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

Hello, everyone. Welcome to today's session, Deliver Proactive & Personalized Service with Data Cloud, and thank you all so much for joining us today. My name is Ariana, and I'm on the marketing team here at Salesforce. And before I begin, I'd like to cover a few quick notes with you about our webinar platform. Today's webinar will be available on demand after we wrap up and will be accessible through the URL that you are on right now. Please note the slides will advance automatically throughout the presentation, and you can enlarge the slides or any other widget by dragging the bottom right-hand corner. [Operator Instructions] And lastly, we encourage you to submit your questions at any time throughout our presentation today using the Submit a Question widget at the bottom of your console. We'll do our best to answer as many questions as we can at the end of our presentation. And with that, I am turning things over to Ed to get us started.

All right. Ariana, thank you so much, and good morning or good afternoon to everyone who is able to join our webinar today. We're really excited to talk to you about an exciting topic around Deliver Proactive & Personalized Service with Data Cloud. I'm sure every single one of you have heard about Data Cloud over the past year. It is like the fastest growing organic innovation. And we're really excited about the impact that's been delivering to a lot of our customers. And what we want to do this year is really talk about how Data Cloud can impact different teams across sales, service, commerce and marketing. So today's webinar is going to be on Chapter 1, as it pertains to the service like operations of your organizations as well. Now before we dive in, if you've ever attended like any Salesforce presentations, you're probably well aware of our world famous forward-looking statements. It's simply just a matter of "Please make your purchasing decisions based upon available Salesforce technology today". And to honor your time -- I've got some good news and some great news. To honor your time, we have an action-packed agenda that you see over here. We're going to talk about what are some of the challenges that service leaders face today. We're going to talk about how Data Cloud plus Service Cloud can work together as a great dynamic duo to address these challenges. And then we're going to do a deep dive in terms of how this integration works. We've got some really good use cases that kind of illustrate some capabilities. And then after we do the demo, we're going to talk about some latest innovations around our vector database, which we announced a couple of months ago, as well as give a little preview of what's coming down the road in our data cloud product road map. And as Ariana mentioned, we're going to carve out time in the end for Q&A. [Operator Instructions] We've got some product experts at the back end who are willing to answer those questions as well. So that's the good news. The great news is that I'm not going to be delivering this presentation. Instead, we have lined up here today a dynamic duo of product experts who are going to walk you through this whole presentation. I want to start off by introducing Dr. Von Clark McClendon. She is the Director of Product Management across Data Cloud and Service Cloud. And then also her tag team partner today, Kathryn Baker Parks, who is our Principal Technical Architect, who's going to walk you through a very compelling demo about Data Cloud plus Service. So I hope you enjoy this presentation and hope this is something of value. With that being said, Von, I'll pass it over to you.

