[Music] [Rohan Bhatt] All right. Hello, everyone, and welcome. We are so incredibly excited to have you here. We'll bring all of the energy for you since we know it's four o'clock after a few busy days of summit. But we could not be more excited to be here to talk to you all about how you can unlock your data to create really compelling personalized commerce experiences. So I wanted to kick it off with some introductions just so you guys can get to know us just a little bit better. Vinay, do you want to kick it off? [Vinay Gopinath] Hello, everyone. I'm Vinay Gopinath. I manage the advertising and marketing technology products globally, in Coca Cola. - Kevin? - [Kevin Haag] Hey, everyone. Kevin Haag, vice president of customer data platforms at Bounteous and Acolyte, an Adobe partner. So I lead the teams that are focused on strategizing and implementing Adobe's data related solutions. Real time CDP, Adobe Analytics, Customer Journey Analytics, as well as Adobe tags. Awesome. And I'm Rowan Bhatt. I am a senior product marketing manager on the Adobe Commerce team focused on the area of personalization. So all of the personalization goodness that we've built directly into Adobe Commerce, as well as integrations across all of Adobe Experience Cloud, which we'll talk a lot about today. So I read recently that the Coca Cola logo is one of the most recognized logos in the world. So I'm not sure Coca Cola needs all that much introduction. But maybe, Vinay, if you can just set the groundwork for us, that'd be great. Sure. Thanks. Thanks, Rohan. So I'm sure everyone at some point in time would have engaged with all the Coke products that we have, especially the folks here from Pepsi. Sorry. Just kidding. Jokes have a lot. Right in front of center. All right. There we go. Great. So but Coke as a company, we have billions of transactions happening every single day, and the whole purpose here is to quench our consumers' thirst. Similar brand that we are going to talk about today is someone who has ecommerce presence in LATAM, especially in Mexico. And they are also trying to do the same thing in Mexico and elsewhere in LATAM. Awesome. So when we think about setting up a successful personalization program, there's really a few pieces that go into that. First is, how do you define a clear personalization strategy that's going to work for your organization? And the second is then, how do you phase that out and actually execute on it? So with our time today, we want to talk about both of those, lay out some frameworks and some tactics that you can take back to your own organization, apply, and then create really effective personalized experiences. Now we've already said the word personalization maybe 10 times in the first minute here. So I think it's important to address why are we talking so much about personalization, and why is it so important to get right. So Vinay, I'm curious to get your perspectives on that. Sure. So few stats here. So 70% of the consumers, when they're shopping, they are multitasking. Gen Zs, their attention span is just eight seconds. And millennials, four seconds more, which is 12 seconds. So it's important here to capture the right attention of our consumers at the right time and at the right moment. Hence, something similar that we are trying to do here with the NT Hooker brand is that if we want to drive revenue, we have to use the right set of datasets so that we can personalize that experience at the right time and at the right moment with the right content. Yeah. Absolutely. And I'm curious to hear from you, Kevin, on sort of the impacts that this can actually create for businesses that you work with at Bounteous. When organizations and brands really not just meet consumer expectations, but really exceed consumer expectations, they can unlock a tremendous amount of business value that's measured in increased loyalty, increased purchase frequency, as well as increased average order value. So it's worth doing personalization. Absolutely. And so, I want to then start out by diving into the first pillar of those two that I mentioned on setting the strategy and then execute on it. And I really like the four steps that Coca Cola and Bounteous pursued together. So the first of these is setting your team structure for personalization success, then centralizing all of that rich data to get a really clear picture of who your customer is and the audiences or segments that you want to deploy the experiences to, then determining a prioritized list or set of use cases that you can actually go and implement, and then finally, executing on those use cases. And we'll talk about each of these steps. I want to start off with really setting up the team structure for success. And, Kevin, I'm curious to hear your perspectives on this one. Absolutely. So, yeah. Assembling the team, preparing for the journey, that was the logical first step. And what we intentionally wanted to avoid is what you see here visualized on the top half of the slide, which is your typical like status quo linear handoff approach. This approach where you have one or multiple external partners brainstorming with the business owner of a brand, creating a roadmap, creating a bunch of excitement, and then handing it over to a brand's technology and IT team, is inherently linear. And typically at the handoff towards IT, is when the reality check happens, because then IT needs to prioritize the asks, feasibility check the asks and more often not really push back on the business because the data is not there yet, the systems are not integrated yet, or there are other high priority items for the technology teams. So we kind of flip that on its head and instead did a true collaborative approach that starts with use cases. So we created this collaborative set of use cases and aligned across all the business and IT stakeholders as a rallying point. So we all knew