Einstein Analytics Keynote: The Intelligence Imperative | Salesforce

Oh, boy, oh, boy. Welcome, welcome, welcome, welcome to the analytics keynote. My name is Ketan Karkanis, SVP and GM of Einstein Analytics. I want to welcome all of you in the room. I want to welcome thousands who are watching us online, and I also want to say sorry to the people who could not get in and are in the overflow room next door. Guys, we'll do this again for you, but let's get this going. It's all about data analytics and AI. We're going to talk about that a lot in today's keynote, but actually what we're going to do is think and talk and show you. How does our technology give you superpowers? So imagine with me for a second. Most of you have probably seen a James Bond movie, right? James Bond Goes to cq. And Q is showing all the technology today. Me and my team, we are going to play the role of Q and we're going to show you a lot of cool technology with the only intent of giving you the superpowers for achieving better business outcomes. That's all today is all about. And no matter how many superpowers you have, I am pretty sure everybody likes free ice cream. AI plus free ice cream. That's at the end. So stick around for that, you know. Love you too. This might be the slide me and my team have spent so much time on. I know it looks simple, but you've heard me say this before. Me and my team, we sincerely believe all our best ideas are not ours, they're yours. Your feedback is features and your energy drives the innovation. You guide us, you keep us honest. You are our North Star. So thank you very, very, very much for everything you do for me, my team, this product, and all of us together. Thank you. Now, we have a lot to cover today. A lot. So I'm going to cut the usual preamble and we're going to get right to it. So pay attention, take pictures, take notes, and avoid blinking because you are going to miss something. Here we go. You know, it's all about questions. You have questions. How do I increase my csat? How do I close deals faster? How do I reduce customer churn? Is my pipeline forecasting accurate? And thousands of such questions every day in your sales and your service. And the common thread across all these questions is the deep desire to be proactive, the imperative to be predictive, and the demand for guidance. And you need all this when you are in your Customer360 platform doing sales, service, marketing, your core CRM business processes. Because if you think about it you can bring it all down together to one simple thing. The role of analytics and insights is to help you achieve better outcomes and a much more optimized customer experience. That's it. And your playbook is something like this. Create a virtuous cycle of innovation fueled by insights. And these insights are, are connected insights. They are driving intelligence into your business, application, sales, cloud, service cloud and AI. As you saw, Einstein came in later. Because Einstein's not the protagonist, Einstein's not the superhero you are. Einstein's role is to be a supporting actor. And when you think about it, to what me and my team have been laser focused on is to deliver these insights to you, these connected superpowers to you. And to do that, we did something quite special. We brought BI and AI together, we infused it and then we made it native to Salesforce CRM. That's what we did. And some call it actually Gartner calls it augmented analytics, Forrester probably calls it cognitive analytics. Everybody has a word for it, everybody has a name for it. Some say it's the future. We call it Einstein analytics and it's available today. And now let's walk through with this powerful augmented analytics native to your CRM. Let's take it for a spin. Let's see what you can do with this. It's kind of straightforward. Everybody wants to look what happened in their business. But now you have the power to see the future. You know a trend line should never ever stop at today. A trend line needs to show you what will happen. Your day should not have to start with guesswork, but a crisp prioritized list of what needs your attention. And we all know and love KPI reporting. Of course we love it. But also you need plus intelligent applications. Intelligent applications when the contextually relevant insights are available to you, exactly when you need it, exactly where you need it. And they are not just backward looking, they are forward looking. Which sales rep would not like to know predicted days to close? Everybody would like to know that whether you are in Salesforce or you want to take these predictions outside, whether in front of your computer or on a tablet or a mobile device. Everywhere, everywhere you are. We all need the need for manual data exploration, of course. But wading through millions of data points and sub second speeds, that's a superpower. And now you have it. With automated discovery, with one click you can discover patterns through all your data and take contextually relevant actions. Now of course we need charts. Charts are beautiful charts. Say a thousand words, sometimes it would be good if those thousand words are Written down in plain human speak. That's why you need narrative stories which explain to you, explains to you what is happening in your data. Why is it happening? Brings you up to speed. Now, another superpower is, you know, you. You of course need governed dashboards, but is there anybody in this room who has been always completely, hundred percent, fully, fully satisfied with their dashboard and have no further questions to ask? It's not possible. Not because the dashboard is bad. This is the game of analytics. You ask a good question, you get a good answer. You get a good answer, you have two more questions, then you have two more questions, then you have two more questions. This is the question and answer tango. And that's why you just need to be able to ask the question. We think data needs to understand you. We think data needs to learn your quirks, your business, your language. And you should just be able to ask your question and receive again, contextually relevant insights which are actionable. Einstein analytics is only about one thing, actionable insights where you work embedded in your CRM. Now, we all know the power of a one size fits all dashboard, and we have received many of those in our lives. But we know you don't just want that. You want things the way you want them. You want them to be personalized according to your need. That's why we are introducing a new construct of dashboards. It's called a watch list. All the KPIs you need to monitor auto trended for you exactly where you work. And thousands of customers worldwide are paving the way, whether it's Schneider, whether it's Indeed, whether it's PwC, all three of them are here right now. They're going to join us up on stage in a bit and share their stories. But this keynote is special for one more reason. You know, normally, of course, even today you're gonna see some of my team members come up and do demonstrations for the product areas. But our customers are gonna do their own live demos and you should be applauding for that when they come out. So don't forget that, cheer them around. So here's it, as I said, simple framework, customer experience, better outcomes, actionable insights, intelligent applications. So we are going to start creating this virtuous cycle right now in this keynote and to take us forward on that journey, I'm incredibly excited to welcome on stage my copilot and product marketing extraordinaire, Ali Witherspoon.