Excellent. Thank you so much. Before we jump into the presentation, this is going to be a very game-changing, innovative webinar that I think that you're going to engage in today. So personalization and predictive insight or predictive analytics, it's nothing new per se. If you're an aficionado or connoisseur of this space, the BI, the data, the probability and statistics, you understand readily that the ability that you have to gain insights, glean critical information, make pivotal, real-time, competitive business decisions are at a critical impasse. Your organization is either on the front end or the back end of these decision-making techniques. And myself here at Salesforce, I don't know if you've seen other webinars that we've produced for you, I'm really elated that I'm a part of this team. I've gone from analytics, I've gone from data at rest, now I'm going to innovating the data in order to productize and feel what the pulse is on connected and unconnected assets and structured and unstructured data at the point of inflection, to gamify all of your use cases that you have today. So in my experience, as all of us, you're looking for this connected data journey about signals. You either have mobile devices, you have back-end systems, you have transactional systems, you have data lakes, you have expectations from your vendor, B2B and B2C use cases. With all of this information, you want to figure out how can I resolve this in a very critical timeline to make sense of all this data and all the wrangling to give ingestion a chance at my company. When I say ingestion a chance at my company, ingestion really means roughly 70% of your companies expect to deliver personalized interactions to make proactive decisions there. So as a service agent, you want to provide this critical service in a timely fashion. So as a result of all of these opportunities that you have at your fingertips, there's a system of engagement. Many times we ask what is the CDP, what's the difference between MDM? And then honestly, what is the difference between correlating and connecting the two. Well, I know this is not in the slide presentation today, but we're looking at personalization in context of a unified profile as it pertains to marketing or addressing context in your system versus a golden record or a golden global ID, trying to get into the information of doing data governance and security. The data is messy, the systems are messy, it's complex, and we try to make sense of all of this. There's a lot of noise out there. In the race to get that data into our fingertips, the customer faces things like timelines, SLAs, the right tool for the right job, utilizing applications in their everyday productivity like Service Cloud. Unfortunately, I can't see your hands today, but typically, I would say, raise your hand if this is a point of inflection or of contention for your organization. So building out these ecosystems, as you see here in the middle, you'll see that there is a centrifugal force within it. You see systems at rest, you see AWS, you see unstructured. You also see at the side of it, some nebulous data points that allow you to impact your business priorities. One of the insights that I know your organization struggles to drive is the priorities that align with the use cases that drill, glean and uplift any of their KPIs and their metrics. So one of those data problems or one of those massive footprints is having issues to cause friction in our service categories. So 4 of those 5 challenges claim that they face this directly with the inability to look back and pivot the service agent at the point of interaction with your systems. Can you improve those relationships? Is there difficulty in identifying what those contact drivers are? Are you demonstrating your return of investment of that service organization? Is there a complete view of the service actual journey? How are you going to impact CRM agent performance as well as the integration between core analytics -- see I just actually did a gaffe there, I'm already thinking about analytics and specific topologies and technologies to be able to bridge the gap between the 2. How do you actually improve those relationships? How do you determine where one toolage stops and the other is going to give an uplift in your system by engineering that into Data Cloud. And that's where Data Cloud comes in. Some of the inflection points, and I want to pause here. Some of the inflection points, when you see this slide, you typically think, what am I looking at? In order to provide any more -- delivering more proactive and personalized experience, Data Cloud has a hyperscale data engine. I don't want to overwhelm you or underwhelm you on this slide, but that allows you to do what, drive personalization via a unified customer profile, turn the customer service experience organization into a revenue driver, and then proactively address service issues that are going to come up using the data from any of these sources that I referenced a little bit earlier. I'm going to pause here. What does that mean? There's an engagement activity. There's an engagement model. So there's some web engagement and you're looking at that feed, identifying what's happening real time, driving that. And then you see in the service console that you have the ability to unify the information at one point with a variety of systems based on a unified ID, a unified profile, an account or a contact or in some instances, the case ID. So you're allowed to unlock any of that trapped data in this system, a monster system. I want to pause here on this slide for a while, though. How do we connect all that data? You have connected data from anywhere, structured and unstructured. You can harmonize that data, preparing it and transforming it and then mapping that data to a metadata model within your system via DLO or DMO, and then resolving those customers' issues that have been identified via that mapping technique. So if we look on the left quadrant of this, you'll see all the connected data or disconnected data, our core Salesforce systems, any data lakes, any EDWs or data warehouses or marts, and then you have the integration layers, something like a MuleSoft with APIs and SDKs tapping into the structured and unstructured data points. In the harmonization state, this is where Data Cloud is pivotal and game-changing and innovative. And if you look at our Gartner results, what Gartner results shows us, we can feed all of that curated and trapped data real time into applications, into ecosystems, into custom apps, and then feed it into an LLM predictive models and glean some insights but then write back to our CRM systems via something like enrichment, CRM enrichment, a related list, a page view and embed it possibly into a record page. So when you're able to do that, you are also able to take advantage of the speed, the actual insights, some of the predictions, some of the app exchange packages that you may have productized or deployed in your organization. The other thing that I think is really, really critical that I think about is the personalization framework that you could take advantage of, that was not there before. You couldn't personalize it because you did not understand Von Clark McClendon in System 1 versus Von Clark McClendon in System 2 versus Von Clark McClendon in a System 3. Why? Maybe there was some type of delimitation in one system. Maybe the e-mail address didn't map back. Maybe my address was off. The unification harmonization point that happens within Data Cloud allows you to prep that data, transform it and then propagate it downstream and then leverage it later on within your critical business use cases, which Kat will show a little bit later on. Kat, I'm going to pass it off to you right now, and talk about that harmonization approach.

