Salesforce, Inc. (CRM) Earnings Call Transcript & Summary

Good morning, good afternoon, good evening or good night, depending on where you are joining us from today. But thank you very much for taking some time and for joining myself and Adam. So we're here today to talk through Bringing Real-time Magical Moments to Your Customer. We'll go into a lot about what that means and how we do that in a moment. But before we do some formal introductions, my name is Christopher Jordan, or you may hear me called Jordy for those that I've met before. Lovely to meet you here today. I'm the Regional Vice President of our cloud sales team here in ASEAN, based in Sunny Singapore today, and I'm joined by my partner in crime, my data cloud for marketing expert and guru, the genie himself, Adam Wise. Good afternoon, Adam Wise, and good morning. Thank you for joining us today. Adam is a Principal Solution Engineer here in the cloud sales team and will talk us through a little bit more in detail as we go through on how, what and how we can deliver on those magical moments for our customers. So before we get started, a couple of housekeeping things and moments that we should probably mention that are critical to what we do. The first, those that have worked with Salesforce before will know is that we are a publicly listed company. And it's important that any buying decisions that are being made should be made based on those things that are publicly available as we speak now and are GA, not those that are forward-looking. So just wanted to cover that off in case anybody here was going through a buying process at the moment and cover that as a result. Now a few pieces of housekeeping before we get into the real content. A couple of things you'll see in this platform. You will be muted. So all attendees are muted. If you do have a question, there is the Q&A tab, which you should see at the bottom of your screen. Feel free to jump in there and ask any questions. And feel free to go through if there's anything that we want to pause and kind of go into, please let us know that. At the same time, if you do have any issues with your browser, feel free to please jump out, refresh and then rejoin. We will still be here. I promise. We know that sometimes that can happen. And then there is additional content as well. And you'll see in the related content box on your screen, there are some additional services and resources that you could use that hopefully will be helpful. So as we go through, as I say, any questions, feel free to please type those in the box, and we will get to those towards the end of the session. So what are we covering today? Well, we talked about those magical moments. There's a couple of things that I think are important as we think about what those magical moments are. And the first of those is going to be a bit of a recap and a summary of some of the insights that we've got from our state of marketing report. We'll go through a couple of things of what we're seeing at a macro level, at customer level and then company level in terms of the challenges that we're seeing as a result of that. We'll then dive into a little bit about the data cloud for marketing overview. You may know this product previously known as CDP or previously known as Customer 360 Audiences, and we'll clarify a few things around that, give a bit of an overview. Then we'll pass over to Adam, who's going to go into a little bit more detail around the use cases, right? How do we bring this to life? What does this look like under the hood and how do we deliver on these use cases through the technology. We're then actually going to show you the technology. And we're very excited to do a relatively deep-dive demo today to actually show you the technology and how we can use that to make kind of success for our customers. We'll then wrap up with any closing thoughts, and then we'll jump into a Q&A before we finish with a thank you. And the intention being that we'll be done in about 45 to 50 minutes. So we appreciate you taking time out of your day, and we're very thankful for you to joining us. So we talked about that state of marketing. What's happening? I think we are all very aware that we're in a new world. I feel like 2 years ago or 18 months ago, we were saying we were in a new world because we were coming out of COVID. All of a sudden, we're in a second version of that new world, right? We're seeing challenges at a macroeconomic level globally, right? We're seeing that uncertainty in the economy on a daily basis with banking in the last couple of weeks, other macroeconomic and political or geopolitical challenges. On top of that, we're seeing labor shortages in particular markets, a real, real focus on CFOs and shareholders and stakeholders on tightened budgets for all customers and companies around there. And then this ongoing supply chain crisis, which has obviously been around for a while, but it's definitely starting to kind of come to a head at the moment. But whilst we're in this new world, we're also in this understanding that we need to do all of these things as the people have -- the word we use a lot at the moment is doing more with less, right? We still need to be able to understand our customers, drive loyal relationships with them, drive productivity, efficiency and provide value during these times. And the reason that we need to do that is that our customers' expectations are consistently rising. We've seen this for a while now, right? There's been this fundamental shift in how consumers both at a B2B and B2C level, think about what they're looking for out of the company. And that engagement as a result, is significant, right? We've gone from this world of mass media, e-mail blasts and Internet advertising that spammed everybody, bricks-and-mortar stores and broadcast media through to this understanding of me as an individual. I want personized offers. I want to be able to buy on my time in my moment. I want information when I want it. I want it personalized to me. I need service 24/7. And to do that, I need AI at the heart of it. We've become AI native, right, very quickly. Everything we do and see, be that on Instagram or the news, is curated for us and personalized as a result. And because of that, customers' expectations keep going higher and higher. And you can see here that 80% of those customers are saying that the experience a company provides is just as important as the product or service it offers. And that's fundamentally changing how we think about marketing and the 4Ps, right? Because suddenly, this experience piece, even if the product is fantastic and it is a cheap price, it doesn't matter. I need that great customer experience in order to survive and prosper. But that's not easy to do because we're living in this disconnected era. On the one hand, you have the brands or the companies or the customers. And on the other, you have your end customer, who has become a real-time customer. They have this expectation of things happening in the moment, instant gratification. Now because of this, you can see on the left-hand side, companies have had, on average, enterprise companies across the globe are using 976 different applications in their businesses. Now you want to understand me as an individual consumer, right? You're going to have to overcome those applications, the challenges on multiple identity data, fuzzy matching. How do we understand who Chris Jordan is not just my different IDs in different parts of the business and in those different applications. How am I taking siloed data from certain systems and combining it with others that are more flex? We're also seeing the end of the cookie. We've been talking about this for a while with the changes that's happening in Google. So how do we overcome this whilst managing that incredible explosion of data. We're not talking thousands or hundreds of thousands or millions of data points now. We're talking billions of data points, terabytes of data even tied to an individual consumer. How do we start to do that? Because if we don't get it right, we can see here that 1 in 3 customers will switch from brands they love after one bad customer experience. That's all it takes. Unfortunately, our customers are a little bit fickle, and very quickly, we can lose that engagement just from that one negative experience. So we need to adapt. We need to think about how we're delivering for our customers, but importantly, how we're doing that with efficiency as we know that budget cuts