what we're going to do across what channels and how. And that alignment and sign off throughout the project not just in the beginning, that really accelerated our path to value. Because we are all facing the same direction, we all know what our role was in the process. Yeah. That makes a lot of sense. And I think oftentimes we see companies out there who have set up their teams in a way that's highly collaborative and maybe they've set clear goals around their personalization strategy and what they want to achieve, but when they go to actually do things and execute on that, they run into a wall. And so I'm curious, Vinay, to hear from you. When you first started trying to shape up what personalization would look like for the en tu Hogar brand and started drafting these use cases, what were some of the challenges that you experienced around this sort of data piece? Sure. So to build a multi moment omnichannel experience, we need data, and we need behavioral data. What problem that we were facing was that our behavioral data, the web analytics system, was not seamlessly integrated with our Adobe stack. So hence, there was latency, the data tagging, because web analytics tagging and your Magento tagging or commerce tagging of the site are completely different. You're tagging at a macro level just to understand how Adobe analytics data looks like. So that was the biggest gap because of which we couldn't orchestrate a truly omnichannel experience. And that also resulted in a use case by use case approach, which added more time in delivering all the use cases, and we couldn't unlock all the capabilities that we could use. Yeah. Absolutely. And Coca Cola is not unique in that challenge. Only about 25% of businesses out there actually have a 360-degree view of both the behavioral data, so clickstream data of shoppers on the site, as well as the operational customer data that really creates a robust profile. And despite that, only about 50% of companies actually have some sort of unified platform or experience management solution in order to be able to create the integrations and ultimately the experiences that they want to create. And so Kevin, I'm curious to hear from you a little bit about the value of having sort of a unified set of solutions that makes this work? Yeah. You know what standing between brands and ultimately the customer experience that they want to create is typically a business capability gap. It's usually not lack of ideas on what we want to do, it's the inability to do so because data is siloed, data is not integrated for analysis or activation purposes. And so at the end of the day, it comes down to unlocking those business capabilities and we're in Adobe conference, so we'll obviously talk about how the Adobe Experience Cloud can unlock those fundamental business capabilities that we need to have unlocked for use cases of all kinds. So Rohan, if you don't mind just spend a few minutes walking us through the experience cloud. Yeah, absolutely. And I know hopefully you saw a lot about the Adobe Experience Cloud more broadly during the keynote address. But I think it's important to really consider all of the building blocks that are a part of this. So we have digital commerce with Adobe commerce, we have experience management solutions, customer journey management, rich analytics, and marketing operations. And, really, the power of this that a lot of these applications are built on Adobe Experience Platform, which I like to think about really as a sort of common data layer that operates on a shared data schema or experience data model. And that allows all of these applications to integrate and work really nicely together in order to allow you to deliver these types of experiences. And I think the magic of this for Coca Cola and with support from Bounteous was Adobe Commerce Data Sharing. So this something that we launched over the last year or two, which really allows you to collect behavioral data as well as back office and profile data, and in real time, share that data through an extension called data connection to the Adobe platform edge network, which then flows to all of the different applications that you see here, to unlock a broad array of different use cases. That's incredibly powerful because that data can serve as an input for things like unified customer profiles or audiences within real time CDP, or as an input for reach analytics within customer journey analytics, or that data can also serve as a trigger for things like campaigns within Adobe Journey optimizer, like an SMS, push notification or email, or delivering the next best experience within Adobe target, and, of course, personalization within commerce. So there's a broad array of different capabilities that you can unlock when you're sharing data seamlessly across your solutions. I'm curious to hear a little bit from you, Vinay, about sort of where you see the value here for Coca Cola. Sure. So I would like to divide this into two parts. The first is, the data part. Now what's great about this integration is that the way the website was built using commerce, now I can capture every single data points now through this connector. The other thing is not just the behavioral data, but what happens after checkout. Those data points can also flow in. And those data points are then eventually leading into the Adobe Experience platform. Now from the technology side of things, you touched upon few of the technologies like AJO, AEP, RTCDP. But what's great is Adobe Target gets unlocked as well. So you're not just sending emails or SMS or push notification, but when they land on the site or on the app, you're personalizing their experience. So that is actually very helpful to orchestrate a truly omnichannel experience. Yeah. That's great to hear.
