Welcome to our Blue Margin Expert Insights Series. We're glad you joined us. This series is for private equity, and mid market executives who want to increase enterprise value by using their data more effectively. I'm Blue Margin co-founder Jon Thompson and today I'm speaking with Kison Patel, founder and CEO of dealroom, and M&A Science. M&A Science is a community of forward thinking m&a practitioners on a mission to perfect the M&A practice. They host virtual roundtables, an annual summit, and a popular podcast with over over 50,000 users, which we'll link to in the show notes. under the m&a umbrella the firm hosts dealroom, which is an m&a lifecycle management platform software Academy, which provides M&A training flex talent, a fractional talent service, and firm room.com, a self service virtual data room platform designed to streamline deal management. Their clients include Pfizer, JP Morgan and Deloitte. Kison laid the foundation for M&A science in 2011 with his dealroom software, he later wrote the book, agile M&A, which lays out an agile framework for M&A execution. Prior to both Kison was a member of the Forbes Technology Council, and a partner at Transatlantic Investment and advisory a boutique M&A firm. It's great to have you here today, Kison.
Kison Patel:Nice to have the time to chat with you today, Jon. Yeah, absolutely.
Jon Thompson:So we're gonna bridge the gap between M&A data and private equity throughout. But to begin with, maybe you could give us a first person overview of your career, and how you came to be a leader in the M&A space.
Kison Patel:I'm still figuring that out. Sometimes that was very grassroots to say the least, I came out of a situation and basically getting discharged from my undergrad program for academic deficiency. So you know, at least to say instead of dropping out school, I failed out of school, and wound up working in residential real estate, liked what it was big transactions, but didn't like the intricacies of being more of an emotional sale than a financial sale. Somehow, I wound up meeting two gentlemen that were starting their own little boutique M&A advisory practice. And one of the things that they generated was a lot of leads online, and just wanted some help taking a lot of these opportunities. And that's how I got my foot in the door, worked with that firm for a year, felt that this is easy enough, I can go do it myself, started up a practice and ran it for nine years where from the bottom, worked on small, crappy transactions, and work to working with larger enterprises starting the hospitality space with Kimpton, hotels, extended stay the Kinta and some of the developers of boutique hotels in the Chicago area. And then ended up working in Fig with regional banks like Firstbank Wintrust on buyside acquisition, then also representing small community banks and the sell side. So built that foundation of familiarity with the m&a industry. And the recession happened in 2007. And things got flipped upside down, which those of us no no. And those were, I had an opportunity to pivot careers and ended up doing a tech startup that failed Absolutely, miserably. But as a lot of good, I don't think I could like operating lessons from it. But I was very intrigued in the way software engineers would utilize project management software to develop software, thinking and reflecting on m&a experience. I had the Why isn't there a project management tool for m&a. And that was original inspiration to start a company dealroom in 2012. And that took a whole series of iterations to get it to a point where, you know, we ended up finding our ideal customer with corporate corporates that are serial acquirers, acquiring two or more companies a year. And then over time, we've kept that same r&d process to discover problems build solutions, and I've expanded it into multiple products, educational products, the service offerings and so forth that we have today to be more of a portfolio that can fully be comprehensive to support them in the lifecycle.
Jon Thompson:That's great. I know you're in Europe- is your home base Chicago?
Kison Patel:It is um, Chicago based. I travel a lot and today I'm in Aalborg, Denmark,
Jon Thompson:But you grew up in...
Kison Patel:I gew up in small town, Nebraska from the time I was about three years old.
Jon Thompson:Iant to hear that story sometime. I'm heading to Chicago this weekend. Sorry to miss you. One other sidebar question. I've seen your studio at rivals those of network news, and you've got a huge following. How'd you end up with that studio? What was the evolution there? Just briefly.
Kison Patel:COVID. I remember, I had a studio in the office. And I was so concerned that I was gonna get locked out the office that I took this condo is like an extra guest suite to entertain folks and whatnot. And I started setting up a podcast studio over there. But it became this, this COVID lockdown project. And I kept adding on adding on and adding on and buying all this stuff from Amazon, returning 80% of it keeping the good and built a pretty elaborate podcast studio setup from the time to find a infectious hobby.