are getting there and kind of we're going to be squeezed tighter than ever before with productivity at the heart of everything we do. And critically, as we'll talk to in a moment, value. What is the value exchange that we're giving with those end customers in order to create that 2-way experience. How are we doing automated case resolution? How are we doing order reminders? How are we using what our consumers are giving us as an information perspective to give them back as a result? So that could be things like their favorite items or suggestions. But this expectation of this consumer has drastically increased, whilst the data is drastically increased and as a result, created this gap. So what we're going to talk about in the next 30 minutes or 40 minutes or so is how do we overcome that gap, and that is why at Salesforce, we have now introduced data cloud for marketing. This is probably one of the most revolutionary innovations that Salesforce has delivered in the last 20-odd years. This is a huge fundamental rethinking about how we work with customer data, how we make that real-time, how we put that at the heart of the Customer 360, which I'll talk to in a moment, and then how we deliver on the value as we just touched on for those customers as the result of the back of it. So what does this mean? What is the customer data cloud? And how do we think about that coming through? So when we think about data cloud, what we're really doing is fundamentally, as I say, repositioning how we think about the entire stack of the Salesforce ecosystem. Previously, we've obviously talked about having that transactional database and our Hyperforce kind of flooring as it were with allowing us data residency. This now elevates all of these elements to deliver hyperscale data platform in real-time. Real-time Einstein AI, hopefully, everyone has seen some exciting new pieces around ChatGPT and our CRM and what we've been doing that recently, and then allowing that to push up to real-time flow automation, not changing what you're doing with those applications, be that from sales, service or marketing, but allowing you to use all of those data signals and do it in real-time. You can see on the right-hand side that we've got some amazing visuals of the data flow happening in terms of our data mapping model. We will show you how we do this in the platform shortly, but this is a fundamental shift in how we think about the data layer sitting under every application in the Salesforce ecosystem but driven through that marketing use case. And that's really changed the Customer 360. Because previously, we've talked about the customer being at the heart of everything we do at Salesforce. And that's the way that we think about this Customer 360 model. You have Genie in the middle. Genie is our branding and element for the data cloud model. It allows us to have that real-time, critically, the real-time multi-milliseconds understanding of who the consumer is, the engagements they're having across that customer journey and then being able to surface those insights in those data or segments or individual consumer preferences to each of those applications, so in the moment of a service. Am I using the most relevant data about that customer to help serve that customer base of that AI or through a first-call resolution? From a marketing perspective, which is what we'll talk a lot about, how am I making personalized offers based off my engagement previously across all channels? From a commerce perspective, what am I recommending? How am I driving that commerce engagement? And all of that is happening with real-time data with intelligent layers over that sitting under it with Einstein, with the automated flows, as I mentioned in real-time and delivered on Hyperforce giving that data residency, data compliance and doing it as a result. So this is really shifting how we think about the Customer 360 model and actually adding that real-time data layer at that real-time customer level. But this isn't just on a Salesforce perspective. When we think about what happens in terms of how myself as a customer engages, yes, a lot of it's going to be on Salesforce, and that's a real differentiator. But I want to be able to expand that. We want an open ecosystem. We've always had open source as part of our go-to-market in our model and underlying that data model and now allows us to have these amazing strategic partners that we can go to market with. You'll see as we go through, we have open data access with Snowflake. We have the models that we're allowed to do that, pushing that forward with back and forth with Snowflake in a very smart way. We have first-party advertising connections with Google, with Meta, across Instagram and even with Amazon app. These are fundamental ways that we can extend beyond your own channels through to your paid channels in order to engage with those customers and then something that customers have asked for a lot and for a long time, which is bring your own AI, how do we bring your own proprietary models that have been built on things like AWS and allow you to use all of that data that you know about your customer to make that happen. It's very exciting kind of exchanges building off the back of that. So our app exchange is constantly developing. We have some amazing exciting new innovations with people like TradeGet, which are allowing us to then push those again beyond, not only first-party advertising, but through the second- and third-party advertising, allowing us to extend that network, extend that customer engagement and really touch every point of that customer journey. As we start to do that, that's where we start to think about value creation. And I mentioned this before, but when we think about why and how we're doing this, this is about creating that exchange of value between you as a brand and the consumer. And when we think about that, that spans the entire customer life cycle and that entire customer journey from top of funnel through to the kind of engagement onboarding and then all the way through to the other side from a loyalty perspective. We heard before how fickle customers can be and how quickly they can shift from brand to brand, but how do we engage through that loyalty to make sure that they're sticky to us as a brand, and we don't lose them through a bad service experience. We might have done all the hard work on the left-hand side here of driving that awareness, engaging in a personalized way, using that data to acquire the customer onboarding them well, but then sending a cross-seller and up-seller at the wrong time and, by doing so, losing that loyalty and that opportunity to make that customer increasing that lifetime value. And as a result, from a key business driver and opportunity perspective, we can go from discovery from a B2B perspective to demand sales increases, lowering cost to serve, all the things that we see that CFOs, CEOs are looking at, at the moment in terms of how do I add that value and how do I do that? And in doing so, we can get use cases that are relevant to the customer at every point, from the acquisition perspective. Could be a more relevant use case to me based on my previous behaviors and data points. It could be a better call to action that is kind of targeted to me personally, reaching more prospects, finding people that look like our best prospects. How do we convert those? How do we start with smarter new journeys, using the channel that the customer is looking for, not the one that we think they're looking for? And then how do we target those retail customers and then drive that loyalty as a result? This really is a never-ending loop, right? When we get to the loyalty, we come back around to that relevant offer. And this is a virtuous circle that allows us to use, again, real-time customer data, understanding every single point of it in order to bring back that customer and bring them through that customer journey. So what are we talking about? Well, we've talked a little bit about use cases. We know we've got this challenge in the customer space with all of these different applications, this explosion of data, this demand from the consumer to have this experience in a personalized manner. But how do we do that? What's under the hood, and then what are the use cases that we can deliver to show you this. So for that, I'm going to pass over to Adam, who's going to talk you through a little bit more detail and then show you the platform on how we actually deliver it.