Sure. Thanks, Von. Once all of an organization's data is on Data Cloud, we can then, of course, harmonize the data to create that single comprehensive record for every customer, like Von was talking about. And that's because Data Cloud allows us to integrate all of your data into a common model that's powered by the Salesforce metadata framework. Now this is such a powerful concept because our metadata framework is the secret sauce, if you will, that makes all of your data usable across every Salesforce application. So metadata describes how data works, such as how it's related to other data or how it's used in Salesforce. And in Salesforce terminology, this will be represented by an object like accounts or contacts and the fields that describe those objects further like account name or account industry. Now by unifying all this disparate data into one common format, all of your teams already using Salesforce will be able to more easily take action on that data using all the great tools and services that we provide in Flow or in Einstein. And then with all of your data integrated on Data Cloud, you can start to improve how you use all of our CRM applications like Service Cloud. So imagine being able to surface a 360-degree view of customers for your service reps, or making it incredibly easy for reps to act on real-time engagement data from your website or products right in the moment that they're engaging with customers. With Data Cloud, all of this becomes possible without needing to invest in expensive custom integrations that eat up massive amounts of time with IT to implement or to maintain. So we've talked a lot about what Data Cloud is, how it works to help drive more proactive and personalized service. But now I'm going to actually show you what that looks like with a live demo. Let me hit the share screen button. Okay. All right. But before I dive in, I just want to set the stage because we're going to tell a little bit of a story. So we're going to start off with Isabella. Now Isabella is a business owner running a yoga studio in Los Angeles, and she frequently travels to host yoga workshops and retreats and has scheduled one of her retreats to be hosted at one of Polonia Hotels Group's International Resorts. Fast forward a little bit, and it's the day of her reservation, the day of her travel. And Isabella had arranged with Polonia Resorts for an airport shuttle to transport her and her guests to the resort. Now unfortunately, 2 of the guests are experiencing flight delays, and it's already quite late in the evening. So of course, not wanting to hold up the rest of the group at the airport, they go ahead and get on their shuttle, but Isabella wants to contact customer service through their website on her mobile device, so that she can find a shuttle to transport the remaining of these 2 guests. So she can hop on her shuttle, I don't know, maybe like this hovercraft type of shuttle here that we're showing on the screen, but the rest of her guests can show up at the hotel when their flight arrives. So a hovercraft may be a little bit far-fetched in the future, but let me show you something that you can do in reality with Salesforce. So this leads us to our next person, so Sally, the service agent. She is in Salesforce in her service console, and she gets a notification about Isabella's case, and she is able to read that incoming text message from Isabella about a request for a later shuttle to pick up 2 additional guests from the airport. Now before Sally goes ahead and responds to this request for assistance, let's go ahead and explore what Sally sees in Salesforce. So in her Service Cloud console, Sally has access to a 360-degree view of Isabella, seeing all of her recent engagement. Down here, you can see at the bottom, including her recent service inquiry and her chat, which opened up this new case record. So we can see everything about her on the top left. Her contact information, her loyalty status, her lifetime value, her propensity scores, all available on the central location on the screen. Now this data, like we talked about a little bit earlier, comes from previously siloed data sources that have been harmonized and unified into Data Cloud's 360 data model, making them easily accessible to Service Cloud users like Sally directly in her flow of work. We can also see over here on the right calculated insights powered by Data Cloud showing that Isabella has a high rebooking score, along with her lifetime bookings and her website engagement. All of this information rolls up to really pinpoint that Isabella is a VIP customer, and so she should get that VIP service. All right. Let's jump back into our story. And we can see how Einstein, Data Cloud, and Service Cloud work together to provide that personalized and efficient solution to Isabella's airport shuttle challenges. So opening up this text conversation, Sally sees not only Isabella's message here in the center, but also over on the right, some recommended replies powered by Einstein and Data Cloud. Now Sally can review and post this message if it makes perfect sense, or she can edit the reply to make sure that she replies with the appropriate information. So here, I might want to add a little bit of extra content here just to confirm that she does need a pickup at the airport and not somewhere else. Einstein will continue to read Isabella's messages and provide recommended replies. It is really awesome. It looks like science fiction, but it actually -- this is here today.

That's why I got excited at that slide. Awesome.

