>> Hi, this is George Gilbert. We have an extra-special guest today on our CUBEcast, Aman Naimat, Senior Vice President and CTO of Demandbase started with a five-person startup, Spiderbook. Almost like a reverse IPO, Demandbase bought Spiderbook, but it sounds like Spiderbook took over Demandbase. So Aman, welcome. >> Thank you, excited to be here. Always good to see you. >> So, um, Demandbase is a Next Gen CRM program. Let's talk about, just to set some context. >> Yes. >> For those who aren't intimately familiar with traditional CRM, what problems do they solve? And how did they start, and how did they evolve? >> Right, that's a really good question. So, for the audience, CRM really started as a contact manager, right? And it was replicating what a salesperson did in their own private notebook, writing contact phone numbers in an electronic version of it, right? So you had products that were really built for salespeople on an individual basis. But it slowly evolved, particularly with Siebel, into more of a different twist. It evolved into more of a management tool or reporting tool because Tom Siebel was himself a sales manager, ran a sales team at Oracle. And so, it actually turned from an individual-focused product to an organization management reporting product. And I've been building this stuff since I was 19. And so, it's interesting that, you know, the products today, we're going, actually pivoting back into products that help salespeople or help individual marketers and add value and not just focus on management reporting. >> That's an interesting perspective. So it's more now empowering as opposed to, sort of, reporting. >> Right, and I think some of it is cultural influence. You know, over the last decade, we have seen consumer apps actually take a much more, sort of predominant position rather than in the traditional, earlier in the 80s and 90s, the advanced applications were corporate applications, your large computers and companies. But over the last year, as consumer technology has taken off, and actually, I would argue has advanced more than even enterprise technology, so in essence, that's influencing the business. >> So, even ERP was a system of record, which is the state of the enterprise. And this is much more an organizational productivity tool. >> Right. >> So, tell us now, the mental leap, the conceptual leap that Demandbase made in terms of trying to solve a different problem. >> Right, so, you know, Demandbase started on the premise or around marketing automation and marketing application which was around identifying who you are. As we move towards more digital transaction and Web was becoming the predominant way of doing business, as people say that's 70 to 80 percent of all businesses start using online digital research, there was no way to know it, right? The majority of the Internet is this dark, unknown place. You don't know who's on your website, right? >> You're referring to the anonymity. >> Exactly. >> And not knowing who is interacting with you until very late. >> Exactly, and you can't do anything intelligent if you don't know somebody, right? So if you didn't know me, you couldn't really ask. What will you do? You'll ask me stupid questions around the weather. And really, as humans, I can only communicate if you know somebody. So the sort of innovation behind Demandbase was, and it still continues to be to actually bring around and identify who you're talking to, be it online on your website and now even off your website. And that allows you to have a much more sort of personalized conversation. Because ultimately in marketing and perhaps even in sales, it comes down to having a personal conversation. So that's really what, which if you could have a billion people who could talk to every person coming to your website in a personalized manner, that would be fantastic. But that's just not possible. >> So, how do you identify a person before they even get to a vendor's website so that you can start on a personalized level? >> Right, so Demandbase has been building this for a long time, but really, it's a hard problem. And it's harder now than ever before because of security and privacy, lots of hackers out there. People are actually trying to hide, or at least prevent this from leaking out. So, eight, nine years ago, we could buy registries or reverse DNS. But now with ISBs, and we are behind probably Comcast or Level 3. So how do you even know who this IP address is even registered to? So about eight years ago, we started mapping IP addresses, 'cause that's how you browse the Internet, to companies that they work at, right? But it turned out that was no longer effective. So we have built over the last eight years proprietary methods that know how companies relate to the IP addresses that they have. But we have gone to doing partnerships. So when you log into certain websites, we partner with them to identify you if you self-identify at Forbes.com, for example. So when you log in, we do a deal. And we have hundreds of partners and data providers. But now, the state of the art where we are is we are now looking at behavioral signals to identify who you are. >> In other words, not just touch points with partners where they collect an identity. >> Right. >> You have a signature of behavior. >> That's right. >> It's really interesting that humans are very unique. And based on what they're reading online and what they're reading about, you can actually identify a person and certainly identify enough things about them to know that this is an executive at Tesla who's interested in IOT manufacturing. >> Ah, so you don't need to resolve down to the name level. >> No. >> You need to know sort of the profile. >> Persona, exactly. >> The persona. >> The persona, and that's enough for marketing. So if I knew that this is a C-level supply chain executive from Tesla who lives in Palo Alto and has interests in these areas or problems, that's enough for Siemens to then have an intelligent conversation to this person, even if they're anonymous on their website or if they call on the phone or anything else. >> So, okay, tell us the next step. Once you have a persona, is it Demandbase that helps them put together a personalized? >> Profile. >> Profile, and lead it through the conversation? >> Yeah, so earlier, well, not earlier, but very recently, rebuilding this technology was just a very hard problem. To identify now hundreds of millions of people, I think around 700 are businesspeople globally which is majority of the business world. But we realize