Jon Thompson:Yeah, so if you want a really good consultant to build a studio, Kison scout the time for that kind of stuff.
Kison Patel:I'll find the time
Jon Thompson:So I guess my question is, is there really a science be behind M&A or another? Take your book, agile M&A, it adopts an agile methodology to the m&a process? Can you give us a quick take on what's unique about your philosophy and approach to the m&a process,
Kison Patel:Everybody talks about the art of M&A, and I get it, I'm not denying it. There is a lot of art when it comes to M&A. And it's really hard lessons learned, you bump your head on the wall, you learned and you do that from this experience that gives you some unique ways of looking at deals and executing on it. But I think our industry truly lacks the science the lessons learned the real things that we could transfer to others and say, Hey, why don't we standardize this? Why don't we know that these are certain things that you should look out for in a deal, and approaches that you can take to make sure deals go smoother? And there is a strong common basis of that knowledge set that can be developed as a science, so that us as teams, M&A teams can execute on M&A are far greater results building off of this sort of common known practice?
Jon Thompson:And is agile a primary framework and theme throughout? Or is that just one aspect?
Kison Patel:It started with some conversations with a friend at Google or became a friend at Google. And it turned to enough justification to create a book around how companies like Google Atlassian use agile practices. And it evolved into becoming a actual framework to say, hey, why not organize this into a framework so that other corporations can learn these practices and apply it and improve their practice? Hence, agile, a lot of it has commonalities with Agile when you think of software development, because the fundamental challenge is very similar. You are dealing with a situation in your project managing based off a lot of assumptions. And that you want to create short cycles to validate your assumptions, and be able to iteratively deliver value, so that you can produce what the customer wants, at the end of the day in a cost effective, quick way. I think m&a is really similar, because when you buy a company, you don't know anything about it, you may think you do, but he really don't. And you will get really far and actually signed an LOI without actually knowing a lot about that company. So all this ingestion of information through the diligence process, you know, if you can really take on a project management approach to be very iterative, like had these deliverables in not only the diligence report, but just how we're going to integrate this company, how we're going to extract value out of it. That just is another really favorable, it's really similar software development, you run an Agile process, you're much more efficient you're delivering and way better results than the old school waterfall. And there's agile m&a is on the same track. If you're running an Agile process, you're gonna deliver far better results than the old school waterfall, a lot of the big four sceptic of still using these practices, not to knock on them, we still like them. And there's some good folks in those companies that we do partner with. But, you know, it's an emerging way of thinking about doing deals.
Jon Thompson:Yeah, I can see the the analog to software development, you've got a bunch of developers out there if they're each keeping their own notes on code and coming together and trying to coordinate and don't have a central way to coordinate and build on that that everyone can access. It's impossible with software it doesn't work well is that is that what deal room manages is bringing everyone together and building this ingestion in And gestation of data and this growing analysis and vetting of assumptions so that it's in one place. Is that is that the deal? Yeah,
Kison Patel:That's like the the command and control system for running an m&a deal. You want to have one central place single source of truth. And just something designed for running an m&a process. I've seen way too many deals cobbled up or utilizing Excel, email series of PowerPoints cobbled together to run an m&a process. And it's
Jon Thompson:Sorry to interrupt is that the characteristic not naming any names or any of your partners or anything but just at large? The, I guess, old style, more ad hoc approach? How do you characterize sort of that traditional approach to m&a Is it isn't one, one sort of senior guru with authority that oversees it and gets fed information and consults the Oracle and says let's do this deal is, what does it look like when it when it's not organized and not done in an agile framework?
Kison Patel:It's simple. If they're running it on Excel, that's pretty clear that there's a very old school way of doing M&A. If you're emailing an Excel tracker, back and forth, this is 2023, you should see a therapist and get some help, because that is highly inefficient. And especially now and then we're gonna talk about it. But like all these like BI solutions out there that really allow you to just see around the corner, see what's ahead. And you're still putting stuff in Excel where you don't really capture that. I mean, there's a lot of F manual effort to do that, and you don't do it. But there's also a bunch of activity data in the way you interact and input the data that does not get captured at all that when you do use a product designed to capture that information to know people are working on what what's actually like you you can really get some broad like a bigger peripherial on what's actually going on right now.
