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Building The Bloomberg For Private Equity | Neha Singh and Abhishek Goyal @ Tracxn
318 Plays2 years ago
The global company Bloomberg mines market data to help businesses make prudent decisions. In a similar vein, Tracxn helps private market investors in their decision-making by providing curated profiles of startups and companies. Tune in to hear the story of two analysts building one of the world’s largest data intelligence platforms!
Know about:-
- The private market landscape
- Scaling up insider understanding of a sector
- The revenue journey
- Go-to-market strategy
Recommended
Transcript
Introduction to Traction
00:00:00
Speaker
Hi, I'm Neha Singh. Hi, I'm Abhishek Goya. And we are co-founders of Traction. Traction is a private market intelligence platform. We work with investors and large corporates who want to track the private markets, the startup landscape and the ever-changing emerging technology space.
Impact of Traction on Investment Decisions
00:00:29
Speaker
To a man with a hammer, everything looks like a nail. What this means is that your world view depends on the kind of tools that you have.
00:00:37
Speaker
And my worldview was recently changed once I got access to a tool called Traction, which is spelled as T-R-A-C-X-N. Traction is basically like a Bloomberg for private markets. And let me break that down for you. Bloomberg is not just the name of a US presidential hopeful, but is a tool which has tons of data on publicly listed equities and mostly focused on the US.
00:00:59
Speaker
It is what every Wall Street analyst uses to make investment decisions and I would say that billions of dollars of investment decisions are made using Bloomberg. Traction does the same except it focuses on unlisted companies and it is used by VCs and angel investors and the likes.
00:01:18
Speaker
And this is how Traction affected my worldview. Every time I would see an ad for a D2C brand on Instagram, I would first look them up on Traction to see how much money had there is, what kind of top line were they doing, and what kind of valuation were they had to decide really that is this a big company or is it an
00:01:38
Speaker
relatively young newcomer. Every time I saw a job ad in my alumni mail group I would look up that company on Traction to see what has been the growth of people in the company and how have they been doing revenue wise. Essentially Traction has tons of data which can be used to make a whole host of decisions.
Founders' Background and Traction's Global Reach
00:01:57
Speaker
Abhishek and Neha, the founders of Traction, were working as analysts in some of India's leading venture capital funds when they felt the need for a platform that would make it easier to build up the basic profiles of companies that they were potentially looking to invest in.
00:02:15
Speaker
And over the next decade, they built up traction to a company which tracks millions of companies across the world today. And despite being based completely in India, gets most of its revenue from the globe. It's really a made in India for the world example, and they are headed for an IPO soon. Listen on to my conversation with Abhishek and Neha about the journey of building up this amazing business. You're listening to the Founder Thesis podcast, and I'm your host, Akshay Dutt.
00:02:54
Speaker
And post that I did my undergrad in computer science. And initially joined BCG, the Boston consulting group and the El Sequoia capacity for me. So the idea of traction got born when I was investing. And I thought that if you look at public market investors, they have
00:03:14
Speaker
the luxury of having platforms like Bloomberg or a facet or a capital IQ, while if you are a private market investor, when you're looking at different sectors, you literally have to build a whole stack yourself. So traction was born by a need that I really wish that a platform like this existed when we were investors.
Investment Focus and Missed Opportunities
00:03:35
Speaker
I think what exactly were you doing? Was there a segment in which you were investing or what was the role there?
00:03:42
Speaker
So I was investing primarily in tech. And this was a decade back in 2010, when everyone was not looking at tech. Like today, if you look at it, most investors would be looking at tech as such. But at that time, it was a smaller team. And then you would look at like technical health care, financial services, et cetera. So I was looking at primarily tech startups in India. And can you give me some names? What kind of companies did you invest in or even companies that you regret passing on?
00:04:09
Speaker
Yeah, so there were a bunch of interesting companies. For instance, Precharge was one company that I had worked on. Practo was when we were, I was part of the 2018 when we had written the first seed check. Obviously, it's great to see the company scale so much. Some of the companies that I, you know, that I've seen very closely, which should not portray to have been invested in, for instance, browser stacks was a very interesting company, which was actually started by Seniors of Mike.
00:04:33
Speaker
from my IT Bombay. So I knew them beforehand and then I had heard about that they're doing that interesting work. But unfortunately, yeah, obviously that companies are tremendously well. Okay. Got it. Got it. Okay. I wish like, what's your retraction journey?
Abhishek's Career Journey
00:04:45
Speaker
Like, and did you meet me? So I joined, my first job was Yahoo. And then I also worked with Amazon. So these are all tech-based software. You were coding? Yes, absolutely. I was not the best programmer I knew, but that was where I spent a few years. And then
00:05:01
Speaker
I was among the youngest CTO in my bad. So I became a CTO at 26. I managed to become a CTO. This was actually a division of 3i InfoTech where we were like running a VU and I was head of technology there. And I always thought that I wanted to do leadership roles and entrepreneurship. And after which, actually after Amazon, I joined Excel. So this was 2008 to 10. I spent three years there.
00:05:28
Speaker
And during Act One, one of my claim to fame is that I helped them lead the Cetron trip card.
00:05:33
Speaker
So you became an investor from being a detail. Why such a drastic shift? I always wanted to be an entrepreneur. So I always used to read about startups and all of that. And one of my colleagues at Amazon was funded by Excel. And they recommended me. I was leaving a two-starter startup and then they recommended me to meet them. They said they do investing. And I met them casually. It was not a job hunt. They were not looking to hire somebody. I was looking to join someplace. And
00:06:00
Speaker
But one thing led to another. I thought they were doing something very interesting and I ended up joining them. And at that time, they were still this is pre-excel based. And I was the only like younger person in the team. There were four partners. I was a Jupyter Atlas.
00:06:14
Speaker
But I spent three years and things worked out well for me. I'm at the ground and over time I realised that the India ecosystem will go through a lot of transformation, internet and tech boomers about to come to India. And I should be a founder again. So I left and started. And this was also the time I luckily, in 2011, I met some delivery founders and I was the first entity in that company. So I had the fortune of backing Tripkart through Excel and then delivery personally as a hotel.
00:06:42
Speaker
In 2013, me and Neha started Traction together. But Traction was not your first to answer, right? Tell me what your first to answer. So in 2011, I started e-commerce, which was into women's lifestyle, sort of what Naika or Parapal is doing. So it was an e-commerce marketplace. So we were selling all kinds of products.