So once Sally has all the information that she needs, she can hop into the case and she can proceed to initiate solving the problem. Now when she's in the case record, over here on the right, she immediately notices an alert that brings her eye to the screen over here that there's a prediction from Einstein Studio that's showing predicted time to resolve this case at over 36 hours, which is just not acceptable in this situation. So this is, again, powered by Data Cloud, and it shows not only the prediction, but it's also surfacing the factors that drive that prediction in case Sally wants to know, but also that we can see recommendations on how to bring this time lower. Now maybe Sally is an experienced service agent, and she already knows that she needs to go through Slack case swarming to escalate this case. But if she's a brand-new service agent, she may not know that, that's the best practice. So either way, she has that recommendation, and that's going to improve the time to resolve, obviously. So she's going to go ahead and initiate the case swarming to Slack to contact the airport shuttle service provider directly. We're going to do this in a new channel. We can see the channel name gets populated, and we can also see that Sally as the swarm agent gets populated as well. And we want to make sure that the right person gets added to that Slack channel with the requisite knowledge. Now she can add them directly here or we can use expert finder to look for certain skills, for example, like shuttle coordination. And then we can add an additional note, just something like additional shuttle service needed for 2 guests. Click next. And then we get confirmation that, that Slack channel has been created. So let's switch over to Slack. We see that Slack channel has been created, and I'm going to do a little bit of demo magic here because you don't want to watch me type back and forth and have a conversation with myself. So I'm just going to initiate a conversation for you between Sally and Anthony, our shuttle coordinator. And in this conversation, Anthony is able to confirm that he can find someone to help pick up these 2 additional guests. Their problem has been resolved. Sally can go back to her Service Cloud console and she can let Isabella know that there is a shuttle driver that has been coordinated to pick up those additional guests. Isabella says, that's fantastic. The problem is resolved. And now after ending this conversation with Isabella, Sally can go ahead and mark this case as closed and resolved, and she can quickly create a case summary. But instead of having to type this out manually, again, we can use Einstein recommendations. That includes details about this case and any related information or knowledge articles that were used to resolve the issue, saving her time and energy, so that she can move on to solving the next problem. So I'm going to switch gears for a moment, and we're going to move into another story. This is our second and last story. And this is the story about Chris. Now Chris is a service leader also at Polonia Hotel Group. And he's not a service agent, but he manages the service centers across multiple regions to support customers at more than 50 hotel chains and resorts internationally. Now Chris also, he can get a complete view of what he needs to see related to all of the service centers through service intelligence powered by Data Cloud, Service Cloud and Salesforce. For example, we've opened up here the cases dashboard on the Service Intelligence Dashboard. This allows him to see key metrics such as overall total cases, total cases closed, escalated cases. These metrics help Chris prioritize the areas that he needs to focus on. And he can also get insights into, for example, which channel has the most cases created. What are cases by priority status, maybe even by channel, if he wants to drill down and get some additional insights. What's the status by cases that were, for example, created by the phone channel. You can also see this all at a glance and how those cases are trending over time, so if there are any kind of spikes, if you might want to drill down and look at that information in more detail. Scrolling down, Chris can also see how key KPIs like average time to close, escalated cases and average CSAT have performed over time. Now if we take a look at this, we can see that time to close and total escalated cases have been declining over time, which is a good thing. But we also see that average CSAT is declining as well, which is maybe not so good. If he once even looks at this by month, we can see that, that decline is even more dramatic. So this is something that Chris is going to want to keep an eye on. And maybe he can even gain some additional insights into this from Einstein Conversation Mining. So another dashboard that comes with service intelligence as well. So Einstein Conversation Mining uses natural language processing and machine learning to analyze that conversation data to help identify top reasons that customers are contacting the service center. So it calculates key characteristics about the contact reason as well as the average conversation frequency, average number of conversations turns to completion as well total topics by volume. And for those of you who just needs a little bit of extra information, so turns to completion, this is the number of times a conversation moves back and forth between a customer and an agent before it's closed. So looking at this dashboard at a glance, Chris is able to quickly assess which topics have, for example, the highest amount of cases, average duration and conversation turns. For example, I think -- let's see, what do we have here? Loyalty program issue renewal. This has high frequency and low turns, which may be a good topic for him to train self-service bots and create agent-facing or self-service knowledge articles. On the other hand, we've got topics over here like reservation find and member preference chat or invoice and automatic payment. So these have high duration and conversation turns. And these highlight areas that Chris may want to provide some additional coaching for his team. So Einstein Conversation Mining predicted insights, what have we got, seamless Slack integrations, AI recommendations, also Einstein Copilot capabilities. All of these are made possible by Data Cloud and Service working together. So Data Cloud enables organizations to consolidate and enrich the conversation data to gain that in-depth customer knowledge in near real time. And then this information can be leveraged for automation to improve the overall service KPIs and increase customer satisfaction, leading to hopefully reduced churn and increased bookings and revenue for Polonia Hotel Group. So I'm going to stop sharing, and we're going to go back to the slides, I think.

Hey, Kat, before we go back to the slides, I don't think they understand the power of what you just showed. I mean that is game changing. So I just want you to do one more live on the Data Cloud contact record, on that actual record. Just if you can pop it up just one more time and hover on the use cases that it's empowering just to ensure they understand the value of this. Let's stop there just for a moment and just hover and kind of circle around there because I know we have a little bit of time here, and we still have some Q&A at the end. But I think the audience would really appreciate what's happening here, just the moving parts, if they've never seen this before. So if you don't mind...

Yes. We've got a lot of stuff here.

There's a lot going on. It's like getting into a custom car, you have a lot of options and you no longer have the base model. We've upgraded them. We've up-leveled them. So let's show them some of those moving parts.

