Finscale in English
00:10:05 · Charlie Tafoya, Solenne Niedercorn
For emerging managers, we're seeing this again really across all parts of the fundraising cycle. And now just given the broader fundraising context for GPs, which is to say it's been a difficult couple of years, I think GPs have woken up both the operational opportunity to have streamlined operations and ensure a true enterprise, if you will. But then further, it's it's really become, again, a precursor to actually going out and trying to fundraise. So happy to comment more on sort of the specifics, which is to say there's high degree of variabilities across different sub strategies, etcetera. But more thematically, we just say that, yeah, we're reaching a point now where again, people are benefiting from technology and realize the benefits of data. [00:10:48] S1: Yeah, so it's just the beginning, I believe. What role would you say advanced analytics play in improving the insight into portfolio performance? And is it something that is currently used by managers or not sufficiently in your opinion?
00:11:05 · Charlie Tafoya
[00:11:05] S2: Well, so Chronograph provides two different products, and this is in response to your question. One is for LPs, which is more analytics oriented. Because for a limited partner, the challenges are ones more of data aggregation, right? How do I take in all this information and structure in a way that gives it meaning? And then further, how do I then interpret that information in an efficient way? If I am a pension plan, for example, I may not have the human resources, the analysts and associates needed to actually process the volume of information I'm ultimately receiving. And so in short, there are probably questions I can answer with the data I hold, but I just don't have the operational capacity to address that. And so that's a good example of someone who will engage Chronograph to help them in effect get out those answers. For general partners, I would say the issue is less analytics. There's a lot of certainly very smart people that work in private equity, a lot of great people who can model. There's a lot of talented technology folks as well now who are involved in all aspects of the value creation process at private equity firms, inclusive of analytics, but also now increasingly AI applications. There, would say the challenge is one more of data availability. Do you have a process in place to actually collect data in an efficient way, again structure it and then report out to your LPs and make it otherwise for portfolio monitoring, again, a way that maximizes your own understanding of your portfolio and opportunities, again, for value creation. So I draw that distinction between LPs and GPs to say that there are different sides of the coin. GPs again need to be more focused on basically the process of gathering and creating the data that they then report out. LPs conversely are more focused on receiving that information and consuming it in an efficient way. So hopefully that answers your question. But yeah, trying to provide a bit of, again, broader context here.
00:12:54 · Solenne Niedercorn
[00:12:54] S1: Okay, interesting. Thank you very much. So now if we focus more particularly on Chronograph, I would be interested that you describe to us the way you launched the business. So now it's been years that you launched it, you already have great investors. And we'll talk a little bit about your investors. So what was the starting point? A blank page? A team? How did you start this business?
00:13:17 · Charlie Tafoya
[00:13:17] S2: Well, you already mentioned some of the original context, was to say sitting there and looking up one day and seeing a bunch of high cost investment professionals manually typing numbers into Excel, which I'm sure many of your listeners will identify with. And that just struck me as a fundamental disconnect. And so in short, started to do some diligence on, well, how have others solved this problem? What is available? And again, you can ultimately, I suppose, call it nerdy arrogance. But we felt that there, in short, no one was really addressing the problem in the right way. So around that time, I actually was introduced to my co founder Michael Bridge. Our wives actually worked together at Bain as consultants. And they've been encouraging us as tech nerds and private equity guys to get together. And we deferred and demurred a bit, but ultimately did get together in Brooklyn, which we're still headquartered today. We had a coffee, which over the course of the day turned into an evening whiskey. And we just saw a lot of the same fundamental problems from the perspective of both an LP and a GP. So both Bridge and I, as he's known, are developers by background. And in short, we were able to ultimately shake hands and sit down and start coding together. And that's basically what we did. So to tell you the story quickly, we originally sat in a windowless closet basically above a beer hall and specifically above the kitchen. And I know that because the smell of bratwurst would become quite pungent around 3PM when they started cooking. But we really built the initial LP platform even as it exists today in a handful of months just sitting there together and again really coding. And at that time, we felt confident we had built something that was sufficiently sophisticated to actually bring to market. So we decided to formally incorporate and put it online. And sadly, when we launched it officially, we had fewer visits to the website than there are even immediate members of my family. So not even my... All my brothers and sisters deemed it important enough to visit. And that was when we realized, okay, scaling a SaaS company takes a heck of a lot more, namely actually going out and selling it. And that was an important early lesson.
00:15:24 · Charlie Tafoya, Solenne Niedercorn
But from there, I would just say in short, we put our heads down and really started grinding for candidly a number of years to really build up the trust in the market and build up against sales motion and everything else that goes into selling software in this space. I'm gonna have to send them this episode because hopefully they'll love the shout out. But our first client was Pacific Life. And I think what's notable is that they were the first client of Investran back in the day, became the main accounting system actually in the private equity space for many years. So yeah, doing something right out there in Newport Beach. But we're very grateful to their early trust. In fact, they gave us some of our really great early feedback that still informs our product to this day. And they've been one of many great partners and clients that we've managed to build over time, noting that today we actually manage over $20,000,000,000,000 in gross assets on the platforms in very significant scale and candidly a bit of a who's who on the LP and GP side around clients and quality clients at that. [00:16:26] S1: I guess you went to bigger and bigger clients, correct? So what was your strategy in the beginning and how it happened in real life?
00:16:34 · Charlie Tafoya
[00:16:34] S2: Well, I think Pacific Life certainly has a decently sized program. I mean, to the point that they're very large insurer, right? And so regardless of the size of the program, there was a lot of realities that we certainly encountered, which were to say things like information security reviews, vendor and contracting processes, stakeholder buy in, again, all the things that actually go into the sale of software. And so being able to navigate that really set a high bar from the outset around overall process competency. And so as a result, actually our second client was APG, the public scheme of The Netherlands, which is the largest public pension plan in Europe. Again, very sophisticated and candidly complicated process to get that sale done. But that really, I would say, set the tone for how we approach the market. So we had the benefit of really starting at the most sophisticated end ultimately. Can go through a number of names in due course here. But in short, yeah, would say we really benefited from securing many of the very largest LPs as clients and then have worked since that time to expand across the AUM spectrum. I think what's important to note is that we actually started exclusively as a provider to LPs. That was really where we saw the biggest and most immediate disconnect in available technology. But it was in late twenty eighteen heading into 2019 where we saw the opportunity to naturally extend into the GP space. As previously mentioned, very different problem set in that, again, it's more workflow and data collection oriented. But we had already solved for a number of technology complexities around things like the data model itself, as mentioned. We need something that can address basically all forms of investment. The fundamental infrastructure, noting that again in the case of an LP, you're probably talking about tens of thousands of underlying companies, hundreds if not thousands of funds, etcetera. In short, we had to have highly performant tech. And so for us, it was really an opportunity to take in effect the back end, if you will, and build a new front end that was serving a different a different use case, but again, with the same sort of concept in place and again, the same infrastructure in place. But that was that was the approach that we took and, again, had a lot of success out of the gate.