00:07:03
Speaker
And I think we raised money from Excel and Tiger. And then after two years, we started running out of money. So we ended up sending to fashion and you. I was near the combined company for a few months. And then I decided to step down and start this company with me.
00:07:19
Speaker
Being funded by Tiger and Accel at that time is literally 0.01% of startups would have had that privilege. So what happened? So I think in 2012 there was a bit of a downturn like we are seeing today and we were not able to raise our next round. So we had raised 2.25 million in the first round and then there was a bridge round of 2 million.
00:07:40
Speaker
And then you were starting to run out of money and market was really good. This was the time when GA gave a term sheet to Flipkart, it broke and Tiger was also looking to sell all their assets. So they actually sold a lot of companies during that period. And so this was like classic case of that we had a little bit of runway. We had few months of runway and then there was someone down there and you get caught on the wrong foot. So eventually we were fortunate that we were able to find them some exit. Otherwise you would have to just shut down a little.
00:08:09
Speaker
And is fashion and you still around? I don't think it's around. Although I only want to prepare. It used to be a pioneer. I unfortunately haven't planned it so closely. So I probably knew about it for a year or two after the exit happened, but after that I have not planned. And this is like nine years back.
00:08:25
Speaker
So I met Neha in 2010 through a common friend. One of my contemporaries at IIT Kanpur was Neha's team at BCG. And she was leaving BCG to join a venture fund. She had some options in Kanpur. And she was asking around if they knew anybody for restaurant checks. And she ended up getting connected to me. And we were a few programmers turned VCs in the ecosystem. So we had a lot of common topic of discussion. And I
Conception and Establishment of Traction
00:08:51
Speaker
think
00:08:51
Speaker
The genesis of Traction's idea happened during that period, so we were building some tools and doing our jobs. We were writing some code to automate some work that we were doing, or we were building some database.
00:09:03
Speaker
As we started talking, we started exchanging a lot of ideas. Over two years, between 2010 to 2012, I think there was a lot of discussion happening. In 2012, Nihan left her job to go to business school, but she came out a few months earlier and then started working on the first idea that was there. I was still running Urban Touch then, so I was not actually a co-founder then.
00:09:24
Speaker
And then she built first version of the tool, and then she obviously realized that this idea is made for global markets, not just for India. So she went on to Stanford for an MBA. And after a year when my exit happened in 2013, we actually went to the US, we got incubated in Lightspeed US office. And we worked there for four months with some of their senior partners, debating how to enter the market. And eventually, it was a big topic of discussion. How do you enter a market? Eventually,
00:09:52
Speaker
If everything goes right, you can do hundreds of things, but how do you enter the market? It's like entering the check review, right? So you had to get that right. So we spent four months and then we came back with an answer saying that this is the way to enter and for that we thought it's better to come to India and build this company from here. So we actually closed down tractioning, came back to India, registered here and raised our first angel round here.
00:10:14
Speaker
What did you decide that this is the best? So when we were talking to people, there were a lot of ways to think about it. You could enter through CRM, you could enter through let's say portfolio management tools.
Private Market Dynamics and Traction's Role
00:10:28
Speaker
Sector-based coverage was one of the things that people thought or people liked it. And then we also thought it was an interesting model from a price point perspective. So you have cybersecurity as a market.
00:10:40
Speaker
Now, as an investor, you want to invest in it. So you want to look at what are the top cuts in that market? What are the top companies? Can somebody scout and show me new companies in that market? So we set up a team, which was us tracking cybersecurity. What more basic question here? So you're saying like Bloomberg or capital like your tools for people who are investing in publicly traded companies. And you wanted to build something like that for private investors.
00:11:10
Speaker
What is the world of private investment? What kind of companies invest? Is it buying and selling of companies or is it startup funding VCs? If you can set out a lay of the land of private investing, what is private investing? When you look at private investing, it's basically your VC funds, your PE funds, who are essentially looking at private companies mainly for investment purposes.
00:11:32
Speaker
Their workflow is essentially they would scout for interesting companies. Once a company comes to them or they're evaluating a company that they have to evaluate the company, they have to look at who else existed in that market. It's an interesting market or not. What is the theme? They have to understand that. That's the diligence part of this. The third is basically when you are investing in a company, you have to do a portfolio management. You have to track the companies. You have to decide when you want to exit the company, how does the market look like, etc.
00:12:00
Speaker
So that is your entire stack of private market from the lens of a private market investors, which are primarily VCs and PEs. And this is obviously global, right? If you look at the dollar spend, like US is a large market, India has now become very sizable market, and then YCA and Europe continues to be one. So that's the ratio of public investment versus private investment.
00:12:22
Speaker
So if you look at public total market cap of the companies, that's about closer to $100 trillion of market cap that you're talking about. If you look at private market, the private market AUM, for instance, is now has crossed $6 trillion.
00:12:39
Speaker
Okay. So it's like less than one tenth of bling. Okay. Got it. Yes. And if you look at the typical LP allocation, like an LP would allocate, if you look at the endowment. You can also explain LP. Okay. For people who are not trying the industry.
00:12:56
Speaker
So if you are a founder and if you have to raise money, you go to a VC to raise money. If VCs have to raise money, they go to LPs, which is called limited platforms. And the kind of LPs are varied. Examples of LPs include sovereign funds. For instance, a country has a lot of capital. They want to invest in dependent asset classes. That's a category of LP. Another very popular category of LP is your endowment funds.
00:13:19
Speaker
For instance, all these universities, especially in US like Stanford, Harvard, Yale, et cetera, their whole institution is actually run by endowment fund. So they have a large endowment fund like Stanford is about $23 billion or so. Harvard's is even larger and they invest across various asset classes and the returns that they get from that about whatever four to five percent that goes into the running of the whole university. That's a classic example of endowment fund.
00:13:47
Speaker
So when VCs have to raise money, they typically go to endowment funds or these large open funds or pension funds is another large category of IPs. So if you look at it from the lens of an LP, a limited partner, right? How do they look at this asset class? So if you look at typical endowment fund, which is one example of IP, they would have that typical allocation. When we started a decade back, the typical allocation would be say 40% to public equities.
