Anand Sanwal of CB Insights joins Nick to cover The Bloomberg for Startups, Part 1. We will address questions including:
- Can you walk us through your background and how you became involved in venture and the startup ecosystem?
- What were the key issues you experienced in analyzing data about private companies when you were doing M&A at Amex?
- Today we are talking the data-centric approach toward investing in startups. Can you start off by giving us an overview of CBInsights and how it fits into the startup ecosystem?
- Who are your primary customers and why do they use the platform?
- How and from where do you obtain your source data?
- Do you think that CBInsights is used moreso for deal-flow or evaluation. In other words is it a sourcing tool or an analysis tool?
Nick: Today # Anand Sanwal joins us from New York City. He’s founder and CEO of The Bloomberg for Private Companies, # CB Insights. And in addition to their fantastic data platform, Anand also sends out one of the best newsletters in the industry with current events, commentary, research reports and he finishes each one up with a very paternal ‘I love you’. # Anand, welcome to the program.
Anand: Thanks # Nick, thanks for having me.
Nick: Yeah. Can you start us off by walking through your background and how you got involved in startups?
Anand: Yeah, sure, thanks. So kind of a long winded route to getting there. I worked at # kozmo.com , sort of an infamous dot com blowup in 1999 and 2000. Kind of was a refugee of the dot com crisis, and actually went to # American Express from there. Wanted to get some stability on my resume. I’d had a few jobs I guess before that in a pretty short time frame. Went to # American Express, worked in their venture and M&A group, stayed there much longer than I expected. They treated me well. Left it, you know, running their 50 million dollar innovation fund, after running that and setting it up. And then kind of saw the data products that we had to use when I was running that group , and thought to myself, sort of, the information about private companies which are these major drivers of sort of economic growth and innovation is pretty terrible. You know, the data is bad, the UI and UX of these providers is terrible, and the customer support, given what we pay, is also miserable. I always wanted to do my own thing and thought, you know, hey here’s a paying point, and to have the desire to be an entrepreneur, let’s give it a shot. And so left # AmEx. Then had a couple guys from my old team join me, and we started building # CB Insights to be, like you kind of characterized it, the Bloomberg for private companies.
Nick: So at # AmEx, were you charged with setting the investment strategy and then picking the sectors that you guys were gonna make investments in?
Anand: Yes, I mean, when I started there I was a low man on the totem pole, so I wasn’t setting much of any strategy. I was kind of, I was executing. And then, you know, over time kind of did enough good work that the company wanted to, you know, at big companies there’s this challenge of innovating in the things that could potentially kill you, right. The sort of corporate antibodies to that type of stuff are quite strong. I mean, # AmEx is actually, you know, pretty progressive as far as this goes
Anand: in terms of big companies. So they set up this internal kind of innovation fund to hopefully get those ideas out that could be, sort of, you know, I hate to use the term, disruptive to the organization, and then fund them. So that’s what I was ultimately running, was kind of, you know, identifying these opportunities and then funding them. And so yeah that was kind of in my last role. And so there it was really kind of getting ideas from around the organization. You know, you have a company that is that big with a lot of smart people who know their businesses very well. They were coming to us with ideas or businesses that they’d seen that looked interesting. And so, you know, a little bit of it was having kind of the relationships to get those ideas . And then, second, was being able to apply a filter to those and making sure that we were pursuing things that kind of truly disruptive to the business. And I hate to use that term because it’s so overplayed. You know, it’s hard for big companies to do. And so that’s what the innovation fund was set up to do, is to try to fund those things, those initiatives that often sort of, they’re sort of the antibodies to these types of things in the organization kill, and we’re trying to fund those. So surfacing them was a big part of our challenge, right. One of them was coming up with our own ideas , second was surfacing them from people in the business units that know their businesses well and their industries well. And then ultimately, kind of, culling all those ideas and figuring out which were the best ones in terms of potential opportunity as well as which ones weren’t just incremental sort of enhancements that didn’t really fit the credo of what we were trying to do with this fund.