that in AI, making recommendations or giving you data in advanced analytics is just not good enough because you need a way to actually take action and have a personalized conversation because there are 100 thousand people on your website. Making recommendations, it's just overwhelming for humans to get that much data. So the better sort of idea now that we're working on is just take the action. So if somebody from Tesla visits your website, and they are an executive who will buy your product, take them to the right application. If they go back and leave your website, then display them the right message in a personalized ad. So it's all about taking actions. And then obviously, whenever possible, guiding humans towards a personalized conversation that will maximize your relationship. >> So, it sounds like sometimes it's anticipating and recommending a next best action. >> Yeah. >> And sometimes, it's your program taking the next best action. >> That's right, because it's just not possible to scale people to take actions. I mean, we have 30, 40 sales reps in Demandbase. We can't handle the volume. And it's difficult to create that personalized letter, right? So we make recommendations, but we've found that it's just too overwhelming. >> Ah, so in other words, when you're talking about recommendations, you're talking about recommendations for Demandbase for? >> Or our clients, employees, or salespeople, right? >> Okay. >> But whenever possible, we are looking to now build systems that in essence are in autopilot mode, and they take the action. They drive themselves. >> Give us some examples of the actions. >> That's right, so some actions could be if you know that a qualified person came to your website, notify the salesperson and open a chat window saying, "This is an executive. "This is similar to a person who will buy "a product from you. "They're looking for this thing. "Do you want to connect with a salesperson?" And obviously, only the people that will buy from you. Or, the action could be, send them an email automatically based on something they will be interested in, and in essence, have a conversation. Right? So it's all about conversation. An ad or an email or a person are just ways of having a conversation, different channels. >> So, it sounds like there was an intermediate marketing automation generation. >> Right. >> After traditional CRM which was reporting. >> Right, that's true. >> Where it was basically, it didn't work until you registered on the website. >> That's right. >> And then, they could email you. They could call you. The inside sales reps. >> That's right. >> You know, if you took a demo, >> That's right. >> you had to put an idea in there. >> And that's still, you know, so when Demandbase came around, that was the predominant between the CRM we were talking about. >> George: Right. >> There was a gap. There was a generation which started to be marketing. It was all about form fills. >> George: Yeah. >> And it was all about nurturing, but I think that's just spam. And today, their effectiveness is close to nothing. >> Because it's basically email or outbound calls. >> Yeah, it's email spam. Do you know we all have email boxes filled with this stuff? And why doesn't it work? Because, not only because it's becoming ineffective and that's one reason. Because they don't know me, right? And it boils down to if the email was really good and it related to what you're looking for or who you are, then it will be effective. But spam, or generic email is just not effective. So it's to some extent, we lost the intimacy. And with the new generation of what we call account-based marketing, we are trying to build intimacy at scale. >> Okay, so tell us more. Tell us first the philosophy behind account-based marketing and then the mechanics of how you do it. >> Sure, really, account-based marketing is nothing new. So if you walk into a corporation, they have these really sophisticated salespeople who understand their clients, and they focus on one-on-one, and it's very effective. So if you had Google as a client or Tesla as a client, and you are Siemens, you have two people working and keeping that relationship working 'cause you make millions of dollars. But that's not a scalable model. It's certainly not scalable for startups here to work with or to scale your organization, be more effective. So really, the idea behind account-based marketing is to scale that same efficacy, that same personalized conversation but at higher volume, right? And maximize, and the only way to really do that is using artificial intelligence. Because in essence, we are trying to replicate human behavior, human knowledge at scale. Right? And to be able to harvest and know what somebody who knows about pharma would know. >> So give me an example of, let's stay in pharma for a sec. >> Sure. >> And what are the decision points where based on what a customer does or responds to, you determine the next step or Demandbase determines what next step to take? >> Right. >> What are some of those options? Like a decision tree maybe? >> You can think of it, it's quite faddish in our industry now. It's reinforcement learning which is what Google used in the Go system. >> George: Yeah, AlphaGo. >> AlphaGo, right, and we were inspired by that. And in essence, what we are trying to do is predict not only what will keep you going but where you will win. So we give rewards at each point. And the ultimate goal is to convert you to a customer. So it looks at all your possible futures, and then it figures out in what possible futures you will be a customer. And then it works backwards to figure out where it should take you next. >> Wow, okay, so this is very different from >> They play six months ahead. So it's a planning system. >> Okay. >> Cause your sales cycles are six months ahead. >> So help us understand the difference between the traditional statistical machine learning that is a little more mainstream now. >> Sure. >> Then the deep learning, the neural nets, and then reinforcement learning. >> Right. >> Where are the sweet spots? What are the sweet spots for the problems they solve? >> Yeah, I mean, you know, there's a lot of fad and things out there. In my opinion, you can achieve a lot and solve real-world problems with simpler machine learning algorithms. In fact, for the data science team that I run, I always say, "Start with like the most simplest algorithm." Because if the data is there and you have the intuition, you can get to a 60% F-score or quality with the most naive