00:14:15
Speaker
which is across US, across emerging markets and across all regions of 40% to public equities. And then your private equity, which includes VCP, would be about 4% to 5%.
00:14:28
Speaker
So you're talking about one tenth, right? 40% is public equities, 4% is private. What has happened now? If you look at a lot of this data is actually released by the endowment funds. How much is the allocation across the asset classes? Now that number has increased a lot. So for some endowment funds like Yale, it is actually your private market allocation has increased even across the public market allocation.
00:14:52
Speaker
So it has become higher. For some endowment fund, it's more than 20%. For some, it has also reached up to 40%, which I would say it's on the higher end. But typically, between 20% to 30% is now a ratio that you can see. So that is the other interesting thing that has happened in this market from where we started to now, that the overall market is also expanded. And you can also see the fact that right now, even around you, some other data, for instance, when we started the term, unicorn did not exist.
00:15:19
Speaker
Unicorn actually got coined after a company got started. And that is also because there weren't a lot of sizable companies getting created in the private markets. If you look at the same term unicorn applied, even like in 2010, a number of companies that would have become unicorn globally would be like just wine.
00:15:37
Speaker
And last year, there were more than 500 companies that actually became unicorn. It might have been even in India that number is now cumulatively across 100. Just the size of companies in the private market and hence the investable assets have also increased. And that is why you now see a lot more money, dollars also flowing into this market.
00:15:57
Speaker
At a broader level, someone once told me the reason why there is so much money flowing into private markets is because of the quantitative easing which the US did. I just want to understand what you think. Why is it that say these are dormant funds from 4%, they are now at 40%?
00:16:14
Speaker
in private equity investment. Why is this trend happening? That there is more private market style increase it. Correct. Correct. So if you look at, so we obviously track this data very closely because this is what defines that. So we feel that if you look at even a longer period of 20 years, public markets, AUM, asset and the management has increased at 5 to 6% K.
00:16:37
Speaker
And private markets have only increased at 11 to 12%. So it's not just recent. So quantitative easing has happened in the last 12 years. But if you look at even 25 years worth of data, I think broadly speaking, private markets have generated higher returns for investors. And then they are doing asset allocation. Obviously, when you invest in private market, there is a longer cycle. So in public market, I can buy and sell tomorrow. But in private market, once I invest, the money won't come up till my 2 to 10 years.
00:17:03
Speaker
There is a liquidity issue or like the availability of trading options like that. So, hence, limited partners have to also define how much they put in, otherwise the entire investment will become illiquid. But over time, as they have seen better and better returns, the asset allocation has moved.
00:17:22
Speaker
So they will move every few business points every year. And then over last 20, 25 years that asset allocation is in prison. This asset is not very old. Like some of the earliest venture funds are only like 35 year old. So this is a new asset plans in some ways. And for the first 15, 20 years, it was still very concentrated and very small. And for only last 20 years, it has complete next quarter. And that is why I've seen Amazon data company for this market is only created recently in last 10 years. Most of the players in our market got started in last 10 to 15 years.
00:17:51
Speaker
Okay. Okay. Okay. You had the idea of traction as a data player for private investing. Were there any global comparables, like any global company doing this, which made you feel this is feasible to do? So at that time, when we were starting, there were hardly even today, because it's a vertical industry, there are globally, if you look at it, there are only about four or five players. But I think
00:18:13
Speaker
What we realize is the pain point is not solved. For instance, a lot of people provide you transaction status, like a good transaction status, which is there, a financial status. But what you need as an investor is you need to be looking at pockets on a daily basis. There are new companies coming and pitching to you. You have to evaluate the opportunity in a couple of days' time.
00:18:34
Speaker
So what we started off building actually did not exist. And we more saw the opportunity more from a market perspective that there will be a data platform, large data platform, which will be also created in the private market space. Correct.
00:18:49
Speaker
And when we were starting, we were thinking that these things should exist. If I'm looking at a company, I want to look at all the other things that exist in the market. We would actually just stylus and search Google or ask around to get new companies. So we started with saying there's something like this should have existed. And then I think over the years, we realized that, okay, public market has a definitive or a capital IQ.
00:19:10
Speaker
And hence, there is a parallel, but we didn't start thinking around from that side. We started thinking that we are investors and we are struggling to do these basic things and there should be somebody who's solving it.
00:19:20
Speaker
And over 2-3 years, when we figured out nobody's solving it, we started to take a stop at this problem ourselves. And over time, we built our thesis that how we'll draw parallels to other markets and all that. But I don't think we started just looking at that file and then started that. We just started primarily saying, I got a company. I want to see comms. And there's no way of placing a look at it. Yet, it was doing bad ones, getting to do this job. That's not a good way to create it.
00:19:44
Speaker
and pieces got built a lot later, I think over the years. So you started with cybersecurity. Tell me how you built up that one vertical and how did you get the data for it and how did you then sell it to how did you get sales for it? Tell me that journey.
00:20:02
Speaker
Yeah, so for us, I think discovering the first product to launch was a little bit non-obvious, right? Because in our market, you didn't have a player, you could say, there's a CRM that exists and let me launch a better version of the CRM. That did not exist. That budget did not exist. So that budget has got created in the last decade. So for us to figure out which module to enter, Puma actually required to work with a few customers and then define the module as well as pricing.
00:20:27
Speaker
So the first, as Abhishek was mentioning, the first module that we actually started off with is basically a sector-based cartridges. And we started with US as the market when I was doing my MBA in Stanford and we got into it at Lightspeak. So we spent about a couple of months in figuring out the module and the pricing that we want to launch with.
00:20:44
Speaker
So the first sector, we started with sector-based coverages as the first module. And we worked with the funds in the US because the US was going to be a large market for private market investing. And we worked with the funds and the corporates over there.
00:20:59
Speaker
And a very interesting thing is unlike in India, if I look at India, a lot of the initial investment was around in the consumer space. Whereas if you look at the US, a lot of the people actually saw enterprise because they have made their money in enterprise. They have seen the company scale and they feel like more predictable versus consumer. It's a little bit hits driven at times. So a lot of the initial set of customers that we worked with were looking at enterprises, the
Traction's Data Collection and Sector Coverage
00:21:25
Speaker
market.