All right. Well, use cases. We talked a little bit about use cases. Now use case in our experience is a term that is used quite inflationary. You can go broad, you can go narrow. So, when we talk about use cases in the context of Coca Cola here. We really define use cases as bite sized experiences that we want to create for consumers. Consumers can be external, the actual consumer purchasing the Coke products. Consumers can also be internal, towards the Coca Cola brand, because there's also a lot of net new capabilities and insights that we were able to unlock by first establishing that unified profile and analyzing that joint data set for the first time and become smarter. So we tackle both consumer facing use cases that is Coca Cola's customer, as well as internal consumers which is Coca Cola the brand.
Now when we talk about use cases and the use case mapping. Like I said earlier, we wanted to go beyond the broad definition of what are the real time personalization one to one interactions ideally AI and ML driven across all channels at any point in time. That's great. That's aspirational. That's a North Star. It's something inspired to do. But at the end of the day, it comes down to creating specific experiences for the customers depending on where they are at in their consumer journey. So when we started to strategize those use cases, there was the business element to it and that's what we're looking at right now at the slides. So in our definition, we wanted to acknowledge the clear data needs. Like there's a specific consumer interaction or a consumer graduating or being incorporated in an audience that pretty much kicks off a journey. So that's the first piece, when piece. Then there's our specific reaction to that consumer behavior. That is the marketing initiative, the personalization that we want to drive. That's the I want to column. And then last but not least, the so that column, that clearly links business outcomes and KPIs to the actual personalization tactic. As you can imagine that is the business side of the use case definition. On the right hand side there is also the technology deep dive into those use cases. There's a few proprietary systems in there so, for today's conversation, we're not going to double click into the Coke specific systems, but just know that there was this very granular mapping of data elements of systems that need to be connected together in order for us to actually launch the use cases that we'll take a look at in just a second. After that was done, we prioritized the use cases in an effort impact matrix to pretty much determine what to tackle first, what to tackle next and what to tackle later. And I love that idea that the use cases here were really a rallying point for both Coca Cola and Bounteous. That's where a lot of the effort started. It helped create the team alignment we talked about earlier, and it helped inform what data was needed and when and how to connect to all of those data pieces as we talked about earlier. So the next part of this really thinking through, once we have a set of use cases, how do we bucket them, and then how do we start to tackle them in a way that makes sense? And I think this framework really helps to structure the use cases, for a Coca Cola, and this really centered around the customer journey. So part one of driving revenue through personalization, I like to think of that as sort of the center or the meat of the customer journey, where you have a customer actually engaging directly on the site and purchasing. The second piece of this around deepening ongoing engagement retention, I like to think of that as sort of the edges of the customer journey, where we're first acquiring that customer and then when we're engaging them on an ongoing basis and reengaging them to turn them into a loyal customer. And then, of course, it's critical to analyze what's working, what's not working, and iterate. So we're really turning the crank on all of this over time to get better and better at it. So that's the framework approach that was developed, and I want to dig into each of these pieces and explore them a little bit more. The first is really around driving revenue through personalization. Vinay, I'm curious to hear from you. As you started to explore some use cases here, what were some of the initial blockers before you had set up a lot of these data sharing capabilities? So from a data perspective, we touched upon that in the earlier slide. But let me get into the details of from a system perspective. So we are talking about multiple systems here. Commerce has been there for quite a long time. AEP is relatively newer system. And so every system has its own framework. AEP was structured in certain way to store data in certain way. Commerce were built in certain way. Now the challenge is how to make them talk to each other. So we spent a lot of time, to be honest, to ensure that the frameworks are aligned without distorting the framework in AEP as well as in commerce. But at the same time, when the data comes into AEP's ecosystem for segmentation activation, it's aligned with the framework. So that actually helped us a lot so that because that set the foundation for all the use cases that we'll be talking about later. Yeah. Absolutely. That makes a lot of sense. And so the way I understand it is, as we mentioned earlier, Adobe Commerce Data Sharing really helped by allowing that real time data to flow and also simplifying the integration process, starting to move away from point-to-point integrations, between every solution to more of a framework approach. And so I want to just give an overview here, and then maybe I'll hand it, Kevin over to you to talk about some of the specifics here. So the way that this works is Adobe Commerce allows Coca Cola to collect everything from behavioral events, so those are actions that shoppers are taking on the site, like adding an item to the cart, signing in, signing out, viewing a product page, viewing a