implementation. >> George: 60% meaning? >> Like accuracy of the model. >> Confidence. >> Confidence. Sure, how good the model is, how precise it is. >> Okay. >> And sure, then you can make it better by using more advanced algorithms. The reinforcement learning, the interesting thing is that its ability to plan ahead. Most machine learning can only make a decision. They are classifiers of sorts, right? They say, is this good or bad? Or, is this blue? Or, is this a cat or not? They're mostly Boolean in nature or you can simulate that in multi-class classifiers. But reinforcement learning allows you to sort of plan ahead. And in CRM or as humans, we're always planning ahead. You know, a really good salesperson knows that for this stage opportunity or this person in pharma, I need to invite them to the dinner 'cause their friends are coming and they know that last year when they did that, then in the future, that person converted. Right, if they go to the next stage and they, so it plans ahead the possible futures and figures out what to do next. >> So, for those who are familiar with the term AB testing. >> Sure. >> And who are familiar with the notion that most machine learning models have to be trained on data where the answer exists, and they test it out, train it on one set of data >> Sure. >> Where they know the answers, then they hold some back and test it and see if it works. So, how does reinforcement learning change that? >> I mean, it's still testing on supervised models to know. It can be used to derive. You still need data to understand what the reward function would be. Right? And you still need to have historical data to understand what you should give it. And sure, have humans influence it as well, right? At some point, we always need data. Right? If you don't have the data, you're nowhere. And if you don't have, but it also turns out that most of the times, there is a way to either derive the data from some unsupervised method or have a proxy for the data that you really need. >> So pick a key feature in Demandbase and then where you can derive the data you need to make a decision, just as an example. >> Yeah, that's a really good question. We derive datas all the time, right? So, let me use something quite, quite interesting that I wish more companies and people used is the Internet data, right? The Internet today is the largest source of human knowledge, and it actually know more than you could imagine. And even simple queries, so we use the Bing API a lot. And to know, so one of the simple problems we ran into many years ago, and that's when we realized how we should be using Internet data which in academia has been used but not as used as it should be. So you know, you can buy APIs from Bing. And I wish Google would give their API, but they don't. So, that's our next best choice. We wanted to understand who people are. So there's their common names, right? So, George Gilbert is a common name or Alan Fletcher who's my co-founder. And, you know, is that a common name? And if you search that, just that name, you get that name in various contexts. Or co-occurring with other words, you can see that there are many Alan Fletchers, right? Or if you get, versus if you type in my name, Aman Naimat, you will always find the same kind of context. So you will know it's one person or it's a unique name. >> So, it sounds to me that reinforcement learning is online learning where you're using context. It's not perfectly labeled data. >> Right. I think there is no perfectly labeled data. So there's a misunderstanding of data scientists coming out of perfectly labeled data courses from Stanford, or whatever machine learning program. And we realized very quickly that the world doesn't have any perfect labeled data. We think we are going to crowdsource that data. And it turns out, we've tried it multiple times, and after a year, we realized that it's just a waste of time. You can't get, you know, 20 cents or 25 cents per item worker somewhere in wherever to hat and label data of any quality to you. So, it's much more effective to, and we were a startup, so we didn't have money like Google to pay. And even if you had the money, it generally never works out. We find it more effective to bootstrap or reuse unsupervised models to actually create data. >> Help us. Elaborate on that, the unsupervised and the bootstrapping where maybe it's sort of like a lawnmower where you give it that first. >> That's right. >> You know, tug. >> I mean, we've used it extensively. So let me give you an example. Let's say you wanted to create a list of cities, right? Or a list of the classic example actually was a paper written by Sergey Brin. I think he was trying to figure out the names of all authors in the world, and this is 1988. And basically if you search on Google, the term "has written the book," just the term "has written the book," these are called patterns, or hearse patterns, I think. Then you can imagine that it's also always preceded by a name of a person who's an author. So, "George Gilbert has written the book," and then the name of the book, right? Or "William Shakespeare has written the book X." And you seed it with William Shakespeare, and you get some books. Or you put Shakespeare and you get some authors, right? And then, you use it to learn other patterns that also co-occurred between William Shakespeare and the book. >> George: Ah. >> And then you learn more patterns and you use it to extract more authors. >> And in the case of Demandbase, that's how you go from learning, starting bootstrapping within, say, pharma terminology. >> Yes. >> And learning the rest of pharma terminology. >> And then, using generic terminology to enter an industry, and then learning terminology that we ourselves don't understand yet it means. For example, I always used this example where if we read a sentence like "Takeda has in-licensed "a molecule from Roche," it may mean nothing to us, but it means that they're partnered and bought a product, in pharma lingo. So we use it to learn new language. And it's a common technique. We use it extensively, both. So it goes down to, while we do use highly sophisticated algorithms for some problems, I think most problems can be solved with simple models and thinking through how to apply domain expertise and data intuition and having the data to do it. >> Okay, let's pause on that point and come back to it. >> Sure. >> Because that sounds like a rich vein to explore. So this is George Gilbert on the ground at Demandbase. We'll be right back in a few minutes.