Jon Thompson:I'm curious about the market penetration, market reach of DealRoom? Are you guys the dominant platform, can you just give folks a sense of that?
Kison Patel:We started in 2012 and the way we look at the market is a lot of people are familiar with virtual data rooms. And there's a lot of them out there is probably a billion plus market cap size, and very fragmented. We look at the other area is this emerging m&a management platforms that look at the broader lifecycle of m&a, provide capabilities around pipeline management, the due diligence management, integration management, to give you that end to end capability managing, and I'm a process that we see small, like probably 50 million market cap, but growing quickly and taking some of the data room as well, because a lot of it has that similar capability.
Jon Thompson:So that's how you're a pioneer.
Kison Patel:We have a product called FirmRoom that plays in the virtual data room space. And it's actually doing extremely well. It's just positioned to say, hey, here's all these data rooms 80% of market are these really old school data rooms that still bill you incrementally per page, which is something that was constructed in the late 90s, when the vendors would actually show up to your office with scanners and scan banker boxes of documents all day, and then charge you to a page. But now everything's in the cloud. And there's such like a menu, incremental cost on the data storage, that doesn't really make sense to charge for page. So we put a product out there that was flat rate pricing, self service to actually interact with the sales rep to activate the service. So for us, it was a nice spin off because we had the data and technology. We noticed a lot of the boutique firms investment banks, law firms, were looking for more of a data security solution, we were able to carve out spin that solution out, make it into a self service product that needed very little support. In fact, it's dubbed the world's most intuitive virtual data room. It uses the same base code. So we automated that. But that's worked out really well. So it's you know, we have a product that plays in there and the big space in a competitive space. So it's fragmented. But then deal rooms been anchored in the m&a lifecycle management, which we do really well. I'm I'm thinking we're set to take number one spot this next year.
Jon Thompson:Yeah, that's great. While we're on firm room, your virtual data room. Part of our argument or our value proposition to the market is that if you've got clean, reliable data, you're able to ask it any question going into the sale process. The stuff that we do that some of the challenges and intricacies and chaos of of a data room are overcome Do you see that need? Or is the is the perfect data room? Good enough? What's your sense of the overlap? Or, I don't know, competitive conflict between really good BI platform, data, Lake House and good reporting and ability to ask questions, versus the data room at the exit at the sale point?
Kison Patel:I mean, can you bring them together?
Jon Thompson:Yeah, essentially pull the data from the BI system, add it to the data room, along with a bunch of other stuff from quality of earnings, folks, and so on.
Kison Patel:Yeah, I think there's a way to find cleaner ways to start extracting data and putting it into a BI solution, then you've got some interesting value there. I think at least that's the goal is to build a basis, right? We're creating performance metrics. At the end of the day, we have certain things you want to track to know things are going good or gone bad? And where do we need to focus to make things keep going the right direction, and there is the data room gets a lot of data. You know, there's you're saying this based on what you're requesting to you request the right data, you get the right data in there. It is static data, right. But it gives you a starting place. And sometimes you may want to find out as you progress in the deal, how you're going to make that static data set into something dynamic. But I think at least it gets you thinking in the right direction on dialing in your performance metrics.
Jon Thompson:We're seeing still, I think a slower PE deal flow, capital market deal flow. I think, although I've heard some bullish reports recently from a few PE partners, and financings are harder to come by leveraged loan levels are down lower than they were post the 2008. market crashed. Despite some signs this year in q2 of a rebound. I'm curious how your m&a team is approaching this slower season or if that's how you read the current market.