Yes. Really, the idea here is that someone can log in and they don't have to swivel chair to find out information. Like a service agent can see, okay, Isabella's propensity to buy score here on the bottom left, or her rebooking score, or maybe I probably should have put this more prominent here, the activity timeline because this is important too. This is every single thing that Isabella has done and the time stamps, all of the details. You can see like maybe she had submitted a whole lot of cases back to back, and maybe she has had problems. That would be visible here. Also, all of her preferences. I didn't highlight these in the demo because we weren't really going into her individual preferences. But the things that a service agent could recommend can be visualized here, but they can also be surfaced here over on the right with recommendations that are pushed automatically to the service agent. Like if she was talking to Isabella about her actual reservation and she was on site, we know that she likes, I don't know, deep pressure massage, and let's recommend her a deep tissue massage at Oasis Spa, where she's currently booked. So there's lots of options here to be more actionable. Does that help?

This is what we were looking for. I was looking for us to highlight how we actually personalize that experience. To your point, this is a critical game changer. Just knowing that their booking is one thing, but knowing who they are and the loyalty and the brand recognition and knowing them and their affinity, to delight them, that's the personalization experience that we're affording them. And the proactivity in not being reactionary that when she or he gets there, they then ask, "Is this available? or Is that available?" We're reaching out. We're providing those recommendations, so that they can get to enjoy all of what they've done there at the resort versus running out for different services. And that's allowing us to uplift that fractured swivel chair where the service agent is not looking in system 1 through n to get that information. They don't have to glean or run after that information, we're serving it up to them.

Yes. In the past, an experienced service agent would know where to click and drill down and find this information. A new hire or someone that's just training may not know. And so this allows pretty much -- it gives everyone the information that they need to be that like top-tier experienced service agent by making CRM intelligent. Yes.

And that's what Data Cloud is powering here, right? So that's kind of the demystification of it all. Question is always being asked like, well, why do I know that -- why do I need that? I have an EDW? Why do I need that? It's inside of, I don't know, my back office system. Why do I need this in the financial system? Well, you may want to reconcile that and unify that in one specific portal, right, and provide your agents these access points, so they can get to the business of allowing delightful service versus reactionary service or moreover searching down information, which we'll speak about the vector DB a little bit later on that allows you to power it and kind of parse through all the minutia of PDFs or manufacturing documents that are, again, hard to find in these downstream systems or these disconnected systems with the unstructured data. So that's one of the powers here I just wanted you to highlight of what they were looking at on this canvas, and what this opportunity is affording them, and the experience will be like in their organization, so they understand that connected experience.

Yes. And I think even similar for, I think, the service leader we were talking about with the dashboards that come with service intelligence out of the box, like giving you that best practice. This is the stuff that we know from Salesforce's experience that you're probably going to want to be looking at, instead of having to, as a customer, build that from scratch.

Exactly. Okay. Great. Thanks so much for going back into it just one more time and just kind of wrapping it up. We often kind of go through our demos sometimes and we don't feel that we've given it full justice, but [Audio Gap] that, and I really appreciate that we have the time today to share that with the audience, the participants. So great.

Okay. Let's power through.