Thank you, Keitan. Thank you, everyone. So I am here to bust a myth with you all today. And that myth is that every analytics process project starts with data. Obviously data is important and you hear this everywhere, but I'm going to challenge you to think in the opposite order. So start with the outcome you're optimizing for, just like Kaitan talked about. What is that business outcome you're driving toward? What is that connected insight that you need? And then design your intelligent application or your entire intelligent experience around that insight. So that's what we're going to talk about today. Intelligent applications. Intelligent experiences. And I think one of our trailblazers at Telstra, Mirco, said it best himself when he said that the powerful combination of predictions inside of business applications can be a real game changer for employees. But the problem here is that 43% of companies lack an AI strategy. And we hear this from our customers a lot of the time. And so that's exactly why we built and launched Einstein analytics plus for you all last year. Einstein analytics plus is the most complete intelligence platform built on top of the world's number one CRM, obviously Salesforce. It's connected, it has AI built in, it's complete, and it has the enterprise speed and scale that you have come to expect and love from Salesforce. You guys are doing tremendous things with this product. You are running 34 million predictions per month, 5.7 million dashboards per month, and I feel like you're just starting to scratch the surface. So you inspire us all every single day. So we've been working really hard over the past year to advance the AI capabilities that come with Einstein analytics plus. And that is why I am so excited to introduce our latest innovation to you all that allows you to ask any question of your data and receive a personalized, intelligent in context answer. You saw a sneak peek of this in Keitan's opening slides, but I'm going to break it down for you a little bit. So the idea here is that when we're in our personal lives talking with our friends and family, we have a very natural, casual, conversational way of speaking. But business lingo and business jargon is a bit different. It's technical, it is industry specific and it is loaded with embedded context. And so because Einstein analytics is built in C inside of CRM, you're hard coding all of your language, all of your lingo, all of your business processes inside of Salesforce and Einstein can learn from that. The problem is that we don't speak data and data doesn't speak human. So Einstein is here to help us translate. So I'm going to geek out a little bit and tell you what's going on under the hood here? Essentially, Einstein is learning from all of these signals in your data, all of the assets in your data lineage, and he's building a semantic graph on top of that. This isn't just any semantic graph. It is powered by deep learning. And so we use one specific natural language model called long short term memory, Recurrent neural network. Say that 10 times fast. And this model is extremely powerful and it allows Einstein to surface these predictions and these recommendations in this chat like interface. Now we've also, aside from the AI, been working really hard over the past year to deliver tailored analytics to all of you, no matter what your role, what your industry, what your use case. Now, you know our bread and butter is sales and service. This is where we got our start. But we heard feedback from all of you. You want analytics in your flavor for your industry. And so that's why we've launched Einstein analytics plus for financial services, for manufacturing, for healthcare and consumer goods. So you can think of it like a flavor of analytics for every industry. And to carry the ice cream metaphor out even further, if you think about the key ingredients of ice cream, it's the same three key ingredients. It's milk, it's cream, it's sugar, and then you add your flavorings in. And that's the exact same with these industry products, same three key ingredients. It's the data platform which Katie is going to talk to you about next. It's editing AI and it's those tailored industry specific KPIs and best practices. And then we of course add some fun little industry sprinkles on top to make them super specific for each vertical. So now I'm just going to spend a few minutes breaking each one of these down for you, starting with consumer goods, which will be available in just a few short months. It's all about helping our retailers uncover key insights and trends in their accounts so they can increase average order value. Right? We all want to increase revenue if we're a consumer goods company. But we also ship these insights alongside powerful predictions like which store will perform the best. Next up on the list is Healthcare, where we are helping payers and providers really increase patient care and how we're delivering care to every single patient with really actionable analytics. Things like average cycle time and identifying at risk members. But then we can also use AI and pretty predictions to understand referral conversion rates. This product is available today. Next on the list is manufacturing really helping understand the ins and outs of your accounts. If you're a manufacturing company, understanding Lifetime value where your time is best spent, all of these key pieces. But you can also do things like understand your white space and predict your forecasts by each account. So once again, descriptive historic analytics side by side with predictive analytics. This product is also available today. And last but not least, we have my personal favorite, Einstein analytics for Financial Services, where