Unlocking New M&A Strategies: Uniting Venture Capital Insights and Public Equities in M&A

22:27There's a lot of science behind conference planning. I'm a big believer in serendipity as well. But with conferences, the more deliberate you can be in terms of these are the 12 people you want to meet in person, and then these are the following 12 people that are on the list that if you don't have time to meet, you want to follow up email. being deliberate actually will produce more results. So I can see how someone can attend a conference and their entire year's pipeline is built if they're very deliberate about it. Plan, prioritize, execute. Got to have a plan for your conference approach. Anything else are we missing in terms of approaches?

22:58There's like the direct, reverse coverage, and then conferences. The product market fit we talked through. I thought that was pretty interesting in terms of analysis. I was wondering, is there like an example? And you don't have to use one that's like specific to your company, but in terms of how you would approach building out a market map and then starting to go through and like how you would typically run through that type of analysis. And I'm trying to get more of the little steps in. Yeah, sure. So market map, I mean, just kind of starting there is market map just means really understanding the landscape and who all the constituents are in a particular ecosystem and the value chain.

23:33So just made up, let's say you're looking at a consumer app company, health app company that you want to invest in. So now you want to think about all of the different players in that ecosystem. So there's the kind of B2C consumer app where someone uses that app to do something, maybe to track the fitness activity. But then you want to also think about the market in terms of all of its partners, where does it get its content from? Maybe its go-to-market is partnership with another meal kit company. And really kind of map out all the different players that touch that company. And then think about which ones will be attractive and a good fit for you to invest or acquire into.

24:08And then once you identify that company, So it's made up an example with a consumer app. To assess product market fit and really take a firsthand point of view and have independent research, what I would do personally is I would download and buy the app. So if you're assessing buying or investing in a company and you're able to use the product, you should use it. So I would buy and download the app. I would go through the entire user onboarding registration system, chronicle everything. So chronicle everything from the friction points in the UX to the trigger points to buying premium features and upgrading.

24:38use the entire product, try out all the features. And at the end of it, ask yourself, is this a good product or not? Is it a good product relative to competing products in the space? Is it a good product relative to the cost? And also be open-minded that the answer may be no, but it also may not be for you. You should really kind of open your mind to who the other user personas and then put yourself in their shoes. Is this product interesting and compelling for them for the price? So you do all that. And then you would want to do that for the top four other apps. So now you're looking at the top five apps in the space.

25:09Do the exact same exercise. Download it, go through the user registration, onboarding, try out the product, use all of its features. And then at the end of this exercise, you would have now taken a look from a firsthand perspective at all top five apps in this particular theme. And then you can create a grid to really compare and contrast all of the features between these five products. And then you come up with your own point of view of which company has really truly nailed product market fit. And then you can even take it even more granular basis or a viewpoint and look at it as product market fit on a feature by feature basis.

25:41And sometimes what you'll see is for a particular app or company, all the value accrues to one or two features that really have nailed product market fit. And then the rest are just nice to have bells and whistles. So that's one way of really digging into a product and having a firsthand point of view. In certain situations, if you're able to use expert networks to really get a lot of insight into surveys or even just firsthand consumers who bought the product and really interview them and really understand what was their path to purchase. And then seeing if that path is consistent with your path and your viewpoint.

26:14And so you can put all that together. And now you can see, you can get a sense of the market map of who all the players are, why the consumer app facing part of the business or sector is interesting for investment. And then double click into that to see, okay, this is the app. How is it good or not? And That's pretty hands-on, in the weeds, a lot of work, but that's your first step. I wouldn't say that's necessarily the first step, but that is a step in the diligence process. When you think about the qualitative piece, of course, we're leaving aside the core quantitative assessments. But if you're thinking about just studying a particular product, that's where I would start, which is you've really got to get the product in your hands and start developing a first-hand point of view.

26:57And even if it's maybe not like a consumer product, it's like a B2B probably sitting through a demo or something like that. It's a similar approach. Yeah, yeah. If it's a B2B product and say it's meant for digital marketers, then if you're not a digital marketer, then you'd certainly want to get a subscription or a trial for your digital marketers at your company and have them try it. Work with them and say, hey, let's review these different products. Okay, so we have the market map. we start honing in on this little segment and we really start doing our diligence on these different products, doing our own product comparison analysis, feature by feature, and getting a sense of strengths and weaknesses between these different products, then what do we do?

27:37That's where I would stop in terms of assessing product market fit. And then there's, of course, you can do diligence until you're blue in the face. There's just a long list. But in terms of just product market fit, I think that'll give you the 80-20 rule. It'll give you kind of the bulk of the value. I go back to those buckets of like, hey, is this the incumbent versus emerging versus a new class? Yeah, yeah. That's right. I want to ask you questions, but I'm trying to keep it relevant to this interview because it's like, that's how it helps our business out. Yeah, yeah. No, no. We can go any which way direction you want.

28:04Well, you know, what's fun is like, I don't have an operational role in our company anymore. So a lot of this stuff that when you talk about strategy, it gets me excited because things that I need to start thinking about. And we're doing some of this stuff right now. Like we're putting together a market map. We're starting to look at the adjacencies around our business and core product. and I think we haven't done this where it's like, all right, let's like really get serious about it. When we do start identifying it, because I think there's this part of where the products sit. I think there's like next part of getting to know people in the company and getting a sense of what the culture is like.