00:21:25
Speaker
And in fact, even in enterprise, the core infrastructure, not the application layer, the core infrastructure stack that they were working with. So some of the sectors that we started off with is actually cybersecurity and coin, which was way back a decade back. IoT was the other sector that we started off with. And so the initial task was basically just build a good landscape of all the companies that exist. Anyone who's looking at it, it should be very effortless for them to find out new companies, evaluate new companies, et cetera.
00:21:53
Speaker
So for that, we then started building a team in India who would then build the whole coverages. And then we continued to be in US for the initial quite some time and work with customers, show the output, see if it is interesting or interesting. And then after we showed the output to the customers, they were like delighted. That was wonderful. It also helped us in initially figuring out the pricing and starting the first paid customers.
00:22:17
Speaker
So that is also when we realized that I think the cycle has started that people are happy to pay the price and they're basically just asking us for more and more things. And we actually started off with a high price point of $5,000 a month, then eventually came down to $500 a month, which is what is our current pricing.
00:22:38
Speaker
What is the data that you connected on, let's say, cybersecurity? A list of companies, their employee headcount, their turnover, what all did you collect? And how did you collect?
00:22:50
Speaker
So one of my jobs, for instance, when I was an investor, one of my job, if I have to look at the sector, then I have to do a sort of a sector scam. And what that means is essentially find out all the interesting companies that exist within say cybersecurity, figure out what are the different type of models which exist, right? Someone is doing endpoint security, someone is doing some network security that various ways people catering to this market, right?
00:23:17
Speaker
What are all the islands? What is basically your taxonomy or the business model stack that exists within that market? How are all the companies placed? How competitive is each stack? Then you find out new companies that are coming up in that space. So whenever you find out a new company, you place it in that market map so that it's very easy for you to compare, okay, this company competes again in this company. So hence, how interesting is this going to be?
00:23:43
Speaker
Okay and like for the company per se you also look at like getting their revenue headcount and such kind of data. Yeah so one is basically the company where what model it exists where does it place in the market map of cybersecurity. The other thing is the information about the company how old is it, where is it located, how fast is it growing,
00:24:03
Speaker
What is its stage that it is in? All the metrices, which helps you also figure out whether this is interesting for me. For instance, different investors have different stages of investment, as well as some invest in early states, some invest in late stage, some invest in a particular geography, like I only invest in the US and Canada, some invest in Southeast Asia, help you define all these parameters to then see if their company is interesting or not. And then obviously, so when you are evaluating a company, you'd look at the
00:24:30
Speaker
some of the matrices, which is how fast is it growing, what is the team, what is their background, etc. Plus you would also look at the other market matrices, for instance, which node is it falling into, how competitive is that, is that company likely to run into an existing dinosaur in that market, or is it a large aggressive player or not, helping you evaluate that whole thing. And this is publicly available, like there's revenue growth, things like that, like how do you get that data?
00:24:57
Speaker
Yeah, so currently, so I'll give you the current now, obviously, we have a lot more data about companies. So essentially, we would get information from a lot of publicly available sources, just you would go to Google, you would search for a company, you'd get information about the company that the company talks about. So we'll get that.
00:25:13
Speaker
Plus, we also get information from the regulatory filing. So we actually go to the previous regulatory filing to get information about it. Right now, I think the other thing is that if you look at user data, it's going more towards privacy. But if you look at company data, it's going more towards in the direction of transparency. So that is important to have more and more information about companies so that it can also help investment, etc. So you are able to get information about that.
00:25:37
Speaker
Okay, okay. Interesting. This would need a lot of sectoral knowledge, like insider knowledge about a sector. If you were to create these different buckets within one sector. So from that one sector of cybersecurity, how many do you cover today?
00:25:53
Speaker
Today, we cover nearly 2,000 sectors across all industries. OK. So how do you scale up the insider understanding of a sector? You had a lot of insider understanding that you needed to make it as an relevant product for an investor, like deciding that this is into 8-point security, or this company is doing this, or this is the trending part of it, and so on. So how do you scale that up, that insider understanding from point to point?
00:26:22
Speaker
No, that's a very good question. And that's actually a core part of the org as well as the process, as well as the platform that we have built. So we literally want like a person to do a PhD in one sector. And in our case, what has happened is that all these sectors are very new. If I want someone to know about say Bitcoin or Web3 today or all these sectors, there isn't a person who knew about
00:26:47
Speaker
in a lot of detail earlier, you anyways have to learn about these sectors fresh. So we literally want a person to spend a lot of time in mapping the sector and understanding the companies. And then they are able to do a call that this is an interesting model that we should cover within that space. This is what the company corresponds to. This is the business model of the company. And the second interesting thing is that they do this globally. So
00:27:13
Speaker
When someone is looking at within FinTech payments, they're looking at payments companies globally and not just in one geography, so that they are also able to track any new themes which is coming up across any country, even far off. I think that is there. And I think the third thing which we have done is obviously built a lot of tech to be able to do that. We even are right now, we actually track like more than half a billion companies at the backend. And from there, our platform actually throws up that this is the next set of interesting cybersecurity that we should look into today.
00:27:44
Speaker
And so that platform has also become very intelligent over time, which is able to mine a lot of data and then throw up the companies that among what I have a million companies, these are the next set of 100 companies that we should look at. And we end up tracking about 50% of those.
00:28:01
Speaker
I want to go deeper into these two things which you mentioned. One is people making them PhDs into that and second is tech. Let's start with people first. How did you build your team? How do you really create people who become PhDs in that domain which they are tracking?
00:28:18
Speaker
So even if you look at our team today, we have nearly 90 sector focus analyst team, which is actually focused behind all the different sectors. Right. And one of the key things is that when someone has to, when someone starts covering a sector, there's a whole training, which is there for them to become intelligent of the sector. The second thing is that there's historical data, which is there, which is basically the coverages that we have on the platform.
00:28:43
Speaker
which they are also able to learn and then figure out, come to speed on that sector much more efficiently than what we, the time that we used to take when we were investors, now they can do that in a matter of a few days, what took us probably a few weeks. And then also the tech platform which constantly keeps bubbling up new companies, which then they have to analyze, they take a call whether this is interesting or interesting and then figure out again what the company does.
00:29:09
Speaker
Tell me about the evolution of the tech platform. You must have had version one platform, which today must be like significantly better than that version one. Tell me that journey, like what did you build? What did you prioritize on building and so on? Yeah. Yeah. So tech is basically a very important part of our whole traction, I would say. And quick background, like both me and Abhishek are actually Comm Science undergrad. So our first, our first inclination is that if we can solve the problem purely with tech, can we do that or not?