we are helping wealth management, banking and insurance companies with a complete intelligence platform for financial services. So you can really manage your entire client portfolio while engaging them and helping them reach their goals. You can see things like churn deposits, referrals, everything that you need if you're a financial institution institution right on top of financial services cloud. So with the help of the amazing Live Demo team, Choks, Shardul and Terry, we are going to show you Einstein analytics for Financial Services in action. So let's pull up the demo. So for the purposes of this demo, I will be playing the role of a wealth advisor at Cumulus Capital and I work in financial services cloud every single day. But this isn't just any financial services cloud. This has been through supercharged with Einstein analytics embedded right there. And immediately in the upper right hand corner I see a task that's calling my attention and it's telling me that I need to check on my high AUM customers. AUM's assets under management, very important customers. And then I also see on this dashboard to the left, kind of a red indicator that's catching my eye and it's telling me my Churn score has increased by nearly 4% since last month, which is pretty alarming. If you're in financial services, you do not want to lose anyone from your book of business. So I'm going to click into this insight to learn what's going on with my Churn score. And right here now I have a full Churn dashboard. But I remember that task in the back of my head that's telling me to check on my high AUM clients and so I can click and filter by those clients that have over 500,000 in assets under management. And Einstein analytics helps surface this somewhat alarming correlation that my high Churn customers are also those with the highest aum, which is not good news. So I have this insight and now I can click and learn more about these clients in my client dashboard. Once again, out of the box dashboard that comes with this product. And I can do things like see my top clients, I can see my demographics, I can understand my client goals and that's all fine and dandy. But as Keitan said, there are just some Questions that, that a dashboard can't answer. And so that's where I go to my friend Einstein and I can just start asking him my questions in my natural language. So we're going to show you that exact same workflow that I just worked through in dashboards, all in this chat like interface. So I'm going to ask Einstein for my top clients by Aum and I'm just speaking in my natural language. He's going to surface up a dashboard, but he's also surfacing up a prediction that I should view this alongside predicted Churn. So I click on that exception and I take it. And I also want to know which of these clients have a high probability of Churn and I want him to help explain those top clients. What is going on with these clients that leads them to be a high Churn client. So once again, those three dashboards that I started this demo with, I've got to the same level of insight just by asking Einstein these questions, but these are also actionable conversations. So right from this chat transcript I can click into this dashboard and I can understand more about Norman Clark, this client that's being surfaced as both high AUM and high likelihood of Churn. I can see Norman's entire account360 on one page, everything I need to know about him. I see his aum, his wallet, share his net worth, but I also see handy dandy Einstein prediction in the upper right hand corner. And this comes out of the box. With Einstein analytics for financial services you get a pre built Churn model for you when you buy this product. And this is telling me here that Norman has a high likelihood of churning. It's 25%. But with all of the predictions that we ship, we also explain why. So you see the predictive factors underneath and we also tell you what to do about it. It's not not just a prediction, it's a prediction with a recommendation for improvement. And so we can accept that recommendation and we can change the review frequency to quarterly. And once we do that, you'll see in real time the Churn score decreases to a much healthier state of 9%. So in this brief demo, you have seen me as a client advisor go from that task to an insight to a prediction, to a, a recommendation to an action. All within financial services cloud where I already work and you saw the power of our new conversational features. So let's go back to slides. While that was a fantastic example of AI deeply and analytics deeply embedded in CRM in the business process, we're no Dummies. We know you guys work outside of Salesforce. You have other systems that you work in, whether they be ERP systems, whether they be finance systems, HR systems, Excel, the list goes on and on and on. And so we knew that we needed to make our predictions exportable. We needed to give you guys the ability to put your predictions in whatever system you need them at the point of insight. And so with Einstein predictions you can do just this. You know, Einstein predictions for its automated data discovery. It's known no code machine learning, but we've also been working really hard to deliver the most trusted AI imaginable so that you have bias protection, you have live model monitoring, and you have predictive factors side by side with your predictions. And as I mentioned, with our brand new API, you can now take every prediction that you build in Einstein Discovery and put it in whatever system you need it in so you have it at the point of decisioning. This is available today. And there is no one better to show you the power of Einstein predictions than our trailblazer. Christine Robertson with PwC.

