 From San Francisco, it's The Cube, here's your host, John Furrier. Hey, hello, I'm John Furrier with SiliconANGLE, The Cube. We are on the ground in Silicon Valley at Mintagos Corporate Headquarters, and here with the co-founder, Hal Segilov, who is the chief brains behind the operation. Chief scientist, Chief Everett, right-hand man to Jacob and John Vera. Welcome to the conversation. Thank you. So, we've had a couple of chats before, been in the office a few times, really impressed with Mintagos. Jacob kind of cleared up kind of the product, the positioning, but there's a lot of science behind it. And what we're seeing in the market right now that we're documenting on The Cube is there are industries being disrupted every day, but now you're seeing this sequence where the cloud, mobile is really disrupting markets that are waiting to be driven. Advertising has been won, and now sales. This is where the money is. Right? The money, you know, go where the people, people rob banks because there's money in there. That's what Steve Herrick told us on The Cube. Great line. Sales is where the money is. So, people are really looking at using big data and technology to optimize that. So, a lot of people are new to this. So, what's your take? How would you explain to those newbies out there who are, you know, sales managers or executives who just want to get more sales, get the funnel filled? What's the science behind this new generation? Yeah. So, you're absolutely right. So, the world in many aspects is changing for the much more data-driven approach. And I can actually quote the CMO of one of our customers, Lisa from Neustar, who termed this as the move from madmen to mathmen. Mathmen, they solve many problems that you need to require domain experts to do. So, today, using the exact same statistical machinery methods, you can predict whether a person is going to have an Epidepsy attack. You can find whether a particle interaction actually contains the Higgs boson. You can predict who's going to click on an online coupon. And you can also predict who would be the best buyer out there in your funnel in a while. And this transformation has basically completely disrupted any industry it's been through. So, why now though? I mean, it sounds like, it sounds kind of like sci-fi to me, which I, you know, I mean, I get it, but like folks out there might sound sci-fi. But why now? What's different now? Is it the cloud? Is it the unlimited compute available? Is it the access to data? What's different now than, say, five to seven years ago? So, first of all, all of the above. This has happened in marketing B2C products that are happening in B2B. So, look at Amazon. Just a couple of days ago, I called and asked everyone in the company who recognized this website with Amazon in 1993. Totally different, of course. And what's happening now in B2B is what's happened to B2C before, which is an abundance of data flowing in. Now, in B2B marketing, the data is a bit different. Unlike B2C, where much of the data is internal to the company. Like, you know, what kind of browsing you did and what processes you make in the past. In B2B, it's all about finding all the signals outside of your premises. So, more than 70% of the research people do is not done on your website. And the signals they do to show that and to show they have a fit or a need are outside your realm. So, you need to bring in, pull in all that data. And now this data is only available to marketers. And they also have the compute power to analyze it and to make meaningful deduction from it. So, is there a distinction between B2B and B2C? You brought that up. I mean, I can see some obvious ones, but is there some other differences? Yeah. So, in B2C, you don't have the company entities, right? So, each one of us is a separate consumer and we buy a company separately. Unlike B2B, where the company membership is something huge important to the person. And you can combine signals from different entities or people in that company. We buy it when we're in B2B contact. We buy both of the company and the person. You must combine these two together. So, it's like a mesh network then. And the data kind of can flow back and forth. Yeah. You do kind of transfer data from the company entity to the person and vice versa. So, I got to ask you the question about the other competition. I don't know the name name, but from my standpoint, I cover this area. There's a lot of snake oil out there. People say, oh, I'm doing social sales. People try to bring it in-house. It's almost a head fake if you will. But what's different about Mint2Go? And specifically for technology that makes it a relevant product right now. Because a lot of the buyers are either smart savvy CMOs and they have to operationalize this new concept. So, they're a little nervous. So, what can you say why Mint2Go is so powerful? So, I think for two reasons. Number one is Mint2Go was built from the ground up from marketers. We never sold to sales or to operations or to IT or to any other kind of buyer. We only concentrate and focus on marketing and our products are aimed at that. So, our products allow marketers to do additional things that they need to get done, which what customers feedback tells us is that we're the only ones that do. Number two is Mint2Go was built from the ground up from the data. So, like I told you before, data is key. And data is really the shift that's happened in the world out there that caused this technology to emerge. We have, I believe, the best data in the market. And we were the first one that started collecting the marketing relevant data. So, talk about the data strategy. A lot of people don't wake up in the morning in this 2014. They probably didn't have data science in their mind in the marketing statement. Certainly other verticals, you know, are moving fast in the data finance along with gas and much other ones. But now marketing, we saw Oracle, we saw Salesforce have their big events. And it seems to be hot right now for a data-driven marketing. What does that mean in your mind? It means explain to us what that means. I'll tell you what it looks like from the position of the marketer that needs to buy from Mint2Go. What we do is we go into an account and we discover this poor guy sitting in the corner. He has an engineering degree, but he's in marketing. And the poor guy has to work with Xim. And, you know, he barely has any data. He can even get his hands on. And what he does get his hands on is mostly dirty. So, that's what he's getting his hands dirty with bad data. So he's starved. He needs some information. He needs data. He's data starved at that point. We have a customer a couple of days ago. They came from a consulting company and said, you know, I came to this big company to work as senior director of the management. And the first thing I asked is, quick, where's the data? And people looked at each other and said, what data? What do I need? That's the kind of... So you guys come in and what do you do next? So the first thing we do is we discover the data needs of the company. So what makes a prospect buy? Why