Thanks, Chris. So yes, let's talk about the way we've built this in a bit more detail, and then we'll get into a demo. So it all starts with being able to handle a variety and velocity of data and connecting all of the different data sources that we need to bring in. So with Data Cloud, we're able to connect to all your business-critical data. We have out-of-the-box connectors for all your first-party data sources in sales, marketing, commerce and Service Cloud. We also have connectors to cloud storage for Azure, Google or Amazon. You can bring real-time web and mobile data into the system via SDKs. We have APIs for streaming and batch, which could be leveraged to bring in any data from external systems, and ISVs can also leverage these APIs to build connectors for additional applications. Then we have MuleSoft to connect 400-plus applications across the ecosystem. We're able to store any type of data, raw data, semi-structured, unstructured. And next, it's very important to provide the capability to prepare the data with transformations. We have in-built streaming and batch transform capabilities that I'll show today. The next layer is where we take the different varieties of data and harmonize it into a customer graph. This is where you get your final model data, taking hundreds and thousands of entities and leveraging them to create a single customer graph that utilizes harmonized data in a canonical format. And next is the critical task of identity resolution and -- which maintains the lineage of all of that data. And that's where the unified layer comes into play with out-of-the-box capabilities for identity resolution and reconciliation. We allow you to create that single source of truth by leveraging any and all identifiers we have of the customer through exact matching and probabilistic matching. Once we're able to create that source of truth, now it's all about getting some value out of it. And with our primarily Clicks, Not Code approach to segmentation, you can get insights about your customers like lifetime value churn or engagement scores. You can use this data layer for business intelligence and exploration or do AI predictions with an open and extensible ecosystem that allows you to connect any application outside of Salesforce as well by leveraging the APIs and SDKs. In terms of the entire ecosystem, now you can act on the single source of truth with ease in a variety of ways. Specifically, you can trigger journeys, orchestrations and flows. You can activate data to different ad platforms and personalized web and mobile experiences because all of that data is available by SQL APIs, restful APIs, SDKs. You have the ability to put in engagement data into sales and service cloud. You can create personalized experiences with commerce, and from an analytics perspective, you're able to connect the data to BI and AI tools like Tableau for Business Intelligence. You can connect to any AI service or Hyphen SDK for your data exploration and machine learning modeling. We also have a very strong partnership with AWS SageMaker, the machine learning service on AWS so that you can create your own models in the infrastructure and machine learning service of your choice and deploy them on data cloud for those AI predictions. You can leverage the ability to activate data back to MuleSoft to connect to hundreds of other applications. Or finally, you can leverage our app exchange ecosystem, which will have prebuilt applications for your most common needs. And all of this is on a platform with open standards. You can divide data into different data spaces, you have governance built in, and you get the full power of the Salesforce platform in terms of extensibility, packaging and our no-code to pro-code approach. So now that you have a sense of the power behind what we've created with data cloud, I'm going to show you a demo of how this develops from kind of left to right on this diagram. Let me just share my screen here. Here we go. Hopefully, you can see my screen. So here we have the Data Cloud home screen built on our familiar Lightning interface, and it's powered by Hyperforce, which is what we call Salesforce's public cloud strategy. It's designed so that you can get all of your data into the system, and it can scale to the needs of the enterprise. So on the home dashboard, you'll see an overview of data streams, segments, activations, how much data has been brought in, everything that's going on within the system. Now let's see how we connect to different data sources that we may have for our customers. We provided a Clicks, Not Code approach to be able to connect those data sources in just a few minutes. And as you can see in a second, we can connect to Marketing Cloud to bring in any e-mail push or SMS information. We have Salesforce CRM to connect sales, service loyalty or any application built on our platform and all the data associated with it. You have B2C commerce, bringing in order data, transaction data, customer profile data. We can bring in engagement data from your mobile apps, views, browse, transactions, purchases. Anything that the customer does on your app can be brought in by the mobile app SDK. The same applies to the web where you get real-time behavioral data about your customers' interactions on your site. We also have the ingestion API, which allows you to bring in data from external applications in either streaming or batch modes. We can connect to Amazon, Google cloud storage. You can also create your own prepackaged data stream and extend the platform and use it across orgs. And then finally, we have MuleSoft, which brings in hundreds of other connectors to connect you with the broader ecosystem. So let me double click in here. We're going to start with Marketing Cloud. Here, this is a native integration that our teams have built. And you can bring in any of that marketing cloud data, e-mail, SMS push or any data you may have imported into Marketing Cloud in just a couple of clicks for this demo. I'll be bringing in the mobile engagement data from mobile push. I select mobile push. And since this is natively integrated, you don't have to set up any custom workflows or custom processes here to access the data. With a simple flow, I'm able to bring in all of that push data and deploy it into the Salesforce data cloud platform. And that's really the power that the platform provides. Very little time spent on workflows or automations to export the data. Just 3 clicks there literally, and the data's into the system. Now let's talk about Salesforce CRM. This is another one where you have the ability to bring in the data sources that you need in just a couple of clicks. So out of the box, you have bundles for loyalty, sales, service, which enable you to get faster time to value because we've already mapped the source data to the canonical data model in Salesforce data cloud, so that you don't have to do any mapping of the data manually. It's all prebuilt. The other advantage here is that ISVs can create their own bundles as well in the customer bundle section. So that's how easy it is to get the data into Data Cloud. Now it's very important to be able to prepare that data and do transformations on it so that you can refine it as needed, and that's why we've built those capabilities right into Data Cloud. So here, you'll see some different recipes that we've created. And in this environment, I have a couple of recipes here such as age transformation. Let's click into this one here. So let's take a look at how we set up a guess age transform here. This is a simple one where we're calculating somebody's age based on their date of birth and today's date. And here, we'll see a preview that will appear on the data. There's a lot of different functions that are available to you as well, trim, substream splits, upper case, lower case, but you also have the ability to flatten data, do bucketing, edit certain attributes. You also have some AI transformation capabilities here for detecting sentiment, predict missing values, time series forecasting, which is going to be critical for those data source needs. I'm going to refresh this page real quick. This is live. This is a live demo. This is a real platform. I admit, I did preload this page about an hour ago and probably my session timed out. Let's click in here and see what we get in the preview window. Yes, so pretty robust set of tools here. And this is actually a recent new addition to the Data Cloud platform in the recent release and really give some additional capabilities that are really key. So here on the left-hand side, we can see how we're preparing this actual flow in terms of doing the transform