28:34Because I feel like that's something I've come across where I can tell like there's certain companies in our arena or adjacencies that we would work really well with and some that we wouldn't. I don't know if you've put that as a factor as you start looking at companies more seriously. In terms of if you were to acquire a company, how does it relate to, I guess, Jason companies? Yeah, it's almost like the culture fit part when you start getting a sense of, is that something that comes in later for you? Or do you start trying to get a sense of that early on? That's a little bit later on, but it's certainly important in terms of the investment process, which is really thinking about how likely is it that you're able to successfully combine company cultures.

29:09If one culture of a company is really, I'm making up an example, leans into R &D and is really big into a lot of experimentation, which inevitably means a lot of failures. And that's culturally embraced at the company. Typically, what you see is kind of an earlier stage company, sometimes a little bit higher R &D, CapEx, a lot of more staff on engineering product, which may translate into a lower current margin profile. And then you contrast it with another company that maybe could be later stage and really everything is around ROI. Every decision they make is around ROI. Then in that scenario, being very experimental without an ROI to show for it is not as embraced.

29:48And then so it comes down to a question of, okay, if you think you're going to combine both companies, which culture will emerge? Or maybe there's a scenario where you keep the culture separate, both people wear their own t-shirts, or is there kind of meeting in the middle? So those are all things that do come in and play. But at the outset, when you're thinking about just the market landscape, and then the company that you're interested in, how good that company is, when you view it from a lens of product market fit, I would say the culture fit is later stage in the process. That makes sense.

30:15I think that makes sense for a more pragmatic approach. And I'm also reflecting on my own biases, I think, as a founder, where it's, hey, let me go meet the other executives to see if they're an asshole or a douchebag. And then we can go do the deep dive diligence. Yeah, yeah. And that's a good point, right? So some cultures are also a no-jerks culture. And in some cultures, it's if you are extremely high performer, we'll tolerate jerks. So those are all things that you get a feel for when you meet the other company. Yeah, I got to learn from you. I should take more of that pragmatic approach and really mapping the space out and doing the diligence of the product.

30:49Because I think that's actually a better approach, more bottoms up as opposed to top down. Yeah. I'll try your way. We'll compare notes in here in six months. Yeah, yeah. Circle back. Okay. So think of that same theme of unlocking new M &A strategies. I'm going to just think of our space. Right now, it's like all the AI stuff is emerging. And we're incorporating AI capabilities. But now all of a sudden, we're looking at adjacency of contract analysis tools. Given that, it's like a new thing because really that was always a separate box. But now all of a sudden, it seems like there's an opportunity to start converging products and capabilities.

31:23Would you just take an idea like that and really take your same thinking? Like we went through, did the market map. We started looking at all these products. We realized, wow, all this stuff out there is like for really big legal firms. We're analyzing millions of contracts. But there isn't a solution or we see a gap for something smaller, the 1 ,000 contracts in typical M &A. How would you go about using that to open up a new strategy? And then I guess this would take us to that buy, build versus partner analysis. Yeah. So I think what I'm hearing the question is you have a solution for contract reviews.

31:57And it's meant for employees that are a little bit smaller, employees between, you say, up to 1 ,000 or so. while a lot of the incumbent solutions are really meant to serve much larger legal firms. I mean, this is kind of using a real example here. We sell a product, we use it to manage M &A. All this AI capability is sort of leading to contract analysis at the end of the day. You're taking unstructured data and you're structuring it and doing all kinds of cool stuff with it. Then when you start looking at that, that's more of the contract analysis space. When you look at that space, it's really servicing pretty high workflows of contract.

32:29And then you're saying, okay, But there's a big broad space of folks that just want to do contract reviews for 30 contracts or 50 contracts. But there's no offering that really services that. That's really interesting. So where I would start is to really conduct research on that segment of folks that your perception is they don't really have a solution. And so now what's critical mass to you, but maybe your goal should be to talk to 10 to 15 people that are in the buyer's seat that are looking for a solution, but are not buying because they don't think a solution exists. And then now really understanding, okay, what is it that the incumbent solution is not satisfying for you and your needs?

33:07It might be just as simple as cost. It's just too expensive. But it might be other things. They might say, look, we don't need a Ferrari. Just a car works, right? And we're willing to pay for it if it's a quarter of the price. Oh, that's an interesting insight. If you have 12 more interviews and everyone gives you the same consistent message, that can be an idea for a much more toned down, simple, easy to use product at a quarter of the price. And you can start building off that. So I would say step one is really conduct the potential customer research and really get to know what they're thinking is.

33:38One, what is the pain point in terms of the solution offerings currently in the market? And then what their price elasticity or what they'd be willing to pay in terms of a product. I think those two would be good insights for you to come back, think about a little bit more, reflect on it, and then go on to the next step. You really take this venture capital approach when you go through this exercise. And it's interesting how you do it. And then it's let it naturally lend to the business case of doing an acquisition. In acquisitions, oftentimes the companies are a little bit later stage. So the discussion is just completely different versus you're talking to a seed stage company where it's really kind of a conceptual company.

34:17And if it's a concept, then it's okay. Well, what gives you conviction in the concept? I've spoken to 100 customers or potential customers. And they're all saying the same thing, that they have this problem and they're willing to pay this much to solve that problem. That's an interesting insight that could be the kernel to really start a new company. You see how those discussions are less relevant maybe for a later stage company that's doing$50 million in EBITDA. And now you're thinking really about rate of change in terms of financials is really the core discussion. I like it because it helps you keep an eye on your blind spot in terms of where does this company actually sit?