Nick: Gotcha. I come from corporate M&A, and I’m curious, we had our own strategy about how hands on to be with the companies that we were making investments in. Would you guys just passively invest out of the fund in various private companies that you’re following? Or would you, sort of partner with them, give them some channel access or do
Anand: Yeah, it was, yeah it’s a good question. It was definitely more kind of hands on, right. So I think we were, we weren’t a financial investor, right, or a purely financial investor, right
Anand: we were looking for things that were definitely at a tie, strategically to the core business. And then the hope was that we could offer them distribution or access or become a client . So I think that was always, that was always kind of the opportunity and the hope. Obviously, you know, when you work at a really large organization and people are, it’s sometimes hard to get that level of attention. But, you know, that was always the intention, was that we would able to help these companies.
Nick: That’s nice. Great, so the background helps a lot. But transitioning more into # CB Insights and what’s going on these days. Can you start us of by giving us an overview of # CB Insights and how it fits in to the startup ecosystem?
Anand: Yeah, sure thanks. So, you know, I think this has evolved quite a bit. So I’ll give you a kind of when we started, we should have this and it was sort of an errant idea. This idea that hey we’ll make this data available and we’ll make it really inexpensive and all these people who never had access to amazing data would pay for it. And kind of very quickly realized that that’s not just not an interesting market, right. You know, startups are not a great customer base kind of high turn, not a lot of money, lots of support requirements. So, and what we saw was corporations and VCs were kind of a big target market who were a lot better as a customer. And you know, we had a big consulting company covered us pretty early, and I think our price was like twenty five hundred dollars a year, it was like throw away cheap. And the consulting company came to us and said hey listen we need you guys to charge a lot more because we want to be first in line when we need help. And so that was kind of like eye opening for us. But in terms of what we do, we collect data on private companies. So any type of financing or exit involving a private company, we are vacuuming that in, using machine learning as well as now increasingly data submitted to us directly from the investment community. And then we’ve done as we’ve built kind of an intelligence layer on top of that data. So, you know, it’s not just a spreadsheet, right. It’s really kind of how do you ask intelligent questions of that information and that data. So how do you figure out which companies you should invest in, who you should be syndicating deals with, who might potential acquires of a company be, you know, which companies are struggling that you might want to push talent from. And you know, which industries are growing the quickest, and from a corporate strategy perspective, where should I be focused. So it’s kind of taking all that data and then offering the sort of insight and analytical layer on top of it, which is what we’ve become. And so now our customers are exclusively institutional. And VCs is obviously sort of the obvious one. I’d say our biggest area of growth is on the corporate side. So you know, corporate strategy, corporate invasion, CILs offices, obviously corporate M&A, corporate VC. But we , you know, we have corporations who have now 9 or 10 subscriptions, separate subscriptions to CB Insights. So that’s really where the opportunity for us is. In terms of how we, you know, when with the startup ecosystem we put out a lot of great content. And you’ve mentioned the newsletters. So I think we sort of don’t make money off of startups, but we hope that by the research that we put out, we’re hopefully informing people and helping them figure out which investors to talk to or whats happening, you know, in the climate so, you know, you can be informed. And that’s I think the way we kind of work with the startups, but it’s not, they’re not a customer of ours.
Nick: So how long have you been around? Because I know when I was at # Danaher this sort of information would have been super helpful
Nick: but we weren’t even aware of you
Anand: Yeah, I mean, so we launched in Feb 2010. And we , to be very frank, like are a very product engineering driven company. So for the first two and a half, three years, we kind of just had people say hey I signed up for your free trial, how do we give you money. It was, you know, we were very product focussed, and honestly just didn’t know what we were doing on the sales side. And then, you know, we got enough of those inbound, and then we said hey maybe it’d be good if we actually like reached out to people. And so, kind of, two and a half , three years ago we started building out a sales organization to actually do that. But yeah we launched Feb 2010, so I guess we’re coming up on our, on our 6th year.
Nick: Gotcha. And we talked a little bit about your customer sets and also the data on the platform. Can we start with your primary customers, you know, why do they use the platform and what value are they getting from it?