category, and many other data types. Also back-office events, so things like order status, whether an order is created or canceled, order history, and then profile data. That data is then joined with data from other systems like the ERP or CRM system, and flows into Adobe Real Time CDP. That is the source then for unified profiles as well as audiences or segments that the experiences get deployed to. And then of course, those audiences created within Real Time CDP flow into create the use cases that you see here. And we'll talk more about the specifics around these use cases, but for this first pillar, they were delivering personalized offers based on shopper behavior as well as location, deploying, personalized product recommendations based on buying behavior, and then engaging with brand cross sell campaigns to sell additional brands that might be compatible with what a customer was already shopping for. Kevin, I'm curious to hear from you some of the specifics around this. Yeah. I think we put in the session description that's a semi technical or non-technical session, so I won't get too much into the weeds. But for the technical folks in the room, there is a two-pronged approach. There is the client-side data collection. So one piece of the commerce integration was auto populating the client-side experience driven data layer. So if you're familiar with digital analytics data implementations, you don't actually need to specify that data layer, hand it over to your development team, they start implementing, start pushing custom events. The data layer is actually auto populated by that integration. And so the events are auto picked up by the integration because it's already standard EDDL, it is standard XDM. So your client-side lift on the tagging side of the house is fairly low. So that is all of the consumer events that are happening on mobile as well as the web. That's one piece. That's the first piece. That's what we grouped here under behavioral events. But then there's a lot of like back-office data on Adobe Commerce, things that the customer necessarily don't do themselves. And those are attributes like the order status, the fulfillment status, potential cancellations, and so that type of data is actually streaming into the platform directly from Adobe Commerce to Adobe Real Time CDP. Those are the two categories of commerce related events that we brought together here by leveraging this native platform functionality and a little bit of customization. Awesome. And so I want to dive into a few of the use cases I just mentioned with greater depth here just to show you guys what these use cases really look like. And the first one we'll start with is AI powered personalized product discovery. So this was for Coke Store in the US. They saw a great opportunity to leverage some of the personalization and AI capabilities that we've built directly into Adobe Commerce, and that's with product recommendations and Adobe Commerce Live Search, both powered by Adobe Sensei. And you can see what the experience looks like for product recommendations units here. You see the personalized picks just for you. By creating this on the site instead of having a new products display, these are one to one personalized product recommendations based on the behavioral actions, and the affinities that each shopper has to certain products. This drove a 117% increase in clicks and a 36% increase in revenue versus just having a static new products block. So really powerful business impacts there. There was also a 17% click through rate for the cross-sell recommendation type, which was using a frequently bought together block in the later part of the purchase process. With live search, which provides really fast and relevant search results, they saw a 19% conversion rate from search. And, generally, the top three results contained what the average searcher was looking for, which shows you that there's really a high degree of relevance in those search results for what someone's looking for. So both of these capabilities really allow you to hone in on exactly what products you want to recommend, as well as the rank order of search results for each unique shopper that comes to the site. Up next, let's talk a little bit about the personalized coupon spanning channels. And, Vinay, I'll hand it over to you to talk about that one. Sure. So one thing that we should also think about is how do we convert a negative experience into a positive experience? Because our consumers, they need help with decision making. So in this scenario, let's say, the consumer was given a promo code and unfortunately, know that consumer is multitasking. He or she must have forgotten to use the promo code before the deadline. And after the deadline, they got a message that, sorry, promo code is not available. But we should read that event and see how we can delight our consumers later. So that's what we are trying to unlock here is to ensure that if, let's say, there's no stock available or the promo code is already utilized by others. So we should be proactively sending those triggers or not just to our consumers stating that, hey, there's this new promotion, do you want to participate in this promotion? So that creates a delightful experience for our consumers as well. Kevin, do you want to touch more on that? Yeah. So what we're looking at here on the right side is pretty much the experience platform in action with all of its components. So on the left-hand side, we got the data collection with the web SDK and the mobile SDKs. Those SDKs are native, and they stream the behavioral events into the experience platform data link. Now the data is there, it can be stitched, it can be appended to the other customer attributes that Coca Cola ingested into the platform. And then we use Adobe Journey Optimizer the actual last mile sent tool to send the email, send a precise recommendation