Great.

Thanks Allie. I'm so excited to be here with you to share how we are leveraging Einstein at PwC to help us with our digital transformation. So you may not know, we are a global network of firms and we have over 270,000 people in over 150 countries. We've invested in Salesforce to help us on our global transformation and we have already really seen the impact of leveraging Einstein. We've got over 33,000 people who have actionable insights right, right there as they're working with their clients every day. And we know that by adopting AI and analytics as well as some of our digital upskilling initiatives that we are going to be able to unlock the creative power of our people across our network. So I'm going to show you a demo of how we are doing that. So in our largest territories, we've deployed Einstein analytics and Einstein Discovery to bring insights, To bring insights right there to our, to our people who are working with our clients. And so as a partner working on an opportunity, I can see first at the top of the screen, an embedded Einstein analytics dashboard. And it's showing me a summary of insights around my client, including a trend on meetings and activities that are taking place, our pipeline across our lines of service and the average days of our sales cycle. Over on the right, I can see Einstein Discovery is telling me the predicted time to close for this specific opportunity and it tells me the top predictors of why it's made this prediction as well as next steps that I can take to improve that outcome. So this might seem like a simple use case, but it is really powerful in helping us with our confidence around our forecasting as well as making our resource management more efficient. So, so that's where we're making sure that we're getting the right people onto our client engagements. So we are again, really excited about this and we are continuing to look at new ways and adding more models. And so because we do have a few models that are in production today, it's really important for our teams to be able to monitor their performance and check their accuracy. So live model monitoring allows us to do just that. So I can see that I've got a couple, couple of different models here, win rate and time to close across different lines of service. And as I click into one over on the right, I'll be able to see the accuracy and performance metrics again. This is so important and so impactful to us because it is helping us to see what are the factors that are really influencing our sales cycles as well as for us to drive adoption and drive new behaviors because we can see which values are most impactful to this time to close and allow us to again, really drive out those adoption programs around those behaviors and improve our data quality. And in fact, our resource managers are now looking for, we're creating a proof of concept that I'm going to show you in a minute around how we're using Einstein prediction service to be able to take these predictions into our resource management system, which sits externally to Salesforce. So I'll show you that in a minute. But before we go and do this, what we want to be able to do is verify that data. And this is a really powerful tool for us to use and we're really investing heavily in our digital upskilling of our people. And so as a business user, it's really powerful for me to be able to create a story myself in order to create the model and then be able to have these predictions. Okay, so to create a story, the first thing I need to do is decide what am I trying to solve for. So with the set of data opportunities. Sorry, the set of opportunities that I have, I'm going to look at the time to close and all I have to do is click Create. And because I want to verify the data first, I'm going to select insights only and then I'll come back to do the predictive options after. So my next step is I need to decide which fields I want to include in the model. However, I actually don't have to do this myself. I can choose automated and Salesforce will select the fields that are going to be the most impactful to the prediction for our time to close. So once I create that story now I'm going to be able to take a look at the insights that Einstein has found for me. And Einstein even makes suggestions on what I can do to improve the outcome. So I can edit the story and I can take out the outliers and then create the predictive model. And with that I can now deploy.

It.

And once I deploy the model and we'll do it with new prediction and deploy the model outside of Salesforce. And there we go.

That's it.

We're ready to go. So now I want to show you that proof of concept around how we can then take that into our external resource management tool. And the power of this is being able to take those predictions of when those opportunities will close into another system where I can also change the factors that are influencing that time to close because I might have new information as I'm working here in this tool so I can change the line of service product, the hours, resource allocation and update and get those same predictions and insights here in this tool right here where I work. No need to switch between systems. So we are really excited about that. So Einstein is really helping us to bring those better actionable insights to our people who are ultimately helping us with, with better supporting and driving more value for our clients every day. Back to you, Ali.

Thank you, Christine. There's nothing more that I love than seeing our customers do their own demos. I think it's so powerful. So just to recap what we talked about today, lots of new products, all of our industry's products, with the exception of consumer goods, are available today. Consumer goods, it's coming in just a few short months. And then Einstein predictions, what you just saw Christine walk you through is available today. So while I kind of trash talked on data a little bit, it is still extremely important in your analytics journey. And there is no one better to walk you through the data platform than one of my dear, dear friends and colleagues. Katie Augustus.