Okay. With that being said, Kat's done a great job to do a segue into the next conversation, which is a lot of what we just saw may be standard Salesforce CRM information within our ecosystem, i.e., our apps and our own platforms. But game changing, we here at Salesforce have always been on an innovative forefront, looking at the Data Cloud Vector Database here. What is that? What is RAG? What's happening here? Is untapped data improving the large language model outputs without fine-tuning it? So you need a data scientist to engage with you. So with the introduction of that, businesses now can sync all of that unstructured data along with what's within the ecosystem to create a fully comprehensive, holistic view of that data and knowledge in one place. What knowledge? Well, your articles or any other information that assists with service agent's research and issue resolution. And there are some metrics called out. 70% to 85% of enterprise is actually using and leveraging unstructured data, but many of the service leaders in the room today or wherever you're at, you can relate to all of this. It's really about harnessing all of that data quickly, building it out quickly in an agile manner for automations, workflows and even obviously, like I said earlier, the LLM models and boosting the service agent and service leaders' productivity and driving obviously faster and easier case resolution and obviously improving your KPIs, average handle times, case duration, issue resolution, the KPIs that your organization is tracking. And what happens there, if we talk about the ability to perform platform-native vector store vectorization results within the point of interaction with that chat transcript or that manufacturer's manual or user's manual that allows uplift to parse feed in that different information and have a correlated result surfaced up to the service agent. So again, they aren't doing a swivel chair and having to look down the top 5, top 10 answers to meet that consumers' or your customers' needs. Again, lots of verbiage, but let's unblock this verbiage. So you're harmonizing within one central portal, the metadata framework that allows you a unified view or perspective of that data. You'll see the Salesforce orgs on the side, the external data that I referenced a bit earlier, web engagement, billing and subscription information, any type of consumption data and that external data. I don't know about you, but I've given the use case over a couple of times, so I'll pause at this moment to speak to this one. Imagine you're in a store and you're buying -- or imagine you're just remodeling your kitchen. I think a lot of us have been there. You're remodeling your kitchen, you buy one of the appliance packages. And this appliance package comes with a dishwasher, a refrigerator, a stove and a microwave. Some of these may be premier models and some are just base models and that's the bundling because some of the actual appliances may fare better in the market than others. So they give you a bundled package at an x price point. Great. But one of these appliances within the first 30 to 60 days may be amiss because it has a leaky faucet or a leaky spigot or maybe the bulb or the button goes out on it, if it's a microwave. So let's say that the microwave was more base model of the bundled package than the refrigerator because this is a more frequently used one in storing, obviously, your critical mass nutrients within your home. This one happens to be a top-tier premium model within that appliance package that you bought to remodel the kitchen. Maybe this is a rental property or you use this in your personal kitchen. So you've got to mix and match them. Suffice it to say, all of that information, at some point you need to reach out to a service organization to get some insights on. I recently remodeled my home and I have LG appliances. I don't want to say the other brand that I came from, but some models are better than others within my remodeling package or work that we did in my home. With that being said, if I were to call in when I had to call in about a particular model that went amiss like a button on the microwave or the light flickering inside of it, I don't want to wait on hold for 40 or 50 minutes. I want the service agent, and I think the rest of us would love the service agent to be able to surface up both knowledge articles and knowledge information within the Salesforce ecosystem as a service provider as well as go B2B on what that vendor that they have a relationship with or partnership and surface the expectations of manufacturers' warranties, the repair type, any work orders, any other cases, any highlights, any recalls, any information that might not be readily within the ecosystem there with service subscribe, ingest that information, augment it readily into the Salesforce to create a curated result, summarize those results swiftly, and create -- and even the community, maybe they're tapping into some community information with complaints per se, recalls per se, fixes or it's just time to upgrade that. Maybe the model is a model that was going legacy and deprecated and you bought it at the end of its life per se, and there was a newer model, hence, the discount to get the base model at 30% to 50% lower price point. What does that narrative mean? Well, that narrative means now when I call into the service agent, I can surface up that data to be prompted to say what can I give this customer as they're calling in today to delight them a personalized experience, so they understand the power of the parsing and promptability of our vector DB. What happens at that moment is all knowledge articles are then grounded. Once they're grounded, they then are surfaced and curated and chunked back into a relatable, summarized ingestion point into a knowledge article to give that service agent some insight to what they should swiftly research and call out to the actual customer without telling them to please hold for 15 to 30 minutes while they research that. While they do that and start to transcribe it, tapping on their keyboard, they are then having the quick ability to provide that insight to an LLM model that is much mature, that is much more knowledgeable and insightful based on that trusted data content from that footprint. So you're now having a model that is learning and learning and learning and becoming much more agile as you continue to curate it or augment it. So when you're grounding that data, every prompt is learning more and more and more about those transcriptions of those cases and the touch points. We've all been a customer. We understand the functional alternatives that you can do with other models, but it's game changing that you get that untapped data into the hands of your customer sooner than later than them having to search down information for personal identifying mass information, generating that in e-mail. Okay. Great. Here are some of the Data Cloud road maps and themes that we talk to. Having a deep integration platform, trusted AI, open and extensibility platform and zero copy allows us to have next-gen personalization, unstructured data I just referenced with RAG and our vector DB, summarizing information, chunking it, parsing it, serving and augmenting it, and then serving it back up into the top 5 hits for a service agent to be able to quickly relate and answer a customer's call at that point of interaction and really some of the next-gen analytics that Kat showed you a bit earlier with Service Intelligence and Einstein Conversation Mining. The ability to actually give service replies and knowledge action means taking quick action based on the information that's been surfaced up to you via a unified profile with, obviously, our #1 AI CRM. What's available for us in the next near future? We'll have real-time event ingestions, which is under 200 milliseconds. We have mobile event ingestions with our Data Cloud SDKs. We have linking and profile data graphing back to unified profile, account, contact and linking it to known conversions that match, do a match exact rules. We have real-time insights using our calculated insights doing specific algorithms such as sum and count aggregations. We have real-time segment memberships on our profile data graph, our semantic layer traversing a unified profile up to 7 nodes down to understanding what those actual DMOs and DLOs are as it relates back to that actual unified profile, again, on an account or a contact or an opti dependent on the model that you're trying to visualize or analyze. And this is coming up fall '24. So that's for real time. With trusted AI and unstructured data and our semantic query, again, I think I saw a question that was called out on connectors. What is one of the use cases that we can align and surface this data via the connectors? Here are some of the connectors that you can do that with to unlock the trapped data for unstructured data. So again, it's a semantic knowledge search about customers using Zoomin, which is leveraged with CSS and Einstein Conversation Mining via Slack, Google Drive, QuickBooks, OneDrive and a host of others that are, again, coming up forward thinking. With fall '24, that unstructured and semantic query road map, again, vectorization and chunking of the large data volumes and data packages, I would say, via PDFs, transcripts, that's of knowledge that are allowing you to have a richer passage into the extraction of that data and, again, surface that up at the point of interaction for answering research for our service agents. And then you have a platform search on unstructured data to find relevant content or grounded on GenAI applications to answer those questions with that interaction point as you saw Kat kind of demonstrate a bit earlier within the LWC or the right panel of what's available for our search indexes, which is surfacing up quicker and powering that back to the end user, which in this point is a service agent and/or service leader. Here's how to get started with the Data Cloud. But before we jump into getting started with Data Cloud, I'd like to kind of call out a couple of things that you saw today, just as a quick recap. Again, I don't usually go back to slides, but I'm going to jump back to the slides briefly just as a call out on what's happening here. So I don't know if we can jump back to one of these. But if not, I'll go and push back. So I think I'd like to go with this one. Okay. Perfect. When you see the slide about more data signals and higher expectations, the fact is you're getting a much more personalized and proactive experience. Again, what you're trying to do is personalize every customer interaction at scale. You may have been able to do it before, but now you're looking to scale that out and be leaner in your performance gains that you can get with powering it by Data Cloud, which is how we're aligned today. And we continue to innovate off of that. So you're looking for a daily experience to dramatically raise the bar on that experience that you would not have had before previously.