there. Okay. So we brought the data in. We've done some transformations to refine it. The next layer is to actually harmonize the data. So for this capability, let's use a Commerce Cloud example, leveraging some online data from Commerce Cloud as well as off-line point-of-sale data. So here, you see the online data that's coming in from our commerce system. This is the source data on the left-hand side. And it's premapped. So we didn't have to do any mappings here. But if I wanted to add another mapping or use data from a different commerce system, I could simply click here on the left and select a field on the right. I'll do that in a little bit. But I'm not limited by the out-of-the-box model. I click here on this little pencil. You'll see I have the ability to extend the model quite comprehensively. I have all the standard objects, but I can go ahead and build a completely custom data model. And most of our customers have this kind of hybrid model of the standard canonical Salesforce data model in addition to some custom data model objects. So if I go ahead and pick some fields that I haven't already mapped here, let's look for some channel data. I can map that order channel into the sales channel. It's as easy as that. So a real sort of Clicks, Not Code approach to actually creating this harmonized data model. Now if I wanted to do the same with some off-line data, I've got a different data source here, totally different source fields. But what we're doing here is we're able to map it into the same sales order object that I map the online data into earlier, giving us a harmonized data model for all of our transactions. And again, all of this is done with Clicks, Not Code really illustrating the power of that harmonized data model with a declarative approach to refining your data and creating that final model dataset that now you can be using for all of those various needs. Now if we take a sneak peek of what this looks like from an individual unified perspective, this is our golden record, our single source of truth. And you can see how that individual is tied to hundreds of other data sources that have already been linked for you. This is, again, the power of a declarative platform with an out-of-box data model. Otherwise, you would have to create all these different relationships manually, leading to a lot of time spent maybe in Excel on a sheet that doesn't scale. So we've seen how you import data, refine it and model it. Now let's talk about how we create that single source of truth and unify those profiles. So exploring the profile unification process here, starting with our match rules at the top, you can see I'm leveraging many different identifiers here. Customer data lives in many different systems, of course, which may have their own unique identifier. So we need to be able to resolve across all of them. You might have e-mail, phone, iOS device identifier, cookie, subscriber ID, loyalty ID, an Android device. Whatever it is in your various systems, we want to be able to unify that. And Data Cloud gives you a probabilistic matching capability with fuzzy name, normalized e-mail, normalized address all out of box, and you can choose to leverage additional systems like Epsilon, for example, for identity enrichment. You can bring in that third-party data source to further enrich your first-party data. Further on down the page, we then move into reconciliation. Our perspective is that we are unifying your data. So we want to maintain data lineage. We want to maintain all of that source data. We don't want to touch that. We want to give the ability to create that one profile and allow you to reconcile certain records based on the profile. What might this look like in practice? If you're getting data from several different sources, that means you're getting a value for first name from several different sources. Data Cloud allows you to declare how you want the customer profile defined. In the case of first name, you can say you want the source that is last update, for example, or most frequently updated to determine the first name for the unified profile. Conversely, you might trust a particular source more than others, such as your CRM contact record. So you can choose here exactly how -- on a field-level basis, though very granular, where do I want to get this field from? Is it the most frequently updated piece of data? Is it the last updated piece of data? So in that way, these rules create the final unified profile record and gives you a declarative way to choose which first name lives on the profile, but all the other source records of the customer are still available within the system for future use. With identity insights, data analysts and scientists have the ability to, not only visualize the volume of individuals, both from source data and unified profiles, but also dig into each aspect of unified individuals and take a closer look at how records are being reconciled. You can customize the identity insight dashboard to inspect data in a way that is meaningful to you. And here, we see identity insight servicing total volume of source individuals as well as unified individuals as well as a breakdown of individual per source and percentage representation across those sources. We also get insight into how those individuals are being unified across the number of sources as well as matching rules as we scroll down the page. We can go one level deeper by clicking into a specific unified individual and analyze the mappings based on identifiers such as phone number, e-mail address and all the IDs. So data analysts can determine how their match rules are performing and what data might be missing. Now that we've created that data cloud-powered single source of truth, let's talk about getting value out of the data. This is an example of a custom profile view that we've created, where we can see a plethora of information about Rachel Morris, customer IDs, what segment she's part of, lifetime value, propensity to purchase, her on-site activity, her last orders in the last 90 days, her average basket size. You can see what brands she subscribed to, a record of her in-store purchases, her online purchases. Because this is built on the Salesforce platform, you can create your own views depending on the data you want to surface to different users. And while consolidating all this information in a single place is useful, this is where the magic of Data Cloud comes in. We took all these data points and then calculated insights on her propensity to purchase or churn and have noted her affinity towards running. We now have next best action promotions that are driven by Einstein recommendations. And it's all based on real-time data as well as product recommendations, I've also set up an event feed, so I can see she attended an event when she viewed certain content on the website. It will also show me when she was suppressed from marketing e-mails. Now we have all the pertinent information in a single view, ready to empower our employees to make every interaction with Rachel personal and relevant. So you can imagine a customer calling in, and your customer service agents have the full power of data cloud at their fingertips so they can see what was the recent activity. Does the customer have items in their cart. Did they place a pickup order in a particular store? What interaction have they had with the chatbot? What is their lifetime value? What's their engagement score? What are their most used channels. All of these insights can now be surfaced to your customer service agents right from within the platform. Now let's talk about how we can get insights from the data. This is one of my favorite topics because the value of bringing all that data together opens up a whole new world of insights for your business. You referenced calculated insights a little bit earlier, such as propensity to churn and things like lifetime value, but we also have streaming insights that allow you to create multidimensional metrics that could be used for personalization, business intelligence and futurization for your machine learning. You can access advanced functions like ranking dense, rank, person rank, all those sort of statistical functions that you may need for calculating things like affinities, engagement scores and all of them with SQL. There's no black box model that you're getting locked into. You have full control on how you want to define the metrics. So let's take a quick look at how that works. So when creating insights, we have the option of either using the builder interface or using SQL or creating real-time streaming insights or loading one from a package. This gives you the