You did trash talk it a little bit. Data is the foundation of the intelligent experience. And when it comes to data, I think we'd all agree that the goal is to spend less time integrating data and more time delivering actionable insights to our users. But unfortunately for many of us, that's just a dream. IDC recently conducted a survey of analytics professionals worldwide to understand how we actually spend our time. And they found that unfortunately we spend the majority of our time doing anything but analyzing. We're searching for data, we're cleaning and prepping our data, we're dealing with data governance. And that leaves such a small slice of time to do the work that actually delivers meaningful value to our users and to our business. And that's not a people problem. And it's not that we have bad tools. The problem is actually that we have too many tools. Because this is what a visualization stack actually looks like. You've got data catalog tooling, you've got ETL and Visual Data Prep. That's two tools. Then you've got some sort of automation, and then you've got your analytics data store. And then you can start doing some sort of visualization and dashboard building. And there's so much friction and time lost between switching across all of those tools. But the worst part is that the second you want to add any sort of machine learning into the mix, you have to do it all over again. But here at Salesforce, we believe that you should spend less time jumping between tools and migrating data and leaping across stacks, and spend more time focused on your customers. And that's why we took all of that disparate tooling and turned them into capabilities of a single unified data platform. That comes standard with Einstein Analytics. Plus, with your Einstein analytics data catalog or data platform, you get data catalog tooling, you get intelligent visual data prep, you get an enterprise scale data platform, and it's all built on a high performance data store. This Data platform powers 15.1 trillion rows of data being analyzed every month. So let's walk through the capabilities that make this all possible. First up, I'm excited to announce our Einstein analytics data catalog, which makes it easy for you to understand and interact with all of the relationships between your data assets. With a matter of clicks, you can easily explore every field and object and the full data lineage of all of your assets down to the original source. And you can even take action natively in context. I'm also excited to say that we continue our momentum with connectors. We have over 50 connectors available out of the box, which make it easy for you to sync to all of your external data sources. We recently announced our high volume tiers for Amazon S3 as well as Snowflake. And today I'm especially excited to announce that we've completed our integration with mulesoft. So that means that all of the connections that you've set up in MuleSoft you can leverage in Einstein analytics and once you've synced your data, it's time to prep it, right? And with intelligent data prep, building data sets has never been easier. With visual profiling and interactive previews, you can easily spot all of the patterns, distributions and anomalies in your data. And with just a matter of clicks, you can build the data flow from the ground up. And because it's powered by Einstein, all of the intelligence that you expect comes built in, whether that's with recommended transforms or predicted missing values or even sentiment detection. And lastly, this is all powered by our high performance data store, which is built for the demands of the future. This is no one trick pony. Our high performance data store is capable of managing both your analytics and your machine learning workloads and connecting directly to your business apps. And it's built with speed, speed and scale in mind. Our query engine is blazingly fast, leveraging an inverted index and columnar data store for instantaneous interactive queries. And it's built for scale with no configuration required. Distributed parallel processing and auto concurrency scaling mean that you can expect that same lightning speed even as your data scales to billions of rows or your users scale to hundreds of thousands. And we're salesforce, so all of that enterprise scale security comes standard so you can manage governed and trusted access across your entire organization. But we get it. Sometimes you might not want to use our data store and it's true you'll compromise on performance, but we still have something for you. Today I'm super excited to announce Einstein Analytics Direct Data so you can query your data where it lives without moving it. We're starting with Snowflake Direct and making progress on redshift and GCP coming soon. But you know, that's enough of me talking. Let's see all of these capabilities in action. I'm please welcome Irina Zubova from Schneider Electric who's going to help me with a demo.

Welcome Irina.

Thanks Katie. So happy and excited to be here with you today and have this great opportunity to share Schneider's message as well as our journey with Einstein and its six. Let's start with Schneider first. Schneider Electric is a growing technology company. We provide energy and automation digital solutions for efficiency and sustainability, empowering our customers to make the most of their energy and resources and ensuring life is on everywhere for every time, for everyone. Now, success for us means means reducing carbon emissions, optimizing and increasing efficiency of our energy. And of course we are working with our customers and our goals are to anticipate customer needs and to partner with them to Provide full visibility in real time and the best quality. We have more than 180 years of experience working in 100 countries around the world, ensuring service for our customers, for growing markets, growing end users like homes, buildings, data centers and infrastructure. Now it's time to go for a quick demo to demonstrate you how Schneider Electric use Einstein analytics in our real life. As an executive, it's important for me to have all my business in one view, in one place. With this dashboard on my iPad, I can see all the indicators I need to manage my business and to ensure we are meeting our goals. I can easily track the growth of my business, the efficiency of my company, the quality of our services. Let's drill in a bit on growth to see what's happening there Now I can see even more details like, for example, installed base distribution by region. But wait, one crucial piece of information is missing there. We are collecting customer feedback via surveys, but I do not see this important information in my dashboard. Let's ask Katie for help. What do we need to do to bring this information to the dashboard?