00:29:37
Speaker
Obviously, we realize that it has to be much more curated enterprise rate, etc. But obviously, we did this proportional investment in the tech platform. Even if you look at that initial, most of the dollar spent would be probably in building the platform that we had done an upfront investment in. And because of that,
00:29:55
Speaker
Give me some, if you can paint a picture that this was version one, this was how primitive it was and this is what it is today. I think initially if I were to look at it, we would probably start tracking like on two things. One is the number of companies that we would track on the backend. So we started with a few hundred thousand over time, it kept growing. Today we are tracking more than 600 million companies on the backend.
00:30:19
Speaker
So that is just the set of companies that we have tagged in the backend and obviously there's a lot more information about each of the companies that has also increased over time.
00:30:26
Speaker
as one. And the second thing I would say is just that on the data intelligence part, earlier when we started, we could actually tell if there's an interesting company or not with a probability of I would say three to 4%. So in 100 companies, if we were to actually add a platform would say the correct answer would probably be like a single digits percentage. Today, it's as high as 50%.
00:30:49
Speaker
So the platform has also become more intelligent because obviously a lot of data has gone into making it figure out what is that interesting set that we want to track. And this algorithm is also one of the core IPs, right? So it's not about just coding it, you have to feed in like millions of data points for it to come back and take your now accuracy is 50%. So the code doesn't change, but the underlying data of like conventional new machine learning, you have to feed in a lot of real human curated data for it to succeed.
00:31:16
Speaker
So it has taken us five years of data inputting to go from 2% to, let's say, 40% accuracy. But I think that is also, this is 40% accuracy of saying that a company is interesting. What is this any company's interest? Since our probability of finding out if this is an interesting cybersecurity company to cover or not.
00:31:37
Speaker
Okay, so I said you can decide whether to include in traction data.
Traction's Global Platform and Revenue Model
00:31:43
Speaker
I'll give you an offline example to help you build a case. So if an investor is looking at a market, that's how you want to fund an ice cream chain. Like a single ice cream store is not exciting, but a chain with more than five stores is interesting. So by definition, 99% of the market is not exciting for you.
00:32:01
Speaker
Or if you look at cap companies, like there are a lot of small SME shops who run 5-10 caps, that is not exciting. But somebody who has thousands of caps, so 99% of the market is usually not attractive for industry market.
00:32:13
Speaker
So a system has, you cannot say, I'll search for ice cream stores and then look at every ice cream store. So you have to do a lot more signaling around that and then say, okay, I think out of this, everything, these are probably 100 most exciting ice cream chains. And then earlier, only 3% of that would be interesting. Now that number is like 40%. So algorithms have learned a lot who say that this is most likely and from the interesting, which how many industrial perspective or a public of their perspective?
00:32:41
Speaker
And so once the algorithm throws up companies, then there's a human curation. Yes, absolutely. Somebody will look at it and say, you know, you do that. And again, every time you look at it, that decision goes back into the system and accuracy gets better. Makes it smarter. In some ways, it has no one, right? So eventually it will reach a point where you are highly accurate. What is your funnel like? This is pretty interesting what you said that 99% of the market is not worth covering in traction. So what does the funnel look like?
00:33:09
Speaker
Where do you get the other data source that the larger data source from? And how does that get winnowed down to what you cover actively here? Just help me understand that. I think if you look at the market, public markets typically have 50,000 to 100,000 companies which are listed. And that if you go to all the exchanges, you can get an onboard immediately. And these are the listed companies. Private entities work at like hundreds of millions.
00:33:36
Speaker
Out of that, investible segment is much smaller. So companies which are scaling, people want to invest in a company because they're in scale or they've already attained a certain scale. And that universe is just really hard to find.
00:33:49
Speaker
And that is what you end up solving with this. And just to think about it, like if you look at ice cream stores, there are millions of ice cream stores globally. This data of the entire universe you get from filings, like companies have to file in this, like in India, there's FDA, so similar. So you can start at multiple places. I think what we did was that when we started the company, digital footprint of the companies were exploding.
00:34:15
Speaker
And so 10 years back, you could not have built this company 10 years back because then you would have to start with legal entities and that is a much harder problem to solve. When we started, all meaningful businesses started to their digital footprints. You wouldn't find the large scale companies which probably don't want to have any presence in digital.
00:34:32
Speaker
So people had websites, we had social media presence, people were hosting jobs. So people were getting into news, all the news was coming online. So there was so much digital footprint about companies that we started with digital footprints and then stretched it around the company. And then you use all these signals to say that this might be exciting. I'll give you a very trivial example to build a case. So for example, if you are a website, there's that digital checkout button.
00:34:57
Speaker
Then there is the likelihood that there is a higher likelihood that this is an interesting company for us to look at. But for that matter, if the company website says that our investors, then you know that there is a higher chance that this company is more exciting than a company, which is not. So over time, we have built hundreds of signals about each company, stretching together all digital footprint, something you can find.
00:35:19
Speaker
So because we are starting in a digital era, we started with digital footprints of these companies to find out. And that's why we are able to sit in India and actually cover companies across the board. Otherwise, you would have to have a lot more physical presence across the globe, and it would have been a much harder problem. I think this company would have been much harder to build 20 years back. What is the take you built to capture digital footprint? Is it like what Google does, like how Google is, it takes in pages constantly? Is it something like that you built to capture the digital footprint?
00:35:51
Speaker
So I think we internally call it Google for companies. So I wonder. So the way I've been indexing the entire web, we focus on indexing companies. So we don't want to index everything all pages. We focus on saying that these are the company footprints, or these are the key company footprints, and we focus on that. So to give an example, you see what to look at a particular website, it would distract their home pages more closely than let's say the entire fontanel website.
00:36:16
Speaker
because homepage tells us enough for maybe one or two level deeper will tell us enough who judge as a human being that this is an interesting company or not. So that's what we end up doing. And I think the other thing is that we had to also invest a lot in technology because we started with global first as a problem. We didn't start with one country, let's launch for one country and then let's do another country. We actually launched the sort of the global coverage first. And I think that is why also tech became like a key cornerstone of our entire infra.