and actually complete the funnel. And the analysis was done in customer journey analytics. So in this particular use case, we really touched all the platform skews and leverage all the different capabilities the way they're supposed to. Yeah. That's awesome. And so I now want to shift gears into that second pillar of this where we're talking about ongoing engagement retention. And this one can be really hard because now we're not just talking about the site experience, but we're talking about the journey across all channels and across that full end to end journey. And so I know for Coca Cola and looking at the en tu Hogar brand, there were some challenges here. Acquiring the customer was really expensive. New traffic to the site was low, and churn was high. And so when we look at the data signals in terms what this actually means, it meant that a consumer might consider buying and then fall out of the purchase funnel, or a customer would buy once and then go idle. Or in other cases, you may have consumers that are engaged with valuable marketing dollars, but then they don't convert. These are a lot of the challenges that we see companies facing. Coca Cola set a clear objective here of when we acquire a customer, how do we take that customer from just engaging once to becoming a really loyal and highly engaged customer? And what that looks like is a customer who is ready to buy is engaged at exactly the right moment, they're engaged in the channels that matter most to them in that moment, and importantly, consumers who aren't ready to buy are deprioritized. So we're not spending those valuable marketing dollars when we shouldn't be. And so I want to delve into sort of the how behind this second pillar, and there are really two parts of that. The first is, how do we create audiences using Adobe Commerce and real time CDP? And really what's happening here is the data is flowing from Adobe Commerce into CDP to then inform the customer profiles, and then create segments that are either rules based, or AI powered. Kevin, I'm curious to get your perspective on sort of the AI aspect of this. I mean, Summit is full of it. We hear a lot about artificial intelligence and what we've done here, was really leveraged the AI components for data related activation projects. And so in particular we leverage the customer AI platform add on to quickly graduate from rule-based audience creation to propensity score driven audience creation. It's kind of like a nice logical sequence. You start out with rule based if this then that at some point, you have your low hanging fruits covered, you have your always on campaigns structured and launched. You are able to monitor those, and then you can get more experimental and really introduce propensity score driven audience creation. That can be propensity for positive interaction, AKA likelihood to buy, or the propensity of a rather negative consumer interaction, propensity to churn. And so that was a nice graduation process to really up level how we think about audience management and audience creation and also audience maturity. Yeah. And I think that one's cool too because it allows you to really scale your segmentation effectively. You can create only so many rules-based segments. But when you're allowing customer AI to really understand create some of those propensity scores, and you have a button to create segments, which you see in that screenshot there, it allows you to move faster and create segments that are really going to convert. The second piece of this around engaging consumers across channels, and this where there's a really incredible combination of Adobe Commerce with Adobe Journey Optimizer. What happens here is that Adobe Commerce data serves as the signals to then create campaigns or launch journeys based upon. So that could be a shopper takes a certain action on the site, and then you want to engage that shopper with an SMS, a push notification, and email over time. Vinay, I'm curious to hear from you a little bit more about this one. Sure. The more the complex the journey is, the better your omnichannel orchestration is. So, hence, you see a lot of branches on the slide that you're seeing on the screen. So once you have gathered all the information about your audience, you know what their likes are, what their interests are. Now how do I send the right communication? And that's through AJL. So now the thing is, with all the technology that got unlocked with this connector, with all the behavioral signals coming in, you are now able to predict what the next step of the consumer would be. So based on that, you can decide whether to have to send an email, send a push notification if there's a mobile presence, or send an SMS or WhatsApp communication. So that creates a true omnichannel experience, a multi moment experience for our consumers. And we ran a similar use case. Everyone knows about it, especially all the ecommerce companies. The only use case that we always think about is cart abandonment to begin with always, because that's where you can drive your revenue.