ability to create batch and real-time insights so that you can take action in the moment but also take action based on historical data. I'll show an example of creating with SQL here to illustrate some of the power in data cloud. And you can see on the left-hand side, down here in the field box, we see that full harmonized canonical model that's come in. We have access to any calculated insights that have already been built, so you can nest them within another insight. And then we can start building simple aggregations, for example, some daytime functions, then you can get into the hardcore statistical stuff, things like rank and NTILE for bucketing, lead lag, case statements for logarithmic, exponential for affinity modeling. There's a lot of power here. As we mentioned previously, you can also just opt to use the builder, take a look at the builder real quick. You can see I'm building out an aggregate here for rank by spend. I've taken that sales order data. I've linked it down to the unified individual with a couple of joins and created an aggregate for ranked by spend. Lots of different options here in terms of those functions that are available. I want to go out and build out a measure. We still have access to a lot of the power that you can get with SQL but via this nice visual builder too. So with this ranking, for example, I'm building here, we could use it to determine who can be approached to buy -- to be an influencer or an advocate for the brand or maybe use that ranking to do lookalikes for marketing purposes to acquire new customers. Let's move on to how Data Cloud can power Business Intelligence. We have native connectors that expose all the data in Data Cloud to Tableau. But we also give you the ability to inspect the data on your own using JDBC drivers, APIs or Python SDK, you can leverage the Python SDK to connect to Power BI or any other analytics tool of your choice. So let's take a look here at Tableau Insights real quick. Again, the great thing about this is just like when you created calculated insights, all of your data model is available right here. So you can do that data exploration. This is one of the out-of-box dashboards that are available for you to leverage on Day 1, or you can choose to create custom dashboards. These dashboards and the store's data is available everywhere in Tableau, Tableau, Tableau Online, Tableau Prep. But since Data Cloud is part of the platform, your business users don't ever have to leave the Data Cloud platform because these Tableau dashboards that the business intelligence analysts created can be surfaced right inside Data Cloud. We've got an overview of engagement here that I created in Tableau that I can interact with right inside Data Cloud, where I was working. So we've seen a lot about how Data Cloud can power various parts of your business, customer service, business intelligence and insights with Data Cloud compiler marketing use cases as well. And this is where our advanced segmentation capabilities with a drag-and-drop builder come into play so that you can create those segments and discover new opportunities at scale. I've started to build out a segment here for a running shoe promotion for loyalty members that we want to try and reengage, and they haven't made a purchase in the last 30 days, but they've shown some engagement on the website, for example, but they didn't open an e-mail in the last 7 days. Again, over on the left-hand side here, I have that full harmonized data model available to me, all of the direct one-to-one attributes available, but all of that related data, if you remember that big customer graph that we saw, here is where we get access to all of that through a segment builder. In the segment, I want to put in, make sure that we -- the customer doesn't have an open case. So let's just drag and drop in their case status. Exactly zero cases that are open. So the drag-and-drop builder is really business user friendly, really marketer-friendly. It's not uncommon for data analysts to spend a lot of time running SQL to bring data together for a segment and then run that query only to get a count of zero for the segment or get a count of 50, spending a ton of time on what is now wasted effort and having to start over again. It could take days, weeks to come up with the final segment, the final campaign that you want to do. But now with these capabilities, you have all of that available in this canvas, so your business users can do segment exploration on their own, find those opportunities for the business and get those campaigns out the door faster. This is a huge gain, and you can also activate using MuleSoft to 100-plus applications in the ecosystem. So a lot of different ways to activate that data. You can, of course, also activate to other Salesforce applications like Commerce Cloud for web personalization or marketing cloud personalization, for omnichannel personalization on the web and mobile apps. So in summary, today, I showed you several ways in which Data Cloud can manage your data at high scale and high velocity, power experiences across the entire Customer 360, leveraging our active real-time lake house. We showed you how you could personalize experiences for customers across the touch points and customer service interactions via our business exploration with Tableau, which you could do with Python or JDBC as well. We showed how Data Cloud compare on noncustomer-related insights as well. We highlighted the Clicks, Not Code approach that enables all this to be done by business users, while still giving robust options for data analysts as well. Everyone in your organization can leverage the single source of truth data. And then finally, as you saw in some of our app exchange partnerships, it's all about the open ecosystem. Salesforce's platform has 7,000-plus applications on the app exchange, and you'll continue to see more and more applications for data cloud because we've created this highly extensible big data scale platform that will power the entire Customer 360. Okay. I'm going to stop sharing my screen now, go back to slides. And now I want to take a look at how some of our customers are using Data Cloud, and let's take a look at how Formula 1 are doing that. [Presentation] So Formula 1 chose Salesforce for its scalability and ability to deliver on the brand's vision. Formula 1 is also extending its 360-degree customer vision with marketing insights from Data Cloud. So the brand will be able to personalize fan experiences in real-time, like attendees shopping at Race Day pop-up stores or global fans receiving communications that feature their favorite drivers. So Formula 1 turned to Salesforce because they really needed to understand their customers and have a single database to house their company and customer data. They already use Service Cloud and Sales Cloud. And now with Data Cloud in the mix, they'll have an end-to-end fan experience strategy. So by adding Data Cloud, they moved away from siloed solutions and will now have an integrated platform to store and maintain. Salesforce will help Formula 1, understand its data across every channel, both physical and nonphysical and around the globe, and Formula 1 is also using Salesforce professional services to manage their technology and implementation. Another story here, Kia Connect is the subscription-free infotainment and telematics service offered by Kia Motors on select vehicles. They leverage car and customer data to try to provide the most personalized experience to end customers. Their end goal is simple to understand customers so they can serve them with the most customized services possible and to surprise them with services they don't expect but would be happy to receive. To accomplish this, they want to take data from different sources, vehicle, personal, charging data, and they push that into Data Cloud with AWS. Then they unify data and build segments to send personalized campaigns and come up with new products and services. So if a customer has been driving with a bit of a lead foot, the system will encourage them with tips on how to save fuel. Kia Connect looks at their business from a customer -- is really built around customers, the product services team, support, R&D teams, they all work together by listening to customers. And Data Cloud has a huge amount of data, and they can graph patents behind the behaviors and the usage of services and then come up with new services and features off the back of that. So we have some really fabulous customers starting to take advantage of the power and capabilities that the Data Cloud provides. So with that, I'll hand back to you, Jordy, if you're still with us.

