73 Strings' Yann Magnan - unlocking valuation intelligence in private markets

00:28:45 · Yann Magnan, Michael SidgmorePlay from here

[00:28:45] S0: I think my point of view is that it benefits all GPs in the same way. And there is a thing, mean, it's a pretty competitive fundraising environment out there. And what we are seeing from our clients is that first they want to be able to scale. They want to be able to create efficiency. They want to be able to deliver this valuation faster and better. And the other way around, of course, the first thing that matters when you are raising capital is your performance. But the other way around, you don't want processes that are not at par with the best practices out there to be a drag on your fundraising process. That's what we're seeing happening these days. [00:29:23] S1: You see a lot of firms, and you work with a lot of different firms. Are GPs spending more and more resources and effort on their operations team, their finance team that relate to valuations, monitoring, things of that nature. Because not only is it an important function to deliver to their investors, and as you just mentioned, attract new investors and attract the confidence and trust of those new investors, but there's also a ton of data that can be really valuable. I imagine in today's world, especially with AI, there's tons of valuable data to glean from their portfolios if they're using it properly. Are you starting to see more and more GPs pay more and more attention to what they can do with all the data they have and then apply more resources against that opportunity set?

00:30:16 · Yann MagnanPlay from here

[00:30:16] S0: Yeah. We've seen that happen, I would say, in the past one to two years, and it's accelerated massively. So now on our side, started by developing these two valuation platforms that was referring to equity instruments, private equity, venture capital, growth equity, infrastructure, and private credit. And we, at some point in time in our journey, we decided that we needed a way to ingest portfolio company data into the platform in the easiest way possible. We developed an AI based extraction technology that operates at close to not 100%, it's not possible, but way enough of 95% accuracy. And again, the play on our side and what we wanted to do is facilitate the ingestion of portfolio company data into our platform. Fast forward three years later, what we are seeing is that this extraction technology is used to feed into our valuation platform, but is more and more used as well by our clients to basically collect each and every data point that they receive from their portfolio companies in board decks, in Excel templates, quarterly reports, compliance certificate, everything that they can. Because the starting point of a thought process today is that you can create massive intelligence and insights when you're a GP from the portfolio company data that you're getting and the time series of this data as well. That's very, very important. It's not static data. You can create massive insights and intelligence, especially if you can leverage GNI and LLM on top of that. But here is the thing is that if you don't get the data in the right spot, meaning structured, consistently, standardized, consistently labeled across the board, then you're missing a lot of these insights. If you have one company that reports revenue in your portfolio company as revenue, another one that reports revenue as net sales, you can't do much with that. Even the best LLM is just going to garbage in, garbage out. If you get everything consistent, structured, consistently labeled, that's when you can get massive intelligence out of the portfolio company data.

00:32:21 · Michael Sidgmore, Yann MagnanPlay from here

[00:32:21] S1: What will it take for GPs to have that level of standardization and uniformity and labeling of their data? [00:32:29] S0: It takes to create a consistent data model in the background, which is what we did on our side because we had to feed into our valuation model. It's all about defining what is the data model that you want to operate in and create that extraction technology and that transformation technology that will take the data that you get, transform that into your own data model so that you have something that is consistently structured. [00:32:52] S1: On that point, how much can data be a moat for these firms? [00:32:56] S0: I don't know if I would call that a moat, but definitely it's the DNA and DNA is very critical for anyone, any firm out there. So, it's definitely something that you should be able to do a lot with, again, if you have that in the right place. [00:33:14] S1: What do you think the next step is for GPs to take the data they have and enrich it with insights, analytics? Do you see things like they're able to find an inflection point in a business faster and then lean in and put more capital into a business? Are they able to benchmark their companies relative to their other portfolio companies and share that with all their portfolio companies so they can see where they stand and what they need to do and what best practices or best in class looks like? Are you seeing a lot of those types of things happen?

00:33:14 · Michael Sidgmore, Yann MagnanPlay from here

[00:33:14] S1: What do you think the next step is for GPs to take the data they have and enrich it with insights, analytics? Do you see things like they're able to find an inflection point in a business faster and then lean in and put more capital into a business? Are they able to benchmark their companies relative to their other portfolio companies and share that with all their portfolio companies so they can see where they stand and what they need to do and what best practices or best in class looks like? Are you seeing a lot of those types of things happen? [00:33:46] S0: Yes, I would say that it's still early days. We are still in the very early days, but I would say that things are moving really, really fast as well. The other thing that I would say is that if you're a GP that manages hundreds of portfolio companies, the intelligence that you'll get out of this 100 portfolio companies is going to be much bigger than if you're a GP even managing billions, but that has only 20, 25 portfolio companies. Because at the end of the day, it's a lot about the stats and being able to correlate things. It's just like... It's a question of big numbers. [00:34:20] S1: That would portend to from an investment perspective, and not to say that the low mid market or smaller funds don't have a reason to exist. They absolutely do have a reason to exist, particularly if they're specialists in their category. But scale really matters here. The more scale you have, the more data you have. And if you use that data properly, there's probably a lot you can do with it.

00:34:20 · Michael Sidgmore, Yann MagnanPlay from here

[00:34:20] S1: That would portend to from an investment perspective, and not to say that the low mid market or smaller funds don't have a reason to exist. They absolutely do have a reason to exist, particularly if they're specialists in their category. But scale really matters here. The more scale you have, the more data you have. And if you use that data properly, there's probably a lot you can do with it. [00:34:42] S0: 100%. That being said, I would certainly not discard smaller managers as well. As you know fairly well, a lot of these smaller managers, they might not have as much data, but they have certain human expertise. They've done the same deals multiple times, and that has also massive value. Combining both is really great. On that [00:35:04] S1: point too, and this gets to the operational side, there's probably a lot of leverage that small firms can gain from leveraging technology, both post investment like a 73 strings or otherwise, to help them and their processes stay small and nimble, but leverage this technology to help them make their decisions or inform existing portfolio companies, lean into investments, whatever it may be.

00:35:04 · Michael Sidgmore, Yann MagnanPlay from here

[00:35:04] S1: point too, and this gets to the operational side, there's probably a lot of leverage that small firms can gain from leveraging technology, both post investment like a 73 strings or otherwise, to help them and their processes stay small and nimble, but leverage this technology to help them make their decisions or inform existing portfolio companies, lean into investments, whatever it may be. [00:35:28] S0: Yeah. I mean, they can definitely leverage our technology on multiple fronts. You can also imagine that maybe a few quarters, a few months, a few years down the road, you'd be able to enrich your own dataset with other type of datasets that will enable you to, again, create that intelligence of that extra layer of analysis on your own portfolio that will enable you to manage the portfolio better. So that's going to happen as well. You're smaller, you should not be discouraged because you are a smaller a smaller fund not to use technology. It's just like it has a different value. [00:36:03] S1: I I think that's a great segue to the impact of AI because it may benefit both small funds and large funds. What would you say the impact of AI on both of those categories, smaller funds and larger funds, will be? [00:36:21] S0: I think it will be massive. I think it will be massive in terms of the ability to operate, the ability to do things faster or better, the ability to extract, again, insights and intelligence out of all this type of information. Yeah. I think it will be massive.