We saw a lot of outstanding results there. We were way above the benchmarks in terms of the click through rates, in terms of the open rates. And that actually helped us to expand our journeys to bring in more channels into the picture. Now what is happening is if someone has clicked on the email, landed on the page again, they are multitasking, they drop off. But next time when they directly come to the site, I can still personalize the experience. I know that they still not bought the product. So that's the whole power of the commerce connector here where a lot of data points gets unlocked. Kevin, do you want to touch upon, the push notification use case? Yeah. Mobile is always a little more difficult than web. Hybrid apps, you can have native apps, et cetera. And so, you know in order to really get that up and running on mobile instead of using the web SDK we'll obviously leverage the experience platform mobile SDK. Once that is deployed, it'll collect your standard out of the box mobile events, initial launches, app crashes, et cetera. But the business is not making money of app crashes or app views. So we obviously need to customize it, tweak it to really pick up the nuanced events that are relevant for the specific brand. And so once we get that up and running and we collect all the behavioral events on our mobile application, we get the real time data into the Experience Platform Real Time CDP, plus we also unlock the ability for Adobe Journey optimizer to do in app personalization. That's the other piece of the mobile SDK is taking care of. It's kind of our Trojan horse that really unlocks our mobile app personalization pieces. Yeah. And, really exciting. We're actually releasing mobile app personalization in April. You may have heard that during the roadmap presentation for commerce. So, we're ready and excited work with you guys on that. Great. So, some of the other use cases that we could think about besides the cart abandonment use case and the push notification is it's also important to know that whom you should not target because that drives your media efficiency. The dollars should not go into targeting the consumers whom you think are not right now are thinking about buying a product or may not be buying a product anyways. So your suppression segments are really important there. Besides that, if you're working with your bottlers in the case of Coca Cola, so whenever an order is placed, it's serviced by a particular bottler based on your geolocation. So if there is any inventory challenges or there's products that needs to be stocked up, we can always do that event as well and send an email to the respective bottlers' team to tell them that yeah, guys, we need to stock up some products. So a lot of this kind of use cases also got unlocked for us, which is what we are looking forward to implement now. Yeah. That's great. And I love how you've really started to fuse some of the B2C use cases with your consumers, and the B2B use cases with bottler relationships and really reminding them when that purchase frequency is needs to be refreshed. So, really cool to see how that's coming together. So the third pillar of this really revolves around understanding. Understanding the customers effectively, understanding the business needs effectively. And if we take a big step back, a lot of this digital transformation work that Coca Cola was doing really leaned on this goal of having a lot of clarity here, and getting better and better at that over time. Kevin, I'm curious to hear from you. If we look just to the broad landscape of what companies are dealing with around this sort of data clarity piece more broadly, what are some of the challenges that we see? Yeah. I think we're all sick of the announcements about third party cookie deprecation. We all know it's happening. We all know we need to leverage first party data. So that's really the market need that that gets addressed there. So brands are really incentivized to really have their customers convert from an anonymous cookie into a known prospect or a consumer. Only once you earn the right to obtain that information, the right to market to that consumer only then can you really leverage the data also for your own selfish marketing purposes. And so the lack of data visibility is an always on need and the experience platform as a whole really targets a lot of the specific needs there whether it's data collection, unification, analysis, orchestration. Yeah. Yeah. So from my point of view, a lot of ecommerce firms, they spend approximately, I think, 15 to 20% of their revenue into performance marketing. And one of the key aspect of performance marketing is to acquire the consumers to track all your conversions, retargeting campaigns that you run. Unfortunately, with all these ecosystem challenges that we are facing, it's all cookie driven right now. That gets impacted quite heavily here. So, hence, having to know your consumers better, all the desserts that's coming to your side, capturing every single behavioral data, be it macro or a micro level, that would really help to understand your consumers. Your lookalikes becomes better, and then you can then decide on all the AI stuff that you run on top of that, then all your propensity scores are more accurate. And that's something I think this integration would also help to unlock. Yeah. That's great to hear. And so I know that the approach here used a couple different solutions, and we've talked about sort of the first step here around unifying customer profiles and creating impactful audiences. But with commerce combined with real time CDP and, importantly, customer journey analytics here, there was another level of depth that was achieved. And so as we were talking about this before, it was really around what were the questions that you were striving to answer and what are sort of the analytical use cases that we can set up in order to be able to answer those. So once that unified customer profile was created and those audiences were created, the analysis then matured across the full customer journey to really determine the next best action that could be deployed, and then, analyzing the results and iterating over time, using a couple key signals like the online revenue as well as conversion as key indicators of success. What was working and what was not working? So, Vinay, I'm curious to hear from your perspective what did this sort of allow you to achieve or to answer? So now we have started thinking beyond cart abandonment now. So it's more about browsing abandonment, product recommendations based on certain product propensity scores that we could figure out through the AI capability that the tool has and then try to