Well, Irina, I can definitely help you with that. So it's my job to make sure that Irina has governed access to the insights she needs whenever she needs them. But that doesn't necessarily mean that I have to make Irina a dashboard. I just need to connect that survey data to her business insights. And with the help of Einstein, Irina can explore that data however she wants. Now, with the Einstein analytics data platform, I have all the data tooling I need to address Arena's request quickly without switching contexts. So, first things first. Arena's interested in survey data, so I want to understand if any of that survey data or any related data is already available in Arena's dashboard. With the Einstein analytics data catalog, I can get a view of all of the data assets already associated with Arena's dashboard. I can see all of the data sets, and if I drill in, I can see the objects and fields and the full data lineage down to the original data source, which it looks like for this is just Salesforce. I know that that survey data lives in an external system, so I'm gonna have to connect to it. Let's flip over to the data manager where I can do this right away. I can see that I have a host of connectors available to me out of the box, so I can sync to a myriad of data sources. But our data warehouse lives on aws, so I'm gonna open up my existing connection and right away I can see that there's my survey object right there. I click continue and I'm going to select the fields that I want to bring in. Let's take a couple of these. Once I continue, I actually get a preview of this data so I can make sure that the data looks good before moving forward. Perfect. Once the data's synced, it's time to prep it. And with visual data prep here, I get a full view of the data flow that's powering Arena's dashboard. I can see all of the original data sources, all of the transforms, and the resulting data sets. I want to join that survey data with my account data. So I'm going to add a join, select that survey data, move forward. Perfect. And Einstein's actually going to help me the whole way here. Einstein's suggesting that we join on the email key. Looks great. Thanks, Einstein. Let's go ahead and add that. And back in data prep, I get a full preview of all the data that I've connected here. I can see the raw survey results, but I think that Irina is not interested in just the raw responses. What she probably wants to know is what the customer was feeling or thinking when they submitted those responses. Yeah, well, Einstein is two steps ahead of me with Einstein's suggestions. I can see that for this column. Einstein's suggesting that we detect the sentiment. I'm going to go ahead and do that. And Einstein is going to predict the sentiment associated with each of those survey responses. Einstein, Einstein's going to add a column to my data set here. It looks great in my preview. So I'm going to move forward and write this back to my data set. Irina, your data is ready.

Thanks, Katie. Now that the data is ready, I can just ask my questions directly to Einstein. Like, what is the sentiment breakdown? Interesting. Einstein is suggesting that this type of data is often viewed by region. Let's take this suggestion. Now. I can see clearly that I have a big number of accounts with negative sentiments in northern region. Let's dig into that. Show me top accounts in northern region by revenue. You see, just by asking few simple questions, I have all answers I needed. This is how Einstein analytics helps Schneider to create and deliver this type of experiences. Thank you, Katie.

Thank you, Irina.

Thank you.

Well, you've just seen the power of the Einstein analytics data catalog to deliver intelligent experiences, or the beta platform to deliver intelligent experience. And that included our data catalog, all of our connectors, our pipeline and our high performance data store. And all of these capabilities are available as part of the data platform today. And for those of you who are Einstein Analytics plus customers, you know that 100 million rows of data come standard with Einstein Analytics Plus. Well, for all of you folks out there, and for you Einstein analytics plus customers to be. After this session, I'm excited to announce that now 10, 10 billion rows of data comes standard with Einstein Analytics.

Yes.

But it's not just about the data. We have to turn this into a reality. And so please join me in welcoming Lauren, who's going to talk to us about how to organize our teams to bring insights into the customer experience. Lauren.

Get them. One of the things I love about my job is that I get to work with customers every single day. And from working with many of you in the room, I know that you all want a great analytics solution. But what's the key to that? And I think one of our trailblazers says it best. The key to a successful analytics project is to focus on the business outcome, to collaborate, and to iterate based on feedback. But sometimes the hardest part of an analytics project is just knowing where to get started. And that is why we are here to help you organize for success. No matter your industry, no matter the size of your company. You can follow these three easy steps to get started today. Step one, assess where you are in the Analytics Maturity model. Step two, define your business use case. And step three, create a culture of curiosity. So let's dive into that. Step one, assess where you are on the analytics maturity model. Now, I've met with many of you in the room, and I know that all of you have some type of way you're finding insights into your data. Whether or not that's an Excel spreadsheet, a chart, a traditional BI tool, you all have some type of idea what is happening with your data. But where things get a little bit trickier is the other two pieces of the analytics puzzle. Finding why is something happening as well as what action you can take. It's important to assess where you are on this maturity spectrum and then from there you can laser focus on what your use case is. And that brings me to step two. Discover your use case. In the beginning of this presentation, we saw K. Tom present a slide that had all these different questions you could ask about your business. How do I decrease my csat? How do I increase my csat? How do I decrease my customer churn? We want to increase csat, not decrease that. But there's all these different questions you can ask about your business, right? And it's important that you're focusing on the business outcome. A lot of times when people start analytics projects, they're focusing on the technology or the data. We are here to tell you, do not focus on the technology first. Focus on the business outcome. And this is something that's really easy. So we're actually going to do this together. So let's say I want to increase sales. That's my business outcome. Who in the room wants to increase sales in their business? Everyone. Me too. So you want to increase sales in your business. Right. So let's think about that. What are the KPIs that matter to me? I need to know how my pipeline is trending. What else do I need to know? I need to see stage analysis. What insights do I need to know? I would love to see what my win loss probability is of an account. And I would also love to have a time to close prediction. And lastly, what's an example of an action you could take? I could run additional campaigns to boost sales. Now that was a very simple example. But that's the point. An analytics project isn't supposed to be this crazy, daunting task. It's something that you can all take out your phones right now and and think about. You could write down what's the business outcome I want to achieve? What are the KPIs that matter to me and what do I want to find insights into? Now you can have all the right data and all the right framework, but if your employees don't adopt it, it doesn't matter. And that takes me to step three. You need to create a culture of curiosity within your business. In your business you have these four varied siloed groups. You have your data scientists, your business user, your analysts and it. But what do these four groups have in common? They all need insights into their data and they all need to be centralized around a business outcome. So how do you do that? We have four values to help guide your transformation. One. Agility trumps perfection. It's no longer the time to wait five years to start your analytics project. Your data is never going to be perfect. The time is now. 2. Experience inspires adoption. The best way to get your users to start using a new technology solution is for it to be intuitive and easy for them to explore themselves. 3. In context is not optional. Earlier in this presentation we saw Ali talk about the importance of having insights right within your business process. 1. In context insight is more valuable than 100 out of context dashboards. And lastly, AI is a collaborator. We need to see AI as a helping hand that can help boost your employees productivity. Now we are really lucky today that we have a customer who has Actually brought these four values to life within his organization. So everyone please join me in giving a warm welcome to Karan Parakh from Indeed. Can we go back a slide? Thank you. Thank you for being here with us today. Thank you.