I'm here. Thank you very much, Adam. That was a amazing demo, great to kind of get under the hood. I think if we can keep using this car analogy and stretch it as far as we can, I think it's very interesting to get into it and see, I guess, the data or the fuel, as I often talk to it in that analogy to see, understand how we can drive those use cases forward. I think some fantastic stories. Great to see what Formula 1 are doing. I think you mentioned kind of connecting the data from Kia Connect. I'd like to see that done on the Formula 1 driver, especially given some of their performance this year but very exciting use cases and some fantastic stories there. So thank you for taking us through that. We're going to move on to a little bit of a Q&A session. There's been a couple of fantastic questions coming through. So we're going to spend the next sort of 5 minutes or so before we wrap up just going through a few of those questions. The first that has come through is one that we hear quite a lot, which is clarification around CDP versus Genie versus Data Cloud Marketing. So it's a great question. It's something that we actually -- we're in an event recently, both Adam and I presenting on the same topic. And this conversation got a lot of things rather up and started because of this exact clarification. So the way that I'd like to think about it is that those that know Salesforce and have been a part of it, you can see on this slide here, we have obviously our character in Einstein representing our smart AI, our intelligence layer, our AI capabilities and pushing it forward. Similarly, when we think about Genie, what we're talking about and you probably saw those that were around or following the content at Dreamforce last year, Genie is the character association with the branding of what we're doing with Data Cloud. Genie is the representation of the real-time data model that we're now underpinning the platform with. Similar to our Einstein across all the different applications and all the different clouds, this is the underpinning of that. CDP was essentially our previous edition and version of customer Data Cloud for marketing, right? And so that when we think about the Customer 360 within it, Data Cloud for marketing is essentially the rebranding of CDP, which was obviously previously known as Customer 360 Audiences. So hopefully, that clarifies. But Adam, anything from your side on that one?