34:54that maybe they are the incumbent and they are getting disrupted. They know that, but you're looking too close at the company versus looking at that whole market and seeing where things are moving and who are the other players and what they're doing. Yeah, that's right. It's interesting point, which is product market fit also is a point in time. So it can be fleeting. You might have product market fit today, but over the next year, if the consumer preferences start shifting, if the marketplace starts shifting where your competitors start more innovating and providing even better products, you actually may lose product market fit.

35:23And that may happen first before it actually starts seeping in and is reflected in your financials. So today things are great. You're growing top line, making up 20 % year over year. And then you in real time start losing your product market fit, given just the factors I mentioned. And then you'll start to see revenue in subsequent months start declining. That's a good point. It's like literally looking long-term, near-term, forecast out from the product view of where the company is heading, as opposed to just those lovely charts that always go up and to the right from financial models. I always up to the right.

35:54They always hit a billion or a hundred million revenue in year five. Absolutely. The bankers, do you ever use bankers on your deals? Yeah, yeah. Bankers are really helpful. We do source and we prioritize proprietary deal. Bankers are helpful and especially for larger deals. So larger to me means a company doing over 20 million a year in EBITDA. But bankers are helpful there. On the buy side or? On the sell side. On the sell side in terms of bringing a steel flow, right? Because nowadays, it's unlikely that a company doing 20 million EBITDA will transact without running a formal process to ensure price discovery.

36:24But we've conducted such extensive research in all the spaces that we compete in. We oftentimes already have a target list of companies that we want to acquire and kind of mapped out. When we do receive an anonymous teaser from a banker, someone on my team, we can scan in a couple of minutes and we'll know who the company is, right? That's always fun. Yeah, it's always fun. And it's important because it gives us a speed advantage. Now, since we go into the process with the pre-research done, with the thesis, with the rationale of why the companies make sense to acquire. In the very first meeting with management team, we can dive right into more strategic topics as opposed to spending that first meeting talking about the product or the business model.

36:58And so we go into a process with a pre-built lead, so to speak. And then as long as we devote the proper resources to that opportunity, we should be able to sustain that lead throughout the process, which increases the odds of us having a successful outcome. I like that. A good approach and good way to leverage those relationships on using representation on the buy side though. It seems like just if your company doing deals over a billion dollars, then you tend to throw a banker on there as like a CYA type of thing. I can't speak directly to that because we typically don't use buy side bankers.

37:28So we do everything in-house. I mean, certain special situations, perhaps if it's a little bit more complex, but the typical buyout 100 % acquisition that we complete, we're doing it all in-house. We'll use, of course, third parties for legal advice or commercial diligence, QV vendors, et cetera. But in terms of an actual buy side banker that's representing us on our behalf in the negotiations, we're doing that in-house. So if I brought you a deal, then it's up to me to go get something on the sell side. There's no buy side search engagement. That's right. All right. Fair enough. What about the public equity piece?

38:02What are some of the things that you've learned from that experience that you use in M &A? So something that I still utilize heavily today in M &A from my time in public equities is the application of Mosaic Theory, which I'll touch on in a second. But before I do, let me point out, a big difference between diligence and M &A and public equities is the sheer amount of information that you can get directly from the management team. So if you're in M &A and you're a strategic or a sponsor, you can create a comprehensive list of information requests, due diligence requests, list of questions, even request access to their dashboards, whether it be Google Analytics or HubSpot.

38:37You send that list over to the company and in a very short amount of time, you get answers to everything. You get written responses, you get call requests to walk through your questions orally. You get a data pack full of Excel files that answers all of your KPI requests. You might even get access to some of the dashboards you requested. And so that's in M &A. That's obviously not how it works in public equities. In public equities, you're actually quite limited in what you get directly from the management team. You're limited to what they file to the SEC. You're limited to what they post to the investor relations section of the website.

39:04And then you're limited to what they say in public forums, whether that's to the press or to investors. Essentially, that spans the Qs, the Ks, earnings releases, presentations, transcripts, non-deal roadshows, investor conferences, and then the occasional interview to CNBC or a trade journal. And that's the extent of it. So as a public equity analyst, to become effective at really getting a much more real-time sense of the company's health and performance, its competitive positioning, the headwinds, tailwinds that it's experiencing itself, but as well as its peers and the industry at large, you have to be very thoughtful in terms of what other pools of data can you research in order to get that view because you're not going to get directly from the company.

39:46And so when I was an equity analyst, my approach was really to think about the full value chain of the target company and all the constituents it touches. That meant for me to research and interview a target company's customers, competitors, partners, suppliers, if applicable, and also to look at alternative data sets. So that could be anything from credit card transaction data, say you're looking at a more consumer-oriented business, to audience data, web traffic from similar web or SNLK, again, if you're looking at a media asset. As an analyst, the act of really taking all those disparate pieces of information and then analyzing them and then putting them together like a mosaic into forming a cogent investment conclusion, the act of doing that is the mosaic theory.