Thank you.

Can you start by telling us a little bit about Indeed and what your role is there?

Sure. So for those of you that may not know, Indeed is the world's number one job site. Our mission is super simple. Help people find jobs. And our role in sales strategy is to accelerate that mission by innovating tools and scaling processes so that we can help our sales teams and our customer success teams go faster and grow faster.

Great. Now everyone in the room is here to learn about Einstein Analytics. And I know with you, Einstein analytics wasn't exactly love at first sight. Can you tell us about how that looked?

I'm going to give you guys a secret. Only you guys are going to know this. Okay. When I first met the Einstein team, I don't think they realized that I saw there was a giant jar of red Kool Aid in the corner and they expected me to drink all of it. But all jokes aside, I think what clicked for me was when I first saw the action framework, I immediately realized that we could have these analytics be leveraged by our sales teams and our customer success teams and they could take action on it right away. It's almost as if Christmas came early. So it was really easy for us to realize that our sales teams would be the initial users of our first implementation because we want to keep them selling and we don't want to keep them working in thousands of spreadsheets. And so it was easy to deliver them analytics that they could take action on so we can maximize their time to sell.

Great. And earlier we were talking about change management. And as we know, change management is hard. Can you talk to us about how you address that at Indeed and how you helped boost adoption?

Oh, yeah. Sales is not easy, to say the least. I think the angle that we try to use to help drive changes, change management, was to make sure the customer is always right. And we didn't want to necessarily go in and break down and revolutionize what sales does. We wanted to augment what they do incredibly well today so that again, we can keep their time selling. And so it was really augment the strengths and help solve for the pain. And together, I think it was really easy for sales to get behind that.

That's great. And in addition to your amazing adoption metrics, what. What other results have you seen?

I think I can Write a novel for all the feedback that we've gotten. So I'll share one story that really resonates with me. There was a sales director who was one of the initial users of Einstein, and this person managed their business. It was a very, very lucrative business with 20 different Google spreadsheets. Now, I know everybody here probably has had some experience with managing spreadsheets, but this was unbelievable. And so one of the greatest points of feedback that I think our team ever heard was when this sales director was able to forego all of their spreadsheets as soon as they were able to enter Einstein and manage their day to day technologically. That was amazing.

That's great. And lastly, I know that you're big into getting feedback from your team. Can you tell us about what advice you would have for the audience?

Yeah. I think first and foremost, you need to make sure you have an incredible team. And I got super lucky that we had an incredible team at Indeed. So a major shout out goes out to Kelsey Shannon, Dayden Calderoni and Monica Moore, our product team and our engineering team. I don't say this too often, but thank you guys because you made our users the obsession. Right? And I think that's the advice that I would give to everybody, is that user centricity should be the cornerstone of every single iteration or every piece of feedback or every sort of scalability program. By obsessing endlessly about our users and caring about everything they need, you're innately obsessing about the experience you're trying to deliver. And suddenly your dashboards and reports aren't static pieces of information. You're creating experiences together. And so my advice, care about the users, obsess about them, and make them your future.

Thank you so much for being here with us today, everyone. Let's give another round of applause. So we just saw an amazing analytics leader, but we have great news. We do not expect you to do this all by yourself. We have all these different tools like trailhead, the learning map, the community. And we're actually really lucky today that we have two Einstein analytics champions here with us. Einstein analytics champ. Yes. An Einstein LX champion is a trailblazer who is a subject matter expert at analytics. So with that, let's all give a warm welcome to Jennifer Shire to tell us about the community.

Come on, data champions. I know you want to hoot and holler.

Sorry.