So we want to go ahead and transition to Q&A.

Let's do it. And first of all, Dr. Von and Kathryn, thank you so much for your amazing presentation. I hope the audience, especially for those of you who stuck around, found like the presentation very valuable. I personally did as well. For the remainder of the time, let's dive right into Q&A. We have a lot of great questions that you submitted into the chat box. Thank you so much. We're going to do our best to power through all of them very quickly. And if you have any questions, but you didn't need a chance to put in the Q&A box, please do so now, because we want to make sure that we're able to answer your questions. So the first question over here, I think it was raised by [ Sugandhi ]. Kat, I don't know if you answered this question before, but connectors needed to connect to other systems mentioned here are part of Data Cloud? Or do they need to be purchased separately? Kathryn, do you want to tackle -- or Von, do you want to tackle this one?

Actually, these are -- I'm going to go ahead and share a screen, too, because I have this actually open. I wasn't sure if we were going to show this. But if you go to salesforce.com/data/integrations, you're able to see what all connectors are currently available right now, and I can't remember what the count is. Yes, these are GA or beta, and there's more coming soon, will be announced. So go to this. And this is a great site to learn about what connectors are available. So these are out-of-the-box connectors. But of course, if our native connector is not available, we also have MuleSoft and all of those connectors and then we have APIs for you to build any of your own integrations. I don't know if you have anything, Von, to add?

Definitely same. Go out there and look at the connectors, but we also have something that we call ideas and voice of the customer. If you have a use case that's prevalent, we take a look at those use cases on a case-by-case basis, and depending on the actual need and the volume and if it aligns with our product road map and strategy, then that net new connector may be taken into account. And you'll see that in beta or in a future release.

Awesome. That's awesome. Here's a question from [ Michele Wolf ]. Would Data Cloud give us the ability to communicate with cases, tasks with users on a different org instance for a company. Von, do you want to tackle that one?

Could you repeat? Did you ask -- what was the question again?

Will Data Cloud give us the ability to communicate with cases, tasks with users on a different org instance for a company?

Yes, definitely.

Great.

Simple answer, absolutely. Yes, multi-org, not a problem.

Absolutely. And the multi-org solution that we are looking at, depending on what your use case and how complex it is, would be by inferring or leveraging what we have something called [ Data 1 ] or remote data cloud.