give the next best experience or next best offer to the consumers. You can send offers through the offer decision feature within AJO now. So these are some kind of use cases that we are trying to unlock here besides cart abandonment. Yeah. That makes sense. And I know we talked a lot about the use cases for the first two pillars here, and that there's a long list of these different analytics use cases that you could create. But I understand that there's a few that you prioritize, and I'm curious to hear from you on sort of what these use cases were, and how customer journey analytics really helped provide some clarity here. Yes. So what you see on the slide is a fallout report. So we understood how the consumers are interacting with our cart abandonment campaigns, how the experiences of the content is working now, what's the journey that they took, what's the fallout that's happening, and how do we then triage those consumers who didn't open or who opened Click, but then they never converted. So those kind of insights are quite important for us because you don't just send the campaign and then forget about it. You need to understand what went wrong with the campaign, what went good with the campaign, and then try to rectify things that didn't go wrong. And CJA is helping us to do that. So and besides that, obviously, we could also understand certain channel preferences based on the performance data that's automatically flowing into CJA, through AJO. So, yeah, that also gives us a lot of insights about what is that channel that we should be using to reach our consumers. Awesome. And so we've talked today a lot about en tu Hogar which is the LATAM D2C brand for Coca Cola. But I'm curious how we think about sort of broadening the lens out to Coca Cola at large. So really expanding to the broader global vision and sort of the path to scale here. So can you talk to me a little bit about that? Sure. So some of the steps that we took was it was not just about connecting commerce and AEP through the connector. It was about testing the system how because there's huge amount of data that will start flowing into AEP. We need to understand how well the system can handle this data. What are the different failure points. So once we understood all these things, then we gradually jumped into running a pilot. So whenever we ingested the data, any data point, be it storefront events or back-office events, we did a POC. We built a segment and saw whether all the audiences are getting qualified or not. And we were always crawling. It was not crawl, walk, and, run kind of an approach until we were fully satisfied that, okay, things are working fine from a system perspective, from a data perspective, and from an activation perspective. And then that became a template for us now to take it and move to other regions as well. Yeah. I love that idea of a template. It's like, how do we use this to know that it's going to be really effective? Start with a single use case or, in this case, a single brand, and then really start to broaden that lens and scale up. And I think it's really powerful that Adobe Experience Cloud is built to handle all of that data across all of these brands. And so we're excited to see how that journey progresses, with Coca Cola. So we've talked about a lot today. We talked about how do you set a team structure for success and really rally that team around a core set of use cases. We talked about how do we share data between solutions and center those on that set of use cases so that we can then start to tackle them one by one. We've also talked about the importance of data flowing from Adobe Commerce to all of these other systems, implementing, testing, and then learning over time. And we talked about how to set a really robust structure and framework in order to be able to prioritize this. I'm just curious to get sort of key top of mind learnings or key takeaways from each of you, just to see sort of where your head's at and what you would take away from this. For me, I think it's a use case mapping. And very particular including the data needs in the use case definition because that was the initial rallying point that set us up in the right direction that we began merging we launched the use cases We delivered results. That was so pivotal in this whole journey. For me, I think the team dynamics was very important because we had folks joining from volunteers, Global Martech from Coca Cola, and experts from Adobe as well. And we were all spread across different countries, to give an idea, so it was across seven countries. I'm based in Singapore, so it was night for me, and in the US, that's when they get up. But I think we understood everyone's skill set. We understood, who's doing what. We had a clear RACI built. We had a clear approach built. We had clear solution built. And the time zone actually helped us. Time zone difference helped us because, whatever we did in the morning, during US hours, if something failed, then in the evening, APAC hours we all managed to fix that. And then we immediately got on a call, sorted everything out. And we had regular sprint cycles as well. Yeah. And I think for me, it's really around, taking an experience led approach to all of this. I think when we talk about this, it can be easy to say, what are the tech solutions that make sense here, and then let's just jump right in and start doing things. But I think really setting the focus around what are the experiences that we actually want to create for the customer. How do we take sort of a bite sized approach to those? And in this case, it was the use cases that were used to do that. What data do we need, and how does that map to those? And then ultimately, how do we light those use cases up to create these compelling experiences? Thank you all for coming. We really appreciate it.
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