00:36:45
Speaker
And also in other some things that people actually don't realize is actually seventy percent of a review is actually outside india so a lot of the people that i speak to say that you know okay it's about the coverage and we actually have customers in for instance germany looking at german companies through the platform in us looking at us companies through the platform.
00:37:06
Speaker
And actually customers now span nearly 50 countries. Tell me the revenue journey. So you started like, which was the first year when you made revenue, what revenue did you make that time? What was the pricing like? And how is that evolved? What is it today like? So we launched the platform in 2013. And at that time, we actually did a small angel round from people that we knew and fortunate to have had obviously really good set of investors all along. So there is the first angel round. The company actually turned profit table.
00:37:36
Speaker
And then we raised the first institutional round. How about each of these? So the first round was 90 lakh, less than a crore, first angel round. This was way back in 2030. And then the first institutional round, we got investors like Sachin Bini from Flipkart, Tsai from Delivery, as investors in the first round. And then we raised the first institutional round that was from Elevation, who was earlier staff.
00:38:01
Speaker
And then we also got an opportunity to get wedding marking angel investors like this. And so after the institution round, obviously, then we started scaling the... How much was the institution round?
00:38:13
Speaker
This was about three and a half million. This was like a typical CDSA check event then. Yeah, absolutely. And interestingly, after that, we didn't have to raise a large round. That was also because of the fact that we were enterprise. We had fairly cash efficient in that sense. So our total dollar raised in late is only about 17 million in which we still have part of the round that we had raised in the last round still in bank. And we also turned the profit table about a year and a half back.
00:38:41
Speaker
And this year, last year was also the first fat frosting year. We also turned cash towards much earlier. So right now, though, obviously, it's a profitable company. We add cash to the treasury every month. All the growth will be. So if you look at just last 12 months, you would have been hearing a lot about downsizing, but we actually increased the team by over 100 people. So today we are nearly a little over 800 people in all.
00:39:06
Speaker
And so I think all the growth has been, the business is able to like, we are able to invest a lot of the cash back for us growth. What is the split of these 800 people? Like how many in what function? There are about 100 people in tech, which is a cross tech product automation engineering team.
00:39:23
Speaker
about 150 members in your go-to market, which is your sales marketing. Then you have a large team of about 100 sector focus analyst team, and then about 200 people in another data operations, which are across the various data modules that we have. We have a lot of bunch of data modules. For instance, if you are looking at a company in UK, if you want to figure out their financials, their cap table, etc, we have a lot of data points.
00:39:51
Speaker
What is this data model? Sorry, this is like the core product or is different from the core product? This is part of the platform only. So everything is basically part of the platform. In the platform, you have one is your sector-based coverages that you have, which is basically led by the analyst team. And then you also have a lot of data about companies, which is one of the points that you were yielding to earlier. For instance, if you're looking at one particular company, like Fabric Company, you want to look at what is its stage? Where is it located? What is the previous?
00:40:18
Speaker
What is the valuation, last round valuation, what is the goal rate, etc., tip size, etc., all these things. So these are all the data modules that are also there about private companies, which basically then merge together to make it very actionable when people are looking at companies.
00:40:33
Speaker
Got it. Okay. Tell me about your go-to-market.
Sales Strategy and Pricing Evolution
00:40:36
Speaker
How do you acquire customers? How did you get that flywheel started? Is it like, is your pricing like a single price point for the entire platform or are there tiers of access or is it like some geographical restrictions? I want to understand all of that. So I think initially, like most companies do, we has held our way through, let's say, first 20, 25 customers.
00:40:59
Speaker
So Neha and me were doing sales, I was going to demos in India and Neha was doing sales in US. And she spent those two years doing sales, a lot of sales herself. And once the cost was there, we got the $5,000 pricing. This was at that time. Actually, it took first. Like, by then, very quickly, we came to $500,000. So $5,000 was there for the entire organization. Then we both were simplifying things. So we said, okay, $500 for 3P per pricing. Yes.
00:41:25
Speaker
So I think we moved from account-based pricing to seed-based pricing very quickly. But I think most of our new customers were on seed-based pricing. And after we had 20 out of all accounts, we knew that if people are buying, people are happy to pay for this money and they'll not go away immediately. Then we started worrying about scalable playbook.
00:41:45
Speaker
And that time, I think there was a lot of buzz going around inside sales. And because we had large presence in India, we thought we will give it a shot. We started working remote sales teams. So there was some demand general work, but otherwise they were giving the demos remotely and then sending the product. That's the value for us. And today our entire sales is actually done from
00:42:06
Speaker
Bangalore slash India because before lockdown, we were all in Bangalore, so all the sales was done that now. Team is slightly more spread out, but theoretically, I can say that all the sales is being run out of the office in Bangalore. And that is something which is quite unique to us, which not many people have been able to scale. But for us, that playbook works because outside sales, we do a lot of content marketing. So we generate a lot of reports on these sectors.
00:42:29
Speaker
So, for example, cybersecurity will run out of an annual report, which is very widely circulated on our customers. So, a lot of these people have heard our name before they come and think about buying our subscription. So, because of these five brands, we see customers are comfortable buying online. And these are medium-sized purchases. These are between $5,000 to $25,000 a year for contracts. So, people are comfortable buying it online. And after that, our support teams work with them and expand the company.
00:42:58
Speaker
And so this was our go-to-motion strategy. How did we do marketing and sales? And I think on the pricing front, we were obviously debating a lot of things initially. Should we do report, pricing for the phone? Should we do all of that? But by then, we had started to realize that there is a funnel for us. Public markets already have data platforms and we can learn a lot from their playbooks.
00:43:18
Speaker
So we saw that all of them do one platform bundled on broadly most of the module in the core product and then they do seed-based pricing. So we moved to that. So we started $500 per seed per month and then let's say subsequent seeds are cheaper. So $1,000 a month, give you three seeds instead of two.
00:43:35
Speaker
and $2,000 a month used to give you 7 seats instead of 7 seats. So you do that in some discounting for wealth buying, but broadly, that was poor pricing. And we are stuck to it. I think last year, we increased from $500 per seat to $600. But other than that, we have, as a company, tried to be more predictable for our customers. We don't want to change pricing too often.