Anand: Yeah, so I think the reality is that big companies and institutions make really big decisions off of using like sort of what we kind of informally call the 3 G’s right, so they use Gut instinct, Google searches or Guys with MBAs, right. And so like, you know, in big decisions about who should we acquire, what industry should we go after, what are our competitors up to, right. Like these are pretty big things that you want to understand. And so we’re trying to give you insight into that with data. So our primary customer now is really the corporate kind of community, from a revenue perspective. I’d say from a number of customers perspective, it’s still probably the investment community. But in terms of sort of contribution of revenue, it’s really the corporate side of things. And so they’re using us to help get forward looking intelligence into sort of where the world is going, you know, where their competitors might be going, what the next hot industry is, what the next hot company might be. So that they can make better product M&A, VC, etc decisions. And then, you know, we have, it’s sort of this horizontal platform. We have lots of other use cases. I mean, we have economic development and organizations and lawyers and accountants using us. But I’d say if I were to focus on the core, it’s really become the corporate base.
Nick: Got it. And the business model is SaaS itself, license fee per head, is that, is that right for most?
Anand: Right. It’s licensed per team. So , so you know, if you’re the corporate M&A team, up to 10 users. So it’s, you know, if you’re corporate M&A with 9 people you can have a subscription, but if you’re corporate strategy and a business unit and you have 5 users, you’re going to buy separate subscriptions. So, but we don’t do seed based. We think seed based is, we’ve done it in the past and we feel like what ends up happening is people just end up sharing seats, and it’s sort of, I think, hurts adoption, and so we’ve sort of gotten away from that seed based model in our case.
Nick: And is it just one product platform? Or is there a premium version as well as a standard version with different pricing structures around both?
Anand: Yeah, there’s, it’s one platform but it’s very modular. And so if you want , kind of just the plain vanilla data and the ability to filter it, you’re going to pay at the low end of the spectrum. And then if you want analytics and predictive intel, you’re going to move up. And then, and then if you want analytics, predictive intel and analysts in our team to help you answer questions and do private webinars with your executives and for us to do events with you and get you in front of the VCs and startups, like then you’re going to pay a much higher, almost like membership fee. So it does, if there’s quite a big range there depending on, you know, needs and budget.
Nick: Gotcha. Got some flexibility.
Nick: So the other thing I wanted to touch on was, was the data side. So, how and from where do you obtain the source data?
Anand: Sure, yeah. So I think our fundamental kind of insight in the beginning was that in 1995 you had to call people to get the data, right. You’d send them an excel spreadsheet and you’d harass them to send you the deals they did .And then, you know, now in 2016 or when we started building this in, you know, 2010-11, there was a ton of information on the web, right. There’s all this sort of data exhaust out there. And so the lion’s share of our data, it’s probably 70%, is machine learning driven. So we’ve built software that will crawl, classify and extract structured data from instructed documents. So this is everything from visiting # Kleiner Perkins portfolio page every day to hitting every local, national, regional newspaper, business journal, tech blog, regional business publication, Twitter, you name it. And so we’ve built really good software that will crawl a few million sources daily and extract out information. And so the benefit of that is from a model perspective it’s much more cost efficient. And then we find a ton more deals. And then the other piece, the other 30% is direct submission. And this has actually been increasing quite a bit. It wasn’t like this in the beginning. I think as our name and as our newsletter have gotten more prominent and our research has become more prominent, people want to get their data to us.
Anand: Because they want to be featured in that stuff. And then the other thing is we now have a few hundred M&A teams that use this, so if you’re an investor and you want your companies updated on # CB Insights because the buyers or potential customers, the CIOs or the potential strategic partners at any of these corporations are using us to do scouting and deal sourcing. So, so I think that’s ended up being, and it’s going to increasingly I think become a big part of how we gather data.
Nick: So we closed a syndicate deal on # AngelList for an augmented reality company. We raised 370K. Is that something that I should submit to you guys and say hey include this or would you pick that up?