40:27And so that's highly utilized by fundamental equity analysts. But what I've come to find is an M &A is actually quite usable as well in specific situations. And really, the main situation that I apply it to is when you're dealing with a larger company, it's an enterprise where they have a customer who's a public company. They have a competitor who's a public company. They may even have an acquirer or a potential acquirer that's a public company. If that's the case, then it's a prime example for you to utilize the mosaic theory. I'll make up an example. Let's say you're assessing, investing, or acquiring a company, a B2B MarTech company, and they serve large enterprises.

41:04In this case, I would think about three additional categories of companies to really analyze outside of what you get direct from the management team, putting aside the data room and the due diligence topics, breakout sessions you have with management team and senior leadership. The three categories I would think about and analyze would be, the first would be its customers. So in this made up example, let's say there's a large customer and it's a public company. So here what you can do is you'd pour over the financials reports, the transcripts, and really try to glean anything you can into two questions.

41:32One, how likely is it the customer will continue spending on the target company solution? That's number one. Number two, what would be the rate of change? Will they spend more, less, hold the same? And what you'll see in certain situations is management will tell you of the target company that everything's rosy and great. Top line is growing by 30 % year over year. And they're going to continue growing because they're going to increase spend with existing customers. Then when you look at the largest customer who's a public company, again, this is a made up example, you see they're actually declining in revenue the last several quarters, year over year, say 3%.

42:03Moreover, the CFO is guiding to margin expansion. So cost takeout and streamline costs. But now it makes you question, how can two things be true at once? You're saying that the target customer is showing that it's declining top line and it's going to cut costs. Yet CEO and management team of the target company is telling you, no, they're going to actually probably increase spending with us by 30 % a year. Now that may be possible and may be true, but at a minimum, this becomes another point of discussion for you to investigate further with the management team. So that's one category. Second category would be competitors.

42:34So here, if you have a public company that's a competitor to your target company, you can do a peer benchmark exercise. And really what this means is you're putting the P &L side by side, pay particular attention to revenue and margins. So with respect to revenues, what you do is look at the top line growth rate of your target company, do the same for the competitor company, and then do the same for the industry at large, the industry CAGR. This will tell you a couple of things. It'll tell you who the share gainers and losers are based on the growth rates. And then also by looking at the sheer dollar value of revenue capture, you'll get an understanding of where each company is in their customer adoption lifecycle.

43:09And so that's telling on the revenue side. With respect to margins, we would look at both gross and operating. And the key question here to really think about, is there something structurally different between your company that you're assessing and the competitor that should drive a difference in OPEX as a percentage of revenue or OPEX as a percentage of revenue? Say, hypothetically, they might have different go-to markets. One's product-led, one's Salesforce-led. That could be the case. But if that's not the case, then now you can conclude, okay, the operations are actually the same structurally.

43:38Then now you can see if your target company is either over-earning or under-earning relative to the competitor. And if they're under-earning, in our case, sometimes we actually would log that as a source of opportunity where we can address in the value creation plan post-closed. And then lastly, the third category of company is natural acquirers. So here, you're trying to think about your bidding strategy. And you're trying to think, if you are in an auction process, who else are you competing with? And so naturally, you could be competing with a larger company. That's an acquirer. That's a public company.

44:07And you want to think about if they were to compete, what would they offer in terms of price, which gives you an idea of where you need to be competitive. So here, the first question I would really check through is, who's a natural acquirer? Is it a target company? And so the way I think about it is, if the target company provides a product or solution that fills an immediate or clear hole in the larger acquirer's company's product suite, and they've stated publicly they're looking to move in that direction, then I'd classify that company as potentially a natural acquirer. And then from here, you can think about and really pour over again the financials, investor days, and transcripts to really think about anything they state about the M &A strategy.

44:44Where does M &A fall in their capital allocation priorities? How has the company been in the last several years? When they've acquired a company, is there a type of company that they acquire? Is it a certain type of IP or content? Is it a certain type of geography? Is it a certain type of business model? And once you have that sense down, now you can look at valuations. What's the largest deal that they acquired in the past? What's the smallest deal they acquired in the past? is there a sweet spot where you notice a range of prices that clusters around. And then of course, you take all those figures, put in the numerator and relate them to KPIs and financials.

45:14So now you get a multiple on everything from audience, user size, Maudows, depending on the business model, and then put those purchase prices over financials to get multiples over revenue, EBITDA, free cash flow. You run all that through the wash, and now you have a sense of, okay, here is a natural car that you might be competing with in a bid auction? And if so, here's the range of prices you think they'll pay, which will be informative for your bidding strategy. I know that's long-winded, but that in a nutshell is the Mosaic Theory, which is highly commonly used by fundamental equity analysts.

45:43But in certain situations, I found actually very applicable in M &A as well. We got a few different pillars. It sounds like the customers, competitors, potential buyers. Do you do a lot with suppliers or not that much? Sometimes, but it really depends on the business small, right? I think suppliers, if you're creating hard physical goods, I think suppliers is a much more important area topic. But a lot of businesses that we're looking at are very much capital light. A lot of them are digital media assets and software businesses. So less so important, but there are some edge cases where we would look into that.

46:14So optional on the suppliers, but mainly customers, competitors, potential buyers. I like how you go deep on these areas, but it rolls up into this pretty macro view of this business and the whole ecosystem. I think that kind of lends similar to the way we described that venture capital sort of approach and doing that kind of product fit analysis. Yeah, yeah. And I don't think it's necessarily either or because to your point, it kind of rolls up into kind of top-down picture. And in certain situations, I'm sure people can justify starting bottom-up first or starting top-down first. I think there's merits to both approaches in many ways that it's going to cap.