00:43:57
Speaker
And so for first 80 years, we had one pricing, which was $500 per seat for the first, per month of our seat. That is now $600. I hope we stayed for quite a while now. And I believe as a customer, when I'm buying a product, I want a stable pricing so that it doesn't change. And I don't have to relook at my financial composition every few days. So that's what we have been doing so far. Neha, anything you would like to add? I think, yeah, it was one of the things.
00:44:25
Speaker
So essentially you have already a lot of data and content so that you package it and use that as leadjet like those new reports, brand building and leadjet that like overall recall, building recall and inside sales.
00:44:40
Speaker
Yeah, I think that is something that works really well. So for instance, if someone is searching for semiconductor reports, they actually come across some of our reports. We also have vertical industry newsletters. For instance, if you want to just stay on top of what is going on, ensure globally there's a newsletter which you can subscribe to that you can track all the news through. So these are some of the things that we can do because we are a data platform and that is how we get a lot of leads.
00:45:07
Speaker
Got it, got it. Okay. And does your sales happen through like self-service, self-checkout, all of that? Or is it like assistant account managers led? So in our case, actually most of our users are enterprise users. So they would want to understand, they would typically have some of the queries, they would have a use case, they would have something. So we do have a call that you do before you actually start voting the customer.
00:45:36
Speaker
It also helps us explain them what the entire offering is. Because otherwise if they do sales checkout, I think they can just look at one small piece and they would not adopt the entire product. So they are at there and they also want hand rolling and we also prefer to hand roll because we get an opportunity to send them everything.
00:45:52
Speaker
And then they sign contracts and they don't, and typically some are quarterly, largely annual, so they will sign up and they renew everything. And I think this is also a product where you need a lot of hand-wording when they're onboard. So it's not as if you sell the account and you can onboard it. There's somewhat of learning curve, and this is also true for public market data platforms.
00:46:13
Speaker
So you want some account manager to come explain to you everything. It's remote, but they spend some time in their onboarding. After that also, they do regular checkpoints and make sure that they understand new things. And I think public market guys have done an exceptional work at teaching their customers new things because their products are significantly more complex than where we are. If we have to offer 10 things, they have to offer 100 things today.
00:46:35
Speaker
So I think we had the luxury of learning a lot from them and otherwise it would have been much harder to crack it on your own using first principles. So I think both on the buy selling side, as well as on onboarding, we want to spend a lot of time with them to make sure that they are updated properly.
00:46:53
Speaker
Now I'm speaking as, let's say, as a layman, when I'm searching for, let's say, funding of XYZ startup, typically I will see, say a trend space in the search result, which has a layman seems to be similar to Jackson. What are the differences that they have a freemium approach where they will show you some data upfront. And then if you want more data, then you have to become a paying subscriber. So why don't you also do something similar? Because you also have the data, which they have.
00:47:20
Speaker
But if I'm searching for funding on whatever XY that company name, generally it will be crunch based, which will come there or yours or your one of these types. So what is the strategy behind your choices to not do that? As I was, as you were yielding to earlier, when we actually started, there were a lot of these transaction databases that would exist. Right. But for instance, if you, if you are an investor, I think you still didn't solve my problem or
00:47:45
Speaker
either sourcing or diligence. So yes, so probably for a very lightweight user, it is easy to quantify and that probably might be sufficient. But if this is my day job and I have to, this is my day job of constantly tracking all the things. If I'm a DTC investor, I have to constantly track all the brands which are coming up. I have to see what are the use areas within the brands. So then it is more of an enterprise grade customer. So we focus more for the enterprise use case, not for the lightweight. In fact, none of our users are actually even companies.
00:48:15
Speaker
or people are just looking at it are actually users. So our half of our investors and actually half is large corporates, which includes 70 Fortune 500 corporations as well.
00:48:29
Speaker
Yes, and so we're actually looking for business-critical decisions. So for them, the requirement of the data hall companies about this is much more nuanced or much more detailed. So you're saying coin space is largely transactional data, and it's more of a B2C or a B2ProC, like a prosumer kind
Future Growth and IPO Plans
00:48:48
Speaker
of... It's not that enterprise-grade.
00:48:50
Speaker
So I think it became a lot more easier to understand when we looked at public market data companies as well. There is a lot of data available on Google and Anzil, a lot of data available on other kind of laptops. So your enterprise-grade customers will rather pay 5x more, but get significantly higher quality. And most of the times my customer doesn't come and tell me that, okay, can you reduce the pricing and get me one person lesser accurate data. And they all say, you know, charge me 2x, I'm happy to pay more, but give me more hyper data, give me more comprehensive data.
00:49:20
Speaker
So even in public markets, like the largest players are companies which are taking Bloomberg, we hear from people that it is closer to $20,000 per seat one year. So it is quite high, but customers happen to pay, and their revenue is closer to $13 million a year now.
00:49:37
Speaker
People pay because that's what they need. And so I think if I'm doing mission critical work, I would rather pay and get good what I need, rather than saving money there. Okay. Okay. Got it. Got it. Okay. You've been building traction for about a decade now. You told me that you are planning for an IPO. What, where do you see your journeys and traction's journey over the next couple of years or over the next decade? Where do you see it going?
00:50:00
Speaker
So as we as founders are actually, we are fairly excited about this market. We feel it's a very deep market and it has just gotten started. And it is going to, if you look at the quality of companies coming up and everything that is happening, the quality of talent that this market is able to attract today, then what it was able to attract like the decade back. Our excitement has only increased.
00:50:22
Speaker
So we believe that this will become a large market and that is why we want to continue to build this. And one of the best way that you can actually do, continue to build the company for the next 40 years is actually to take the company public. There is a, there is an option of whether you should, you want to run private or public. You feel that if you're running a public company, it's much more accountable. It's much more dynamic that you can run it with much more systematic manner for the next decades to come. And that is why we had taken the call of then listing in it.
00:50:54
Speaker
So interestingly, we actually started the process earlier. And that's it. But I think it is going to be cyclical, that is there. And even if you look at the whole market, so that is one of the things that I also tell that there is going to be market cyclicity. For instance, the Fed increasing the rates on Russia going to war in Ukraine, you cannot predict it, but it is going to have an impact
00:51:16
Speaker
So you should just like the small things. So what we say is that, you know, how in public markets, you cannot predict what the next six months is going to be, but you can definitely predict what next five years are going to, because if you are consistently building the company, if your market is large, if you are having the old tailwinds, which is also there and you are able to execute well.
