix those issues. Well, what type of issues do we cover? Well, you've such things as backlinks that aren't working correctly. If accessibility issues with your site, it'll identify accessibility issues. If you're having high bounce rates, it'll find those high bounce rates. If you're missing images, it'll find that you're missing images within your product itself. All normalized use cases that drive your experiences and really net affect your overall experience that your customer will have, the AI agent will be able to find those quickly for you and be able to give you those suggestions to fix those promptly so that you don't lose those eyeballs because we know if a user lose, user's attention span is even less now in the digital world than it was before and it continues to drop. So if they don't have a good experience, then they're not going to come back to visit the site itself. We've seen already organic traffic increase in one month by 4% by utilizing Site Optimizer, and we've also seen a 19% increase in Core Web Vitals overall.
So, so far, we've talked a lot about the storefront, we've talked a lot about being able to fix that storefront. We've talked about our merchandising services, we've talked about the catalog. All of this, as I said, one SKU within the product, fully integrated, seeing the product work itself. The last piece that I'm going to cover on here is the before and after metrics and the reporting. You also get the reporting within Commerce Optimizer itself. The before and after metrics are really all about being able to say what is currently happening within my site. What point in time and what point after what has happened? If I made a change, if I've ran a new promotion, anything that can drive more eyeballs to the storefront are being measured in this. So these before and after metrics and this reporting really helps you to drive to the experience and really find out what has been the net effect, so you don't have to go looking somewhere else. This is available right in the product itself and made available for you.
Overall, this is what Adobe Commerce Optimizer is. Really, existing commerce backend don't change. Keep your current existing commerce. Don't try to change that. We don't want to change that. We're here to look at your experience layer. We're here to change your experience layer itself, keep your backend, keep your cart and checkout. We don't need to affect your cart and checkout at all. What we want to drive in Commerce Optimizer is your experience layer by driving it with our merchandising services, our new catalog data model to really build out those use cases that you have to be able to then drive and drive it directly to the storefront so you have a fast performance storefront using Edge Delivery. And then utilizing the site performance agent and the before and after metrics to really find out what's going on with your site and being able to fix any issues that you have with your site overall. All of this in one product, bringing the best of worlds from Adobe into a singular product so that you can drive your business and drive conversion higher.
So quickly, we are also looking for Early Access providers. We have some people that are already in our Early Access, some merchants. We're looking for additional merchants to be part of our Early Access. Commerce Optimizer goes GA in June of this year. But right now, we're in Early Access adoption. So if you want to be part of the Early Access products for Commerce Optimizer, you can go ahead and fill out our form and then be able to drive-- Be able to figure out what we're driving towards in that area.
And last but not least, take the survey in the Summit app for a chance to win either a Starbucks or the Bose QuietComfort headphones. Yep. With that, thank you, everyone, for attending. - Thank you. - Have a good rest of your Summit.
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