46:48But yeah, that's an approach that works for me. So customers really understand the customers or behavior, the spend on this. The competitors, pure benchmark, sound like a good approach. That way you get a good understanding of where the different competitors are going and where they sit. And then the potential buyers, obviously you can figure out who the natural buyers would be. Then you can get a sense of how inquisitive they are. And that's where I think it's fun for good public companies like yours. You learn so much about M &A, even going through like S1 filings to even see deals they've done before they went public.

47:22Using that information to get a sense of valuation and what others would actually do in the market. That's right. What do you think is the hardest part when someone wants to emulate your strategy? The hardest thing is fusing M &A into the culture of the company. Partly what makes it challenging is it takes an extended period of time, an extended period of time of just repeated reps and sets. Ziff Davis as a company is highly acquisitive. We've completed approximately 40 transactions in the last five years. And we have a very highly functioning, well-oiled machine in terms of the M &A programming team.

47:56So with every closed transaction, we increase efficiencies, we gain learnings, we reinforce this muscle memory. And by continuously reinforcing this muscle memory, what we've done is we've converted or turned M &A as one growth tool into something that's very natural and almost instinctive for the company as opposed to being a tool that's pulled out of the toolbox every other year and has to be dusted off and relearned. And so that allows us really to move with speed which in certain situations becomes a big advantage. It's building your M &A muscle. Precisely. Yeah. Building it and then also just consistently keeping it in shape or reinforcing it.

48:33That's what's going to make it tough for someone to copy what you guys do. That would be my take. I think you're right. I feel like the last few interviews you had something along those lines to say. Yeah. When you switched over to M &A Given your background, you didn't come up through banking. You came up through the public equities and venture capital. What was the hardest part of transitioning? So I've been in M &A for a while now, but I guess when I first joined, where I went to hone my craft was really around becoming more deliberate in terms of how I want to compete in a highly competitive bid auction process.

49:02So in a highly competitive bid auction process, you're competing on a variety of funds. You're competing on price, competing on speed, certainty, ease of transacting. And the only way you can ensure victory is if you offer the highest price. Now, if you don't want to just completely lean on that lever to compete, then the more clear-eyed you are going into a process in terms of how you want to distinguish and differentiate yourself in a process, the more likely you'll have a shot at success. And you can back it up here. Before moving to M &A, my job as a researcher and investor, my core responsibility was to generate investment ideas for the firms I work for.

49:33So that meant I spent all my time analyzing companies, interviewing management teams, conducting channel checks, tracking industries and markets, building models, forecasting where a company would be anywhere from one, three, five years out. All that's still very much important in M &A. But in M &A, there's an added element in certain situations where you're in a competitive bid auction process and you need to be able to compete on those metrics that I mentioned. And I think for our team, over time, we've really leaned into speed and certainty as areas that we really compete and differentiate on.

50:02On speed, I don't need to belay the point. We built a system, a machine that works is repeatable. Without any hubris, we can move as fast, if not faster than any other party. With respect to certainty, something that we do a little bit differently from others, I think, is we actually do a lot of work the initial bid stage. So I know it's common practice for some to almost flippantly throw in an initial bid, IOI, with a wide range, just to get to the next stage and look at the data room, right? That's not our approach. Our approach really is to spend the time doing the work, doing extremely detailed diligence at the initial bid stage, given the information set.

50:35So that way, when we do put in a bid, it's a highly informed bid, given the information set at that stage. Sweet and certainty has really been something that we've been able to differentiate on. And there's been times where we're not the highest bidder and we end up winning the bid for one of those reasons. And then so going back to your question. So today, I expect myself, but also my team to continue going extremely deep on the purely investment analytical perspective, critical thinking there. But I also expect myself and my team to run an extremely tight process and ensure seamless execution.

51:05That way we continue to really lean into speed and certainty as a differentiator when it comes to auction processes. That's what it comes down to. If you can build that as your skill to be able to execute speed, certainty, and put an offer on the table. That's right. We also hear speed and certainty as a combination word, almost as if they're linked, but they're distinct. Speed is how quickly can you complete diligence, get the internal approvals, and then wire the funds. Certainty is how likely is it that you can hold your offer through confirmatory and close when you say you're in close? And so those are two things that we think about as discrete items and we try to be very strong on both.

51:45Yeah, you got to do that. That's what it comes down to. You're actually putting a substantial offer and committing to it. That's a big thing. It's not industries that take that big of a bet, that big of a check. Yeah, staying the obvious, but that's given the information set in stage one. In stage two, if there are new learnings that are contrary to our thesis or belief at stage one, then we'll have to accommodate accordingly. Makes it tough. Yes. Everybody wants to get the offer on the table, but then little do they know. For fun, I got some AI-related questions here. On a scale of one to 10, how excited are you about AI and M &A?

52:23Very excited. On a scale of one to 10, I'm at a nine. So I think AI will transform all of our workflows in M &A. It'll help us surface more insights. It'll drive better long-term decision-making. The reason I'm at a nine and not a 10 is I think we will need to be patient over the next year, maybe a couple, as a lot of these tools get experimented on, refined, and really become more enterprise-grade deployable. I'll give you an example. While certainly possible, I have a hard time seeing that an AI co-pilot or agent becoming better than an associate at updating a financial model or say new information uploaded to the data room or creating a sim from scratch or writing an investment and to do it at a high quality level.

