Origin of EverString | CEO/Founder of EverString, Vincent Yang

I was originally born and raised in China, and then I was studying mathematics over there. After I graduated from undergrad, I started my first company. It was a data mining company back in China helping carriers and China Mobile and China Unicom to analyze their internal data and figuring out who's their loyal customer, et cetera. But that was a long time ago. It was like 12 years ago. It was too early, and nobody talked about big data, et cetera. So, So we did it for about 2 and a half years. The issue for that is for a B2B company, if— so one interesting— I would love to know, like, among all of you guys, who's interested in building B2B versus B2C? Okay. All right. Nice. It's good to see, 'cause a lot of people want to build the next Facebook. And sometimes it's a little bit harder than building B2B software. So, but the problem about building B2B software is it's very easy to become a consulting company, especially when you have big clients. Because big clients can pay you big dollars, but they also ask a lot. And at the end of the day, you become a consulting company to them. So that's kind of— we didn't do a good job early on, so our first company failed in 2007. And then after 2007, so we were not able to raise any money. And then it's basically my co-founder and myself. So all of us are math majors, we're classmates, and we don't know how to run a company. There's no single VC that is interested in us. And then there's— and then we're starting to figure out like, what's the best way to raise money? And then we said, why don't we just get a job in VC so that we know what the criteria are looking at? So then I actually very fortunately bumped into Andy, who you know a lot, and I became an intern in DFJ Dragon back then. By analyzing all of their portfolio and helping Andy to learn, how to run a company. And then after a while, I realized, okay, I know a little bit on how VC do deals, but I understand from my back then that the experience is it's not easy to run a company. So then I asked Andy, and how do I run a business and how do I make the quick money so that I can bootstrap a business without a VC? And then people tell me that the easiest way to make money, the fastest way back then in 2007, is to do investment banking. Because back then it's very easy to make money. And then I was very fortunate to enter into JP Morgan, and then I worked there for 2 and a half years. And over in banking, obviously, you know, my main job is to analyze companies. And then after 2 and a half years there, I developed a model to analyze all the public companies to identify which public companies shall we invest and prioritize. And then after that time, I got a call from A Silicon Valley private equity firm called Summit Partners. It was down in Menlo Park. So they manage about $16 billion. So they called me and asked me to move over to Palo Alto, and then I did. It's very exciting to be closer to Silicon Valley. So in Summit, it was a very unique model. So we have 40 of us, and we did a lot of cold calls. So I did 3,000 cold calls a year, and calling all the CEOs. And all my job is to call the CEO and to figure out how big they are, what's their revenue, what's their EBITDA, and to decide on whether we should invest in them. And because I do so many cold calls, and compared to my peers, obviously back then I was not born in the US, I was a native English speaker. And all of my peers, they're football players, they're basketball players, former basketball players in college. They're much stronger than I am, and then they look and talk stronger than I am. So compared to them, they can make 10,000 calls a year where I can only make 3,000. And then I was thinking about, hey, how do I compete with them? And so one of the things I figured out is, hey, maybe I'm a little bit better in mathematics. Is there a way I can develop a machine to analyze every single company in the US to tell me which company should I call? There are 24 million private companies in the US. That's a lot of companies. You cannot analyze them one by one. So how do we analyze companies to figure out which company shall I call? All the private companies, they don't have any financials because they're not public, right? But they have news. They have their website. So then now I was thinking about to use a crawler to crawl every single company, analyze their news. So for example, I found there's a company that in their local news they mentioned they're hiring head of global sales. So why would you hire a head of global sales? That means you're expanding globally. So that little tiny clue is actually very, very important if you could capture it. And also, I found a company where they announced that they got named in Gartner's Magic Quadrant. So if you're mentioned by Gartner's Magic Quadrant, that means you're doing great. And also, there's another company that sponsored a big conference. Why would you sponsor a conference if you don't have the money? Of course you have the money, you're expanding, right? So by those little tiny signals that I realized, hey, Why don't we utilize every single signal on the