Oh my goodness. I'm so happy to be here. Thank you so much for having me. I have to start out with something because I left my son at home. And it's his ninth birthday today. So happy birthday to my son, Josh. Family first, always, right? So whenever you work with data, you learn a lot about yourself. And one of the things that I learned was that I speak from my heart. I was given a script to come up here today, and I realized that's just not me. I'm 100% true to myself. So I'm going to be a little bit vulnerable with you today, and I'm going to tell you that this is my 18th year in the Salesforce ecosystem and my 16th Dreamforce. And, you know, somewhere along your career path, you have to decide, you know, I can't be an admin forever. Well, I can, but I can't. So I decided along my path that I had to be once again true to myself. And being true to myself meant I'm a data geek. I'm sorry I'm a data geek, but that's just who I am at the end of the day. And when I went to make that transition, it was a little scary for me. Where do I fit into this E ecosystem that we have? And how am I going to start this? You know, I can't go back to Harvard, right? I can't go get a data science degree. But guess what? Einstein analytics is out there. It's a gift you can give your career. If you are a data person and you even think that you have an inkling, just an inkling of wanting to do this, it's very easy to pick up. The only barrier is you, your want. You just have to want to do it. So once again, back to the vulnerability. I decided that I was going to go in the community when I first, you know, I'm going all in. It's like driving the first time you get in a car. You don't know how it works, and then pretty soon you're in the fast lane telling everybody, get out of your way. Right? So baby steps. So we have this wonderful trailblazer community out there. And the first time I went in there, Pete Lyons was actually answering almost all the questions. Datatribe, you know, Pete Lyons, Yes, I answered or he answered my first question in there, like in five minutes. I thought, wow, this technology, it's so approachable. And then also the community is very friendly and fast. This is actually going to work out for me. And so, you know, fast forward to today. I'm in the fast lane driving my car. So if I can do it, everybody out here can do it too. And if there's anybody out here who's Even thinking about changing their career path and giving themselves the gift of Einstein analytics, the community is where you start. So I owe it to my mentor and good friend Pete Lyons for getting me where I am today. And he's next up on stage.

Jesus. Thank you so much, Jen, for being here.

Thank you. I appreciate it. Appreciate all of you. Can I stay up here with me?

Sure.

And everyone, let's give a warm welcome to Pete, who's going to show us a demo.

All right, so I'm Pete Lyons. I'm with Huron Consulting Group. I'm also host of the let's play Salesforce YouTube channel. And I was not always a technologist. I was a cook. And so for me, training and enablement is mission critical. We have to be able to come up to speed on this technology and it's mission critical for Salesforce as well. So we're going to show you a quick demo about this. So this is the new homepage in the winter 20 new analytics studio homepage. And if we look on the left hand side, we will see a link to the learning center. Up at the top we've got all of the different trailhead badges that we've taken along the way. Down at the bottom we have new releases, new release information about the new features that are coming out that we need to come up to speed on. And additionally, we can link to one of my favorite pages, the learning map. This is going to. This is a curation of all of the different resources that we have available to learn from. So for example, if I want to click on Add Intelligence, I'll see that I have things like description, discovery, data insights and prediction builders that are going to allow me to add AI intelligence to what I'm building. And if we scroll down, there's actually a little bit more. We've got this nice extra tabs down at the bottom here. One of them. Meet our trailblazers. This section is all content created by people just like you. People who learned on their own, people who reached out through the community and created this content. If you write a blog post or create a video and teach everyone how to do something, reach out and we can help you get that up there. That's all I have for you today. Back to you, Lauren.

Thank you very much for both being here. Let's give them another round of applause.

Thank you.

Last but not least, before we bring Katon back up for one very exciting announcement that you don't want to leave for. We have all these free adoption and learning academies, so. So up here you can see just our schedule for December and January. If we're coming to a city near you, contact your ie and you can get into one of these academies. It's hands on free training with experts where you can go from beginner to proficient in just two days. And with that, back to you, Kaitan.

Good job. How cool was that? Everybody had fun seeing all these things. That was a lot to cover and I'm sure you have a lot of questions. So we are going to do something special is tomorrow we are doing a global webinar for our community. It's not a product pitch, it's not a presentation, it's definitely not a sales call. But come ask us any questions. It's a typical AMA session. Any question, anytime, anywhere. I'll be there. The entire product leadership will be there and we'll take your questions. Like if you're like, oh, did that make sense? You know, if it didn't, come challenge us. We want to hear from you. And then I will encourage all of you to just go to the free trial. It's available today and you can go do it. And after that, after you've made note of both these things out here, how about we go and have some ice cream? Thank you so very much, guys. We are incredibly proud of you and I'm incredibly grateful to my team. What a great job everybody did. And let's give one more round applause for our customers who did their own demos. They were senior executives. That's cool. Have a fantastic dream for us everybody and I'll see you on the road. Thank you very much.