53:04So while certainly possible, I think we're at least a year, maybe a couple years out from that. Are you currently using AI in your practice? We are experimenting with a variety of tools, a lot around increasing efficiencies in our day-to-day workflows. So everything from recording call notes automatically to summarizing, meaning takeaways, next steps, action plans, to creating outlines for new memos. And more recently, looking at AI-powered sourcing tools really to help us better find our next acquisition target. But right now, it's really kind of in that trial and experimentation stage. Haven't really fully integrated any particular tool into our data workflows just yet.

53:41There's a lot out there. We're going through a big hype cycle. So you have to disseminate between generic AI and stuff that's very industry use case oriented. Yeah, I'd love to pick your brain, maybe offline in terms of tools are working well for you. What tasks would you prioritize to apply AI to? AI is going to transform the entire stack of M &A activities, sourcing, diligence, execution, integration. What I'd like to see the most help in the near term is around diligence and specifically within the data room. So I'll give you an example. What I would like to see, what would be very helpful for me is if there's an AI copilot agent that I can deploy into a data room And within two seconds, it can summarize all the key findings in the 100 plus files in the data room.

54:20And it can flag for me all the key risks, classify them as yellow flags, red flags. It can summarize all the key variances between management's forecast and where the company is pacing. It can summarize all the key changes in trend lines between KPIs and financials. And then allow me to interface with the data room really in a natural language manner. So at a minimum, like a chat GPT where it's a chat bot, type in questions and it'll answer it instantly. based on what it knows in the data room. And better yet, even voice activated, or if I could just talk to it and ask a question, that would be incredible.

54:53Just thinking out loud here, what if the AI can actually be an attendee of your internal meetings? Say it's more of a passive or ambient attendee where it's on the side of your Google Meet, it's listening to the conversation that's being held, and it'll actually offer up insights that builds on people's points. And then taking a step further, if it hears a question that it knows it can answer from information in the data room, it'll instantly answer it in the box inside. How many times have we been in an IC meeting where someone asked, are the actuals through April or May? And then there's this pause and you being the one built the model, you're on the shared drive, you download the Excel, and then you open Excel and then Excel crashes.

55:27And now you've lost a minute. Why can't the AI just hear that question and just know from the data room answer on the spot? That would be incredibly powerful. I like it. You got a great vision of AI for the future. It all starts with just thinking about what gives me friction and pain points every day. And now if you can throw an AI at it, then that could be hugely helpful. And you think about what separates AI from humans when it comes to, in this regard, data rooms is AI has perfect recall and it has perfect extraction. Those are two traits that are incredibly natural to build directly into a data room.

56:00Yeah, I completely agree. I'll just share some thoughts because this is what the notes I picked up. This year I've interviewed about 50 customers around AI use cases. The one we're really focused on is legal diligence, mainly doing extraction of some type. Because if you look at any organization, they all have customers, employees, and vendors. There's contracts associated with all of them. But it's so much around extracting key information out of those contracts to be able to do analysis. And then right now we're deploying like a comparative. You got two insurance policies. What are the difference between the two?

56:36if you have a base template you use for employment agreement and you want to check 50 of them and know which ones are different or have some negotiated clauses that are different. That's probably the main use case that's emerging right now, which has been there. There's been a lot of contract analysis tools we talked about earlier, but it's going to just be simplified, so it's just part of the daily workflow. That's why I brought that up. That's why I was interested in that. Yeah. Then there's AI search, just getting really good at search. This is where some of the stuff where you want to get information that's somewhere in the data room.

57:06There is some limitations to that now. Like you just can't put all the information through AI and it just magically gives it to you. It just hallucinates and you don't get really good accurate results. What we're finding is like you take that extraction approach where you take the document, detect what the document is, extract the key information. Then it gives you more of like that RAG. What does the RAG stand for? I keep forgetting. But where they can dig into information that's more specific to what you're looking for and know what information to pull in there to give you more of an accurate result.

57:37There's some work to actually do. That's where, for us, we're really working closely with customers to understand and build around their specific workflow and use cases to get that kind of result. Otherwise, out of the box, no, it doesn't. Yeah, in a way, you're kind of crowdsourcing your product roadmap. What annoys you every day? okay, can our engineering team build a product to address that? Basically, the only thing that gets nuanced, because every industry has all these variants to it, but we're starting to get some fundamentals that here's some building blocks that you can start with, and then you would just start tailoring some of these templates to your firm and just specific approach and they're more yours.

58:16And I think that third level, this is probably talking along the lines with the future view, is like platform AI, that now you have something that continuously learns as you're doing more deals. It's got historic information. You want to know how long does it take HR to run through diligence on a type of deal like this? You'll have that information inside or what the common bottlenecks are going to be in the type of deal you're running. In terms of your predictions on the future of AI and M &A, what are your thoughts there? AI will transform M &A activities really in three phases. So the first phase is automation.

58:51Second phase is insights. Third phase is prediction. In automation, as the name implies, this is really, how can AI give me tools to automate away my lower value-added activities and then give me the tools to increase my efficiency and throughput on higher value-added critical thinking type activities? I think you're starting to see some tools emerge on there, but I think there's still a lot of refining and experimenting that needs to be done. And then the second phase is really around prediction. And this is really how can AI lean into its strength, which is ability to digest and analyze data at a much higher level and throughput than humans can, right?

