Invest with AI
00:09:33 · Kirk McKeown
[00:09:33] S0: You know, it's it's so two things. I always I always let people listen to my calls when I was doing calls. And then the first thing I'd say, listen to my call, and then never run a call like I do, right? Um, because I would I was actually a high frequency trader of calls. I would actually jump on 25 calls in a day, do 10 minute calls, collect anecdotes from 25 people, and then sort of create a portfolio of inputs around that based on sort of the ecosystem. I wouldn't suggest that to a team that was doing these calls. Um, those are usually 45-minute calls that is sort of both, you know, what I would call knowledge and trend, right? At the end of the day, there are two types of checks in my eyes. There's a knowledge check, which is how does Databricks make money, right? And then there's a trend check, which is how are Snowflake and Databricks competing against each other in the SMB market? Right? Knowledge versus trend, right? Um, and the best checks have both. You end up working both of those into the call. But fundamentally, it's really about taking the taking the the consultant on the journey and really building an understanding of where what what their vantage point is in the ecosystem, because that's the most important thing. What can they see from their seat? And then that drives what the most important part of the conversation could be. If you're talking to a paint contractor about the paint supply chain, you don't care about their demand. Like what's happening if they're a paint contractor in San Francisco, all you care about are lead times from Sherwin-Williams and price points, you know, sort of from the distributor. So you got to figure out the scale point in every conversation and then really form and craft and understand the believability or credibility of the person on the phone, right? So there's an optimization around, because that goes into weighting, because that's really important here. I think one of the challenges that you're gonna have with the agentic workflows over time until they get sort of um, you know, sort of richer and more textured, uh, is weighting data points against the person who's telling you the data points, right?
00:11:30 · Brett Caughran
[00:11:30] S1: And and how do you think about the sort of the interaction with the investment team in doing these calls? Because for, you know, sort of using that same same question, companies have sort of this debate resolution cycle where like there's a debate on one element that gets resolved, and then three months later a different debate emerges, right? And so how is a primary research team have did you think about staying interwoven with the investment team? So you're always focused on sort of the key debate that matters in that industry and company at a given time.
00:12:02 · Kirk McKeown
[00:12:02] S0: Yeah, you know, it and actually, you know, over time, you know, sort of at first it was it it was it was it was more of a challenge at you know, at all the shops I worked with, because when you're running sort of longitudinal repeated um, you know, sort of workflows monthly, you're tracking change in trends, you know, sort of uh traffic, ticket, you know, sort of bookings, billings, all of the things you would think are standard in in sort of running a waterfall, right? But then there was always that 20% that needed to be temporal, what was happening in the moment around tariffs or $5 or $6 diesel or all of the different things, right? And so you there's what what what ended up developing in all of these ecosystems is first things first, we always we uh I've always been of the belief, and even at CarbonArc, that you know, sort of uh you need to be a counterparty, not a service provider in these types of businesses. So to be a service provider, it it's and that's a mindset, right? Um, you want to be the expert and you want to show up with um you know something credible and something cohesive and something that is of value to the person on the other side of the phone or the the other side of the table. And so first things first, how do you how do you integrate with investment teams? You create the space where they are the best at what they do and you are the best at what you do. So you need to be better at calls than they are, first order, right? And you need to have a deep fundamental understanding of how the businesses work, not the stocks. And then you stay away from the stocks. Never talk stocks, right? That's their job, right? Know your place in the in the ecosystem, know your place in the value chain, and even if you have a view on a stock, it's none of your business, right? Uh and and sort of deliver the news and be incredibly um self-effacing and uh maniacal about calling calling it out when you were wrong.
00:13:53 · Brett Caughran
[00:13:53] S1: Great uh minimize your wins, maximize your losses. Great advice for the primary research teams uh listening to this uh this pod. Yeah, you had a you had a really interesting front row seat because to some degree what you were doing in 06, 07 was bitter lessened away by alternative data, right? Even back in 08, I was calling Wendy's franchisees and trying to get in touch with 20 and and then sort of fast forward credit card panels. Can you sort of walk us through the sort of the early evolutions of the alternative data sets and how that changed what you were thinking?
00:14:26 · Kirk McKeown
[00:14:26] S0: Oh, yeah. I mean, it was wild, right? So I I was really lucky at Glenview, uh, you know, I had I had this middle office function. I had a great team of people, um, and and Larry was a huge proponent of primary research. So I got the opportunity to work with like early, early data providers that were coming on in like 2010, 2011. RS metrics. We were we were looking at parking lot data. Um, you know, that was the first, that was the first alternative data asset I think I ever looked at was RSmetrics. What year was that roughly? 2010, maybe 2010, yeah. 2011. Yeah. Um and um and then vertical knowledge. Uh and uh if you've ever met those guys, Matt Carpenter, um, they they were they were they had been working with like DOD and everybody else, um, but started to get on Wall Street in 2010, 2011. Um, started to that was more scraping. Um, but you know, those are early days of data. And what was really interesting was uh trying to figure out how to augment um you know some of those data assets into the process at the time. Um, and then sort of obviously saw over the years um, you know, sort of the the evolution of and the need for things like doing calls on comps, you know, understanding retail, right? You know, I I used to I used to do, you know, when I was at you know Glenview, we used to do monthly calls to understand where comps were going to shake out and how, you know, sort of wholesalers were seeing momentum and promotional cadence through the department stores and of the other big players. And then, you know, sort of credit card data started to prolip proliferate with some of the private providers that were going into the funds and then 1010 of the world, the 1010s of the world, and it completely changed how consumer uh due diligence uh and even consumer trading performed over the next decade, right? Like consumer is a completely different space than it used to be from a due diligence perspective and from a research perspective. And I think that's the place where we've seen the most transformation. Um, you know, I do think um channel checks have held up because of some of the reasons CarbonArc started, which is outside of card click, app and geo, um, which are the sort of the four horses of you know what I would call alternative data on Wall Street, they've they've hogged most of the CapEx from many firms.
00:16:32 · Kirk McKeown, Brett Caughran
Um so you largely still have a data richness on 150 tickers, and then there's a whole host of other tickers that still don't have good data. So you still need to do calls. Um, but it has it has evolved um, you know, sort of how people do channel checks in in consumer for sure. [00:16:47] S1: Yeah, yeah. Um yeah, and sort of, you know, like Tom Elning from Tegas always talked about, you know, selling alpha versus selling selling beta. What uh can you sort of walk us through that evolution? Because sort of felt like my rough prior was like those data sets actually were a ticket to alpha for a few years. Oh, absolutely. Like the early the early distribution was very tight. Very tight. Right. And as it broadened over time, they sort of the almost a sort of now casting dynamic happened where those data sets almost set set set an efficient first tick, sort of the day, the day they're released. Um how is how has sort of that use case evolved, do you think, of these data sets on these, specifically on these 150 tickers? Like how are funds using these data sets now versus 2012?