web and have the crawler analyze every single company? So that's kind of the original idea of Everstring, is to crawl the whole world, analyze the company, and replace myself as an analyst, because this whole job is to replace my former manual work. So I did the first prototype and applied it to Summit, applied it to my territory. It works very well. So every quarter, as an investment manager in Summit, we are Required to present about 3 term sheets out. So you have to source 3 deals. And then using that methodology, in one quarter I was able to get 27, about 9 times higher than the average, and with 3 times lower the call volume as my peers, because I wanna make sure every single call I did was high quality. So with that, I'm like, wow, this is very amazing that I should quit my job and start a company. So I quickly called my old co-founder. Hi. who did my first company with me. And then it's spot on. And he did investment banking afterward as well. He made quite some money after 5 years. So we, this time, I think after 5 years, we are more mature and we have a little bit of money so that we don't need to rely on VCs initially. And also back then there was no incubators like this. So there's no YCs, etc. So then we start Everstream. So the original idea of Everstream is actually not what we are doing. So we pivoted. The original idea was to build a software that helped Summit and private equity firms and venture capital firms to find who should they invest in. We did this for about 1.5 years, and then I was also running the business, and then at the same time went to Stanford Business School. So over there, I was able to meet a lot of Stanford PhDs and hired a bunch of early co-founders over there as well. So we have 2 co-founders that with a PhD in neuroscience in Stanford. So they helped build a lot of the machine learning algorithms. So after selling for a year and a half, what we realized is in the whole world, there are only 10 private equity that can afford to pay us. So we realized the market is very small. And I wish I thought about it earlier, but I didn't. And then what's next? So there's a period of time, it's very It's very, we're very distressed. And we almost, the company almost went bankrupt. Like all the other startups that company pivoted in the early days as well. So then, well, I think we only have like half a year of cash on the balance sheet. We, I got a call from one of the head of dimension from McAfee, and from alumni. And he called us and said, hey, I wanna see you, and I have a project for you for our sales and marketing. And to us, we have no experience in sales and marketing. None of us know anything about marketing. And then, but when he bring us to the room, what he was saying is, hey, listen, we have a million leads in our database. In the past, there's only certain percentage of those leads that convert to the customers. 99% of those guys don't even become our customers. I really want you to help us predict who are those leads that will become our customers. And then for us, this is marketing, that all we're doing is analyzing company. And to him, it's like, this is the same thing. You're analyzing company because it's selling business to business. It's the same thing as analyzing companies to figure out which company shall we call. But this time, it's not from an investor, but it's from a marketer or from sales. We did the project. It turns out to be very well. The model that we run, the model we built, although it take about 47 days, to build the first model. It actually turned out our top leads, what we predicted is very, very— the leads that we predict is going to convert, it actually converts about 3 times higher than the average benchmark. So it actually works. And then I remember that we were very excited, and we think this is a very interesting market. So we sunset our typical traditional business of financial services offers and move all the way to sales and marketing. And it was about 10 months ago, We have only 2 weeks cash on the balance sheet. We have 10 people. We only have 1 customer. And then at this point, but the model is proven. So that's why finding the product-market fit in early stage is very important. And then this time there's— we're starting to go out to look for Series A venture capital. So we raised about $12 million Series A VCs back then, led by Lightspeed. I'm sure you might have heard of them. It's in Sand Hill Street as well. And followed by Sequoia and IDG Ventures and some of the early members in Facebook, etc. So that round really helps us. And then the last 10 months, we grew from 10 people to close to 80 people right now. And also our revenue increased by a lot. We grew from 0 customers to now close to 40 to 60 customers right now. And some of the big names you might have heard of, like We have 5 Fortune 500 companies using Everstream right now. So right now we are doing fairly well, and then we're starting to see another 5 to 10x increase on our revenues in the next 12 months. We have just moved to a new office, which is 5 times bigger. So now we are really hands down on first application in sales and marketing to do better on predictions. Thank you.