59:27Really just ingest big data, really leverage its tool in terms or its ability to do pattern matching and pattern recognition at a level that humans can. And then ultimately to surface us, the practitioners, to insights that were either underappreciating or missing altogether. And this kind of goes back to my kind of data room example. And it sounds like your contract review example, which is there's a corpus of information can be structured, unstructured, deploying AI in there for it to organize and really extract out the key insights that help our decision making. And then the third phase is prediction, which I think gets really interesting.

59:59And I'm not sure how far away we are from this stage. But this is really where I'd like to be able to just have the AI tell me, is this a good deal or a bad deal? I want the AI to take a step further and say, based on everything I know about you and the industry, here are the five companies you should go acquire in the next two years. That would be extremely powerful. And the use case would be, if we're thinking about acquiring a company, I'd love to have a platform where I can type in the name of the company. And now the AI can look at every deal in history that looks and smells like the deal that you're about to do.

1:00:28Same industry, same thesis, same target financials, etc. And give me a probability of success score. And really on a variety of metrics. So from how likely is it that you'll achieve your pro forma target financials and synergies? How likely is it that you'll be able to retain management for longer than a year? How likely is it that you'll be able to combine culture successfully? We talked about culture earlier. How likely is it that you'll get your deal through regulatory approval? And give you a probability score. And then there's the headline score, which is for all these reasons stated, is this a good deal or bad deal?

1:00:58And this would be a point of view that's derived from a data-driven, empirically evidence-backed point of view. So this isn't just Spotify recommending your next song. This is actually really data-driven. And then taking a step further, which I think would be really interesting, is what if you can feed the AI everything that you want to know about your company privately, also all public information. And then it also takes external data on your competitors, the industry, where the industry is headed in terms of growth forecast. So now it's not only backwards looking, but it's taking a forward-looking point of view on where the industry is headed and where the puck is going, so to speak.

1:01:32And then it goes back to your stated mission, your stated growth strategy, capital allocation priorities. And it says, taking all of these factors in account, these are the five companies you should go acquire the next two years with all the rationale behind it. That's data back. And just having that list going into a strategic planning session is incredibly powerful because even if you don't take any of the recommendations, just the dialogue that would spark would be invaluable. I'm really fascinated by or just excited by the prospect of that coming soon. If one day I see an announcement about you starting an AI company, I'm not going to be surprised.

1:02:06I've been thinking a lot about the space and how our jobs can be much easier. This is awesome. We'll have to catch up offline and I'd love to just show you some of the AI stuff and what we've been thinking about. Your background is unique. We kind of covered the venture public equities background. And there's a lot of folks that reach out to me that don't come up through banking or private equity. And they're looking to break into M &A or just even advice for first-time practitioners in general. So give me your best. Yeah, sure. My advice for first-time practitioners would be don't underestimate the value of firsthand and qualitative research.

1:02:38For a lot of people in the industry, we can become exclusively focused on financial or quantitative analyses. So building out financial models, forecasting KPIs, running all kinds of sensitivities from return on ad spend to return on investor capital. All that's still very important. But when you augment that with firsthand qualitative research, you achieve two things. One, you get a much more complete picture. with the investment opportunity, more holistic view. And two, you actually identify more risk upfront, which then you can mitigate through a mitigation plan. And so I talked about product market fit earlier, but it's very important where if you're assessing, investing, or acquiring company where you can actually use the product, it's important for you to actually try and buy the product and use it.

1:03:17And that way you develop a firsthand point of view where you actually, at the end of trying the product and all the features, you ask yourself, is this a good product or not for the money? Would you recommend this product to a family member. And again, being open-minded, it may not be for you, but you should think about yourself and other potential user-person owners. That's important. There's also a lot of information that you can get from social networks. So part of my investment process is anytime I'm looking at a company to assess, I'll go on LinkedIn and I'll read everything I can about that company, especially recently.

1:03:44So look at what the employees are posting about the company. Are they positive-leaning? Are they negative-leaning? Read the comments. There's actually a lot of insights in the comment section too. Are those comments positive-leaning? Are they negative leaning. You can also look at job openings of senior leadership roles. For example, if the company you're looking at has a CTO job opening and it's been longer than a year, that may be indicative that the company is having trouble hiring top talent for a tech position. On the flip of that, look at how short tenures can be of senior leadership. So if the CMO, you notice, churns after every year, that could be suggestive of problems at the company in terms of lack of leadership, maybe lack of marketing direction.

1:04:22Those are all kind telling signs. In some of the vein, part of my investment process is to go on Glassdoor and read about reviews, read about what the companies are saying about the employees are saying about the management team. And this will tell you a couple of things. It'll tell you a temperature check in terms of the morale of the company. And then secondly, it'll also allow you to see if the reviews are consistent with the story that the CEO is telling you. So the CEO is telling you, let's say in a made-up example, the company's grown like a weed in the first four years. So everything's been great.

1:04:49Then year five, they hit a growth wall. They had a right size and a win and a riff. And then since then, they've been steadily building the company back, recovering. So going into Glassdoor, even before I read the reviews, I should have a hypothesis that if I sort these reviews chronologically, I would think years one through four, you'll see great reviews. Everything's great. Everything's growing. Then year five, you'd see a lot of negative reviews. People are upset. There was a riff. And then since then, you would expect a modest rebound and uptick in employee reviews in terms of positivity.