Yes. I mean, I agree, we've definitely changed kind of the name a little bit over the last few years, but we really sort of started with securing and honing a smaller use case focused around marketing before rolling it out to the full Salesforce platform. We've changed the name, yes, but it's the same vision for the product of really creating those experiences for customers at a scale and speed, that's never been possible with Salesforce before. Yes, we have all those capabilities, personalization analytics, insights, AI. That's true in other parts of the platform. But really, this is, from the ground up, a re-architecting to bring those capabilities together in a totally new way. It really is -- that first organic innovation that we've had in a long time. So it's pretty exciting stuff.

Nice. Thank you. I think on that point of that innovation, one of the questions that's come through, which is a more technical question on this, but I think it has both the technical side and the use case side, I might firstly pass to you, Adam, is in terms of matching the siloed data, having the need to have the same unique identifiers, such as a phone number or device ID, et cetera, what if we don't have those identifiers completely, which means we can't map the data perfectly? How would we handle that?

Yes. So I mean, you don't need the same ID across every single data source. So you've got 10 different data sources. The first 2 have the phone number. The second one has the phone number and the e-mail. Your third one has an e-mail. We're able to kind of bridge between all those different data sources. As long as you have a decent selection of different data points, to actually do the matching across, then we can create this graph across all of these different data sources.