And that's going to be announced at the Dreamforce, which is one of our key announcements, which is going to be great. Here's another question from [ Sneha Ashish ]. I'm curious to know the collaboration capabilities with non-enterprise tools like WhatsApp with reference to use case #1. So if Isabella wants to communicate, show the coordinations using WhatsApp or another messaging channel, is that possible?

I'm pretty sure that WhatsApp -- I'd have to look back at our documentation, but I'm pretty sure that WhatsApp is one of the included like integrations with the service replies and the service chat as well as other capabilities. Just want to make sure that you're using the most up-to-date integrations there.

So I can take that one. You can definitely configure your system for the integration with WhatsApp. It's a perm set, and so that's in setup and you can do that there. And I don't know if there'll be a follow-up, but I can definitely post it somewhere of how to do that from the Service Cloud perspective.

Awesome. And also, there's actually going to be a blog post on WhatsApp integration with respect to the Zero Copy Partner Network, which should be very interesting as well.

Correct.

Awesome. Here's a question from Subramanian. Was the Service Intelligence dashboard created using the standard Salesforce dashboard? Kathryn, do you want to tackle that?

Yes, I can take that. So this is using the CRM Analytics capabilities that was built on CRM Analytics with Data Cloud data.

And for those that don't know the new naming convention, that's formerly known as Einstein Analytics, formerly known as CRM.

Wave?

Formerly known as wave.

Many names.

Awesome. Here's one question from [ Kennith U Dong ]. Is the service leader dashboard out of the box? Kathryn, do you want to tackle that?

Yes.

Go ahead.

I was just going to say yes. Yes, short answer.

Okay. Great. Perfect. Moving along, there's a question from [ Chris Hollenberg ]. There's 2 questions from [ Sam Arder ]. I'm going to combine them into one if that's okay with you. The first one is what Lightning page component is being used to create that customer card in the top left of the customer record page.

Yes. So in the demo, when we were showing that customer card 360, that would actually be a custom build at this point in time by just using the Lightning component to pull in data from Data Cloud.

Okay. And then a follow-up question that Sam had on a different topic is do Zero Copy connections to our large data stores have an effect on latency?

Latency?

Latency, yes.

It should not. We say it's under 200 milliseconds, so we hope not.

Okay. Great. We're almost done. We've got 3 minutes left. [ Valerie Prest ], will Salesforce assist us in building out the Data Cloud database? Kat, do you want to tackle that one?

I didn't hear the question.

Yes, Von, that was will Salesforce assist with building out the Data Cloud database. So one way to respond to that is Salesforce, we have, our C360 data model, which essentially comes out of the box. We're giving you -- this is what all of the CRM objects mapped together look like for a variety of industry purposes that you can use. If you have -- and that's fantastic that you can use out of the box, but you can also create your -- build on top of that data model. Just like you can in Salesforce today, you can do the same thing in Data Cloud. We showed our professional services slide. So if you want professional services, it can absolutely help with that or any of our partners as well. Von, do you have thoughts?

I was just going to add, we allow customers to build their own data kits. So we deploy these by data kits, So you get an out-of-the-box DMO or data model or semantic model. And then with that model, you can extend that model through your own specific custom objects, your custom fields within that packaging. So the extensibility is vast, which you saw one of the slides I spoke to extensibility, and you do that with a custom data kit with our out-of-the-box DMOs and the data kit that's bundled when you subscribe or you're provisioned that particular license based on what your SKU is. That will be the technical answer.

And you might check AppExchange as well, because we have people that are starting to build out those kits as well to be available there for a variety of different use cases.

We have starter kits as well. So there's a plethora of information out there. And then when you actually align with your AE, if you're engaging some of our success architects, they understand it as well. So they can interact with you on some recommendations from a best practice standpoint to help you so you don't go off the rails, or you don't have high implementation costs, quite honestly, where you've over-customized the out-of-the-box asset.

Awesome, I think we have time for one more...

Stick with out of the box if you can. Yes.

We got time for one more question, one more minute, and there's a few more. So let's pick Sheena. In the Polonia example, what different data sources were unified here? Are all coming from Salesforce? Or are there external data sources there?

All right. Yes. So for the purpose of the demo, the majority of the data is coming from CRM, so Salesforce, but then we're also using, I think, in that environment, we're bringing in from S3.

Plus the Conversation Mining. Yes, the S3 bucket was used for some of those knowledge articles from those technical manuals.

Great. Well, we're at time. And first of all, Dr. Von and Kathryn, thank you so much for spending time with us. And to all the customer trailblazers who participated with us, we hope that you felt honored by the content you listened to and also the questions we've answered. If you have any questions, this webinar will be shared afterwards. Thank you.

Thank you.