00:51:35
Speaker
Then you can definitely predict the next side. Abhishek, you were saying something. And secondly, I feel that for companies, going to public is next natural step. If you feel that there is a 40-year work of growth in front of you and you have figured out your business is stable and it's growing linearly, I think you should go public. That provides the necessary discipline.
00:51:55
Speaker
It's like if you're an athlete, your natural next step is to go and represent your country. If you're a cricketer, you want to go and play for the national team. So going to public markets are like that. So I think we always thought that once we are mature enough, we will actually go and list ourselves. And so I think for most entrepreneurs, eventually it's been rather than if you should go public or not.
00:52:16
Speaker
If you look at all large companies globally, eventually large companies going public is the necessary condition to remain disciplined and remain accountable. Okay. And tech companies haven't gone in India. Tech companies in India haven't gone public in fast. So that's why there is a question. But I think in next 10 years, that will not be a question anymore. If you will just assume that once you build a company, it's you reach the flow where you are growing rapidly and you are what you're doing.
00:52:43
Speaker
then you go and rest yourself. But currently, both of you are holding the majority stake because you did not raise much, so probably you did not dilute much also. Yeah, so we would fall in the traditional public companies have the promoter kind of a title. So we would focus, which is having some sizable stake. So yeah, I think we continue to own.
00:53:04
Speaker
So as far as filings like outside ESO, which is 10% or 10.9%, we own 41% and investors own 49%. And then on top of that, there's a 10% plus ESO pool that is there. Even though 70% of your revenue is global, you are like very Indian in your outlook, like planning to list an Indian market as compared to say like Freshworks, which listed in the US. So why did you choose to be so India-centric?
00:53:34
Speaker
So I think when we were starting, we always had our eye on going public. We weren't at that time still sure in near US. And I think the initial days we met one of our advisors who also became shareholder leader. So he was mentioning that, look, you had some state in your early night in company, you will have to decide, are you building it for Ebony or are you going to try to go?
00:53:53
Speaker
If you're building it for IPO, you have to worry not more about setting up processes. You have to be cognizant of all your power structure. You have to build it. You have to build it assuming when you go public, everything is open to scrutiny and you will see that you have done a great job at building the company.
00:54:08
Speaker
And we got more serious about it. We initially thought when we have five to seven years down the road, we have to decide. But his advice came in handy. A lot of companies, like he was on board, those companies had gone public and fast. So his advice came very handy at that time. And when you were thinking about that, you also thought that you want to listen to US or India. We didn't know the exact time at that time, but we knew that this is going to be a very, very cash-rich industry because generally,
00:54:33
Speaker
you are selling data to financial industry, financial industry largely has been very rich. And we also knew that innovative companies are the future. So most of the well creation is happening in innovative market as compared to mature markets. So we, and you had to work as a model also again. We saw that and we saw that this market should be ideal in that market. And so we intuitively knew that we are among the richest markets globally.
00:54:57
Speaker
And India likes profit stories more. And that was the time when we thought that maybe if you want to listen, India should come here. And then we actually registered in India. Then we moved our entire thing. We closed the US equity and we started just working here.
00:55:12
Speaker
So we had our eye on going public very early on. We built our company, keeping that in mind from day one. So everything was systematic. We were very disciplined with how we can set the processes very early on. Our CFO was among the first maybe 10 employees in the company because we knew we needed a solid financial muscle and that person is still our CFO. So we planned everything accordingly and that drove a lot of our decision or how much discipline with which we run the company so far.
00:55:39
Speaker
Amazing. Okay. Yeah. And just to add, I think we'll be among the first few pure SaaS companies to be going for listing in India. For instance, obviously a lot of the SaaS companies have been HBO in US or in Singapore, but we are a pure SaaS company, 100% of our revenue is subscription based. And among the first few ones to be going for listing in India.
00:56:00
Speaker
There's that famous ratio that VCs look at which is CAC to LTV ratio. What is that like for you? That is very awesome for us because we have a high price point and we are able to do inside things from India. CAC to LTV is more than 10 which is regarded as pretty good.
00:56:18
Speaker
But I think it's an unsaid comparison because those guys are doing an interview to catch where the sales team is in the US. So the customer is in the US and the sales team is in the US. There you have replaced your customer is in the US, but your sales team is in the US, which is less than one-tenth the cost. You don't have a single sales person outside India. Actually, everybody was in Bangalore before the long term. Entire production, we are a unique company, the entire production is in India and the entire distribution is also in India.
00:56:47
Speaker
And we found a way to make it work and now it's been great for us. It's phenomenal. This is essentially like the product-led growth approach, where your product, which is the reports that you published, basically the research you're doing lets you publish good quality, unique reports, which help you get growth. Amazing.
00:57:09
Speaker
Based on so many millions of data points that you're churning out and studying, what are some of the interesting trends that you are seeing, like some future gazing, if we can do? It's a very broad question, I know. I'll tell you one of the most interesting things that I noticed in the last nine years, as an investor, you always used to say, you can't stop an idealist's time, but it was like a motherhood statement. You wouldn't have data till back end.
00:57:35
Speaker
And actually, when we did analysis, we realized that because we grew up competing to taxonomy loans, you could actually see mushrooming in the data. You won't see that one idea suddenly, for years, nothing happened. And then suddenly, two years, you won't see the company founders look at that idea and start executing that. So we started seeing mushrooming first, and it was so exciting.
00:57:56
Speaker
So it was really amazing to see that data firsthand, that questioning is real. Across the globe, we are able to see the same idea, same time, because the market itself, something has changed in the underlying market, because of which a lot of signals together think that this is possible to build today.
00:58:12
Speaker
And that was the most amazing thing that I noticed in India. In short, there is an explosion of companies which are doing insurance for employers. Yes, absolutely. Employee insurance was a great example. Suddenly for 50 years, nothing happened in the last three years. Everything happened at once, right?
00:58:32
Speaker
Similarly, you can see even the other ones, like space tech, there weren't a lot of companies, there are companies there, there are companies which are, even in data, they are tracking satellite data through that. So there are a lot of these nodes. So right now we actually have this view on the platform where you can actually track emerging nodes.
00:58:51
Speaker
So you can track where the mushrooming is happening in a particular industry.