There's a follow-up to that, which I've got some thoughts on but would love your thoughts as well, Adam, which was collecting those identifiers such as the phone number, et cetera, it can be sometimes challenging. Completely agree, especially as we're moving into this data privacy first model. So do we have any examples or best practices on how to collect those identifiers for every single data source so that we can match those siloed data. So I guess, personally, from my side, this is kind of what we were touching on earlier when we were talking about that value exchange. How do we look at use cases that can go through data in enrichment? So for example, if you have an e-mail, is it a question of sending a customer a very simple e-mail journey that has a data verification process? You probably have seen the big platforms do this. Google, Facebook, Meta, those sorts of brands have done a very good job of -- when you log in to the engagement platform, saying, is this your correct details? Is this your right phone number? Is this your latest phone number? Is this still your home address? So there's definitely a value exchange opportunity to speak to customers and offer some sort of discount or personalization and explain to them how and why you need that data as that identifier, and therefore, the benefit that the customer is going to get of that. But Adam, anything from your side in terms of thinking about collecting that data?

No, I think you've got the nail on the head there. Of course, you want to do that in a trusted and compliant way, and that's something we're very much working on in terms of privacy. Obviously, we have GDPR compliance, CCPA compliant, all of those things. But we're definitely working on a consent management system as well to make sure all that data is collected in a trustful way.

Fantastic. Thank you. I think a very good question for you and one of my favorite topics as well, what happens if I have a different analytics platform for each of my owned media? I know this is a topic close to your heart, Adam, so I'll let you begin this.

Wow, I want to say [ Marc and Kevin ] Intelligence, right? it seems like an easy one. Go ahead. Give me your feel.

So this is a very good question and something that we see both in terms of owned media but also paid media whereby you're looking at analytics and understanding the performance of each channel in a silo. And one of the things that we want to do across the Salesforce Customer 360, not only with Data Cloud but also other parts of the Salesforce ecosystem, be that Tableau or be that Marketing Cloud Intelligence, which is our analytics and intelligence layer within the marketing cloud allows you to do that, allows you to bring in all of those different data sources, harmonize that data and get an understanding of which channel is working where against which segment at the highest performance but, most importantly, adding a layer of intelligence over that. So making decisions, pushing messages out or pausing campaigns or redirecting funds, et cetera. This is something that's very critical for us and something that we see as a big focus, particularly as we see this kind of diversification of the channel. And so centralizing that to get a full view of what's happening with your customer journey is definitely something that I would be looking at marketing intelligence as an analytics layer to think about. We've got a couple more questions. I've got time for two more. Let me have a quick look at what you were saying. There was a question on integration coming through here, Adam, which was what are the differences between the data stream connector and the integration?

I'm not sure I understand the question. So Data Stream connects, obviously, we have these out-of-the-box data stream connectors, best-in-class for the entire Salesforce platform. But then, of course, we want to be open and agnostic as much as possible. What are the difference between Data Stream connector with the integration? Hi, Karl, if you can maybe add a follow-up statement, maybe we can get back to you.

In the meantime, let's do one other question, Adam. How do you assess and advise where Salesforce starts and ends and interacts with the third parties.

In terms of third-party data, do we think that means?

I think it will be third parties in terms of activation and engagement. So we talked about obviously the AWS. We talked about Snowflake. We talked about the page channels. I think it will be around that.

Yes. So it's true with a lot of customers, we see there is some overlap. Customers have built data lakes. They built data warehouses. For customers that have data warehouses, data lakes built on things like Snowflake or AWS Redshift, those kinds of things, we have this zero-copy integration. So actually, all of the work they've done already, we can just take that and plug in the additional layers of data cloud on top of that. So there's no sort of loss of work, no technical debt there. Jordy? Yes. I'm thinking about the other end.

Good. Yes. Well, I think we've got 5 minutes left, and I do want to take a moment to say thank you to everybody for joining us. We massively appreciate everything that you have done and taking some time out of today. Hopefully, you've got a very good understanding now or a top-level understanding of what Data Cloud for marketing is, how it can help you to engage with your customers in real-time and in those personalized ways because as we saw it start, right, we are in this slightly challenging moment. I often refer to it as the personalization paradox whereby we have customers expecting this personalized experience, but this kind of huge, massive challenge around how do we understand who the user is behind all those data signals and then, in real-time, be able to use that to activate as a result. So this is a big kind of focus for us. I know there's some more questions coming through. So we want to say thank you for those. We will follow up with those. And if anybody does have any additional questions, feel free to reach out to your Salesforce contact, and we will, of course, be happy to go through that. I also wanted to take a moment to say thank you to some of our supporting partners that have done a great job in driving attendance and engagement with this webinar. It's thanks to those people and those partners that we're able to provide these engagements to you, hopefully driving that understanding and innovation that we have been doing. So thank you again for all of your time. Good morning, good afternoon, good evening, as I say. We look forward to speaking to you again soon. And thank you, Adam, for joining me today and for that fantastic demo and those customer stories. We shall speak to all of you soon. All the best.

Thanks.