1:05:14But then you go into Glassdoor and what you've come to find is years one through five ever as expected. But beyond the RIF point, you've noticed reviews have continued to worsen and worsen. That could be indicative. And that can be a kernel of insight where, okay, maybe the company hasn't won back their employee base. Maybe the morale hasn't actually turned that corner as the CEO is telling you. So then now at a minimum, this is a discussion point that you need to have further with the management team to really reconcile their story with what employees are saying online. So all that to say is when you augment quantitative research with a firsthand hand point of view, qualitative research, it gives you a much more complete picture of the opportunity.

1:05:52It also allows you to walk into any opportunity with eyes wide open in terms of what potential risks or underlying issues are with the company. And if you make this part of your investment process, I think longer term, it should drive better decision-making. Golden advice for this whole podcast. You think so? I'm glad. I think so. I 100 % back it up. I generally agree with guests on the podcast because it's a nice to do. But this one, I do believe, I'm a huge believer in that approach of doing quality research, which is why I do these interviews. This is where I'm going to learn. I can read a bunch of M &A books, but I'm not going to learn nearly as much as talking to active SMEs that are doing deals.

1:06:27It could be almost, I mean, maybe it's not a spicy take, but it's a mild take because, or it can be perceived as that. Because a lot of folks in the industry almost subscribe to the adage of, if you can't quantify it, then it doesn't matter. And this is very much not saying that at all. That actually, when you can marry both sets of assessments, you actually become a more holistic investor. That's a really good view. I got to get better on the quantitative. I feel like data's lied to me a few times. Oh, so you already leaned into the qualitative research, which is really important. More so than the data.

1:06:56I'm always skeptical because I'm like, I don't trust this data. The chart's always going the same direction. This is good. What's the craziest thing you've seen at M &A? I've seen a lot of crazy. I mean, I started my career in the GFC. So a lot of craziness has happened since. The craziest thing in recent memory is the 2021 valuation bubble. And that was a valuation bubble of high growth, unprofitable companies and more speculative areas of the market. So rewinding the clock a bit, 2021. I would describe that market as a year of exuberance. You had markets at all-time highs, NASDAQ all-time highs, number of SPAC issuance, IPOs, all-time highs.

1:07:31And then you also saw really highest in speculative areas of market from crypto to NFTs to meme stock. And you had zero interest rates. So this was a risk on exuberant party for the market. And that risk on exuberance really spilled over into M &A dealmaking as well. And you saw all types of distortions in the market that year where I remember going through a real time questioning, are we in a bubble because of these really extreme things I was seeing in the marketplace? And so I can recall even getting QV for the company, we would be a one month wait list because all these consultancies had a backlog of projects they need to work through.

1:08:06I can recall deal timelines compressed and compressed to the point where it wasn't uncommon to receive a book in a process letter saying, you have two weeks, no supporting model, no data pack, no access to management. Just tell us if you're interested in bidding in two weeks, which is just crazy. You also saw reps and warranty insurance almost become customary. It was great for that industry because it was a blockbuster year for them. But when you're thinking about structure, proposing a holdback was almost dead on arrival. And if you want to be competitive in the auction process, you had to use reps and warranty insurance.

1:08:34And so those are just crazy kind of things you saw in the marketplace. But I'll share a story that I think sums up the frenetic mood of that year quite well, which is a fellow VC shared with me a story that one of his friends, who is a founder and CEO, surprised himself even at how quickly he was able to raise around a venture. He raised it in three days for meeting the VC, which is crazy. But even crazier than that was throughout the process, the VC never even asked him about the margin profile of his company. Just purely invested in him on revenue. his story, and his background, which I think just captures that's the type of mood and craziness we were living through in 2021.

1:09:06That is crazy. What were some of the valuations you were seeing in 2021? You don't have to name any companies, but I'm just curious. It wasn't uncommon for people to think in double-digit revenue asks. Yeah. I think I had a friend that told me he had a term she had 20x revenue and I was like, take it. Yeah, in hindsight, it's 2020, but the justification in that year would be if I'm tripling the business every year, then 20 times revenue is cheap. And then you fast forward and for some companies you now see that, okay, there was a huge pull forward in demand during 2021 or the economy has completely changed, et cetera.

1:09:40But yeah, living through it, there's always ways to defend valuations. Crazy year. Yeah. Yeah. This has been great. I appreciate taking the time helping me become a better M &A scientist. Those of you still with us, love to hear some feedback. You can find me on LinkedIn. I always like to hear from those that can listen through the whole entire podcast because you're a true M &A scientist for doing that. Love to hear from you in terms of feedback, the justice speakers, any other topic ideas. Until next time, here's to the deal.

1:10:22Thank you for taking the time to explore the world of M &A with our podcast. We love hearing feedback. Tag us on a LinkedIn post, add a review on Apple Podcasts. We'd love to hear from you. If you need help standing up an M &A function or optimizing one that you already have, we're here to help. And if we can't help you, we probably know someone that can. You can reach out to me by email, Kisan, K-I-S-O-N, at mascience.com. Or you can text me directly at 312-857-3711. If you just want to keep learning at your own pace, visit mascience.com for a lot more content and resources. That's where you can also subscribe to our newsletter.

1:11:07Again, that's mascience.com. Here's to the deal.

1:11:20views and opinions expressed on M &A science reflect only those individuals and do not reflect the views of any company or entity mentioned or affiliated with any individual this podcast is