Anand: I think we probably would pick it up. But we do have, you know, investors or companies submitting data to us. So, it’s sort of like we’ll use a lot of confirmatory signals to make sure the data is good. And so if somebody submits data to us and we obviously had it already, that’s a great sort of confirmation. So yeah, I hope we, I hope the technology has probably already picked it up. And if, if it has, you know, if you submit it it’s a great way to ensure that everything we’ve picked up is correct.
Nick: I’m going to put you to the test. I’m going to check it out.
Anand; Yeah, yeah, absolutely. I mean, I was actually going to log on right now. What’s the name of the company?
Nick: It’s # Scope AR. But I think docs, I think docs are not totally finalized, I think they need a little more
Anand: Got it , okay
Nick: on pro rata, so in the next couple days.
Ananda: Okay, cool
Nick: So the technique, is it a variety of API’s and scraping?
Anand: Yeah. I mean, it’s, it wouldn’t rely a lot on any API’s. That’s because we’ve found that being reliant on any sort of third party, like the rules change. You know, we don’t use any of the, any publicly available sort of data sources that are out there. We just find that they’re super messy and the data quality is not good. So yeah it’s mostly us, the engineering team has built crawlers that will go out and parse through. We have a wide list of sources. so it will go and parse through the sources on a daily basis, look for changes on Kleiner’s portfolio page, new logos let’s say everyday and , and you know, think of doing that across all hundreds or thousands now of private equity and venture firms
Anand: So that’s the primary method on the technology side. We kind of call it the cruncher. But it’s, yeah it’s ingesting lots of data. And then, you know, we’ve started adding to that as other types of life events of companies. So partnership data, you know, we’ve built a classifier for that. Customer data, so if there’s a press release announcing a customer signing, we’re starting to extract that information too. So if you think of an organization as an organism of sorts, financing and exit are just two life events. And so now we’re looking to add a bunch of other kind of life events, again using a lot of the same base technology.
Nick: So, # Anand, do you think that # CB Insights is used more-so for deal flow or valuation? In other words, do you think it’s more of sourcing tool or an analysis tool?
Anand: Yeah. I guess that the real answer is it depends, right. So if you’re a growth equity firm trying to understand kind of who’s coming down the pipeline, we are definitely a deal flow tool. If you are a corporate strategy team, it’s much more of an insights and analysis tool trying to use this data. You might not care necessarily about the individual companies, but you want to know collectively all these companies indicate that there is a lot of interest in, you know, block chain, or in this type of digital health technology. So I think a lot of the use case has evolved into being sort of more insight driven and so getting kind of a valuation of markets or competitors or things like that. But it does vary. You know, given sort of the eclecticness of our customer base, I’d say there’s no universal use case. It is , it varies kind of based on, based on who you are.
Nick: Got it. So as a a VC, I could use it for both- to source deals, to find,
Anand: Yeah, yeah I mean
Nick: to find new deals that are at Series A or Series B as well as
Nick: to help evaluate those by seeing maybe user growth scores and things of that nature?
Anand: Yeah, so yeah, you could use it to source and identify companies. And then, you know, our, and I know we’re, I think we’re going to get into it, like the NSF funding that we received was to help narrow a list of companies down, right. So I think being able to quickly evaluate thousands of companies and get it down to those that are maybe growing the fastest. And then I think the VCs also use us to understand markets, right. So can I look at the heat map of # Sequoia Capital and see, hey they’re doing a lot more deals in a particular sector. You know, those guys are smart money. Maybe, actually, you know, maybe that’s something I want to be looking at as well, right.
Anand: So I think like getting those sort of higher level thesis insights or, is also a big part of I think what a lot of a venture firms uses for. And then those market insights on the corporate side are definitely the core value that they see.
Nick: Yeah, and on that point, before we jump into the # NSF, you know, I often hear people talk about how they invest in market places or education or drones or healthcare or IoT or SaaS or machine learning, etc. But they mention these things all in the same breath, you know, some classify them as sectors, while others talk about horizontal themes. Clearly something like SaaS is more of a business model than it is a sector. At # CB Insights, how do you think about these categories and also structure the data, so that you’re not mixing apples and oranges?
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