do people really buy it? People don't buy from us because they're in California and in the IT industry. People buy from us because they have a really need in marketing. They're spending a lot of money on marketing. They're using advanced tools and they're not getting the results. I can manage those. People that have engineering degrees in marketing are more likely to buy from people that don't. So you guys get a lot of technical customers that like you guys when you guys have a huge solution? Not only. Not only. Customers that sell tax-related solutions and all kinds of things. And this is really applicable to a very wide audience. So I've got to ask you this. What's the secret sauce? Tell us the secret. What's the secret algorithm? Is there secret algorithms? I saw on the board over there written in secret Hebrew. I mean, what's going on here? Come on, tell us. What's the secret sauce? So there's not one piece of it that's the secret sauce. It's a combination of three things. Number one is the integrations. So being able to pull data in from all kinds of different sources in our customers' systems. Number two is the database that Mintico is constructing and that contains very rich and very granular information about each and every company. But telling you someone is using a database isn't going to do you much good. But if I can tell you they're using MySQL, Oracle, and something, that's something you can actually go to work with. So that's number two. And number three is the capability to match all that together in smart machine learning statistical data analysis on that data set. So what's the theory behind this? I mean, we always talk to John Bear, the president, and Jacob about network theory, social media, connected devices, humans are internet of things. I guess humans are connected, too. The data is out there. It's all flowing. Is there like a thesis that you have technically? Is it network theory? Is it surveillance? Is it a lot of that kind of, you know, Jacob mentioned, you know, focusing on the good aspects, the good suspects, if you will. So what's the philosophy? Is there a thesis you can draw up from that can relate to that's science behind it? Well, I wouldn't say there's one sort of a bullet that tends to work each and every time. You know, you basically need a wide variety of data sources and data types in your system. And different things work for different customers. It's not the same solution each and every time. Some people find that a specific behavioral thing on their own is important. In the other case, you may find some hiring that the company is right now conducting is very important. And third one, it's a system they're using. So you guys do a lot of automation. If you just plug data in and just kind of runs, isn't that simple? It's definitely that simple. That's our goal. Actually, we encourage our customers to do that multiple times. And our thesis is you don't just need one specific model in order to build your marketing, you need multiple models. So I've got to ask you the question. Who are you disrupting? Every good technology is disrupting something. I know you guys have an efficient model. Give it to the noise, which is our model. Get the signal from the noise, which we do at the queue, which we're doing here. But when you get down to when you were actually going into the data and algorithmically cleaning it up and getting it more focused, that's going to, someone's going to lose on there. Someone's going to win, which you guys, who lose it? Who do you guys disrupt? You had the points in the queue. Well, I wouldn't say there is one point where we're disrupting right now. The usual suspect is the marketing information, which we're taking to the next level. But we're actually coexisting very well with those systems. I think we're allowing our marketers to do things they weren't able to do before. We're allowing them to gain much better insights into their focus. That's a great political answer. You really didn't say anyone. I could be working with you. No, but I mean, we were talking, the old way and new way, we're going to a new way. So if you argue that we're going to the new way, I would say that doesn't the marketing automation definition change? So if your marketing automation solution is doing something today, it's probably automating the wrong things. Or it needs to automate new things. How do you look at that technically? Because there's new ways of doing things and you're automating them. So do customers get that? Or is that just something you guys just kind of roll into the product? It's early, right? So earlier that was good. Mass markets, I wouldn't say that's where they're at. They're kind of, let me first start with kindergarten and then talk to me about high school. You take them through the progression, right? Yeah. There's an analogy in evolution. In evolution, every step of the way must be self-sufficient. Otherwise, you wouldn't be able to reach the end result. So only the really techy, savvy, advanced marketers do the lead in one step, but the rest is going to take the evolution part. So I've got to ask you the same question as Jacob. So why did you guys start in Mexico? What was the passion? You guys are co-found, obviously, yin and yang on the start up here. You guys are partners as partners. Which is the idea from me? Sipping coffee in the cafe? And you say, hey, let's go disrupt sales and marketing? Or what itch were you guys scratching the game to passion to start in Mexico? So both Jacob and me and most of the courting of Mexico, we have many years of experience in big data analytics and finding unique patterns and huge sets of data. And what we did is we thought about where can this kind of solution apply to a great problem that no one can solve. And that's when we started meeting this poor guy with the X7 in the corner of the marketing department. And that was pretty unique because most other departments had their emerging big data solutions, but marketing did not. And frankly, marketing's problem is also much tougher than the problems you encounter in other areas. They deal with millions of records doing very little about them and with very few tools to handle them. That's why we saw the match. So you have it fun? I have it a lot of fun. It's a good problem to solve. I mean, you've got a lot of sexy marketing angles there to solve, but it's also a technical science thing too as well. They need the solution. Marketing seems to be a need. The feedback we're getting is amazing. Hearing customers talk and telling us about how the problems we're tackling are important to them, that's great. I'll create a chat with you here. It's on the ground with Cube. Appreciate it. You guys are going to be a big company. Have a good feeling about you guys. We're really happy with what you're doing. I think you're bringing the science. The art is already out there in sales. Certainly, finding the good suspects, as they say, is now a science. You guys do some good work. I'm John Furrier with SiliconANGLE with Cube. We're on the ground at Silicon Valley here at Mintigo headquarters. Thanks for watching.