Kison Patel:I would say it varies by industry, I can see a slowdown like Ignite, we would think of a slowdown generally with a lot of the roll ups out there. I think Tech has probably had the more of the screeching halt moment with a lot of activity. But we see in healthcare pretty strong, like healthcare is moving along and have really good multipliers at that as well. So it varies by industry for sure. But it is generally across the board slower. Are your
Jon Thompson:private equity clients looking at ways to maximize the upside of the of the downturn? And if so, how? Or is that not really an area to get into strategically with
Kison Patel:You're always asking people what they're up
Jon Thompson:Yeah the fat and happy versus versus the more them? to. And there is when there's sort of, hey, we're not buying then there are more on through cost cutting initiatives. You know, it's not how do you sort of trim the trees and, you know, optimize or catch up on some of the integration work they got left behind, because there's a lot of value to capture when you integrate proper companies efficiently, we're finding easier to get folks attention and talk to them about their process and pain points and what they can do to improve. So that's that's been a good thing. It's funny in a hot market, you think it's a good thing? It's hard to keep improving the hot market, everybody's reactive. It's all activity versus how do we make things better? hungry and strategic? Yeah, let's talk data for a second. From what I understand part of your sciences, calculating the synergies of a given transaction for your clients, helping him to see economies of scale and proving their position in the market opportunities just cross pollinate client lists and products and so on. What role does data play in that process? As you guys do your work?
Kison Patel:There's data in the process of M&A itself, which, like activity data, for example, can give you a good sense of who's actually doing work is the work getting done, essentially, managing the details and making sure things get acted on. So data gives you insights in the process itself, essentially, who's doing what, which allows you to manage the work getting done, manage the details and make sure things are getting acted on. So in addition to getting that real view of the whole m&a process, and how things are getting done. There's performance metrics we talked about earlier. And we have an investment thesis behind this deal and have identified value drivers. And that's where we want to start shaping these performance. metrics, that's those things iterate over time. And it becomes the driver of the company, when it really gets operationalized with other performance metrics, but if they can shape them around, what are these milestones we need to reach to assure that we're capturing that 10 value of the deal, then that's the other key area of getting some of this this information from the m&a process itself to start shaping with those performance metrics would look like
Jon Thompson:I know you're yours is more of a platform. So whatever their data is, it is but you also have extensive suite of services and professionals helping in these processes, do you help a company clean up their data, augment their data, before going into a transaction, go tell him talk to you or somebody else.
Kison Patel:We got a good tech stack, you know, we do well at providing a tech stack, we have certain expertise in a network that are more of the senior level for that executive leadership on the front end, or how you integrate the company, we have that but in the product itself allows you to capture a lot of data, we build a lot of this into Google's Looker solution. So have built our domain expertise around that specific. But if we look at data, now, there's a lot of inoperability. So it doesn't really matter, like the tool can be here. And we can harvest a lot of m&a data in the product that we have specifically. But it can be mobile into other products. So if there's a company has more competency with Power BI or another solution, they can leverage that data and you know this stuff better than I do, John, this is on your wheelhouse. But yeah, there's a lot you we how collect a lot of this data. And then you know, there's so many ways you can do it. But it just depends on what you're trying to do at the end of the day.
Jon Thompson:What I'm interested in is, you've obviously seen a lot of transactions. And part of the premise of what we do is that the cleaner and more comprehensive your data is and organized. So you can get the answers out quickly facilitates faster sales at higher valuations. And that can't just be done at the 10 yard line. It's got to be done earlier. But having observed a lot of transactions, what is the impact of of data on value on speed of of transaction? Is it? Is it everything? Is it? Is it is that the whole ball of wax? And it's a silly question, or what role do you see it play in terms of facilitating successful transactions?
Kison Patel:I think the clarity and goals is probably the biggest value. It helps you identify like where to focus when you got clear goals for your teams. At the end of the day. I think that's the thing that leads to the biggest issues when goals are unclear. So if you can see, it's not just purely quarterly reporting data, but just understanding how are you going to use that data, being able to prioritize it and distill it down to getting good, those performance metrics. And saying, Alright, here are the key are things we're truly truly aligned. Like, we're not just aligned to a single level for lying down to the level that's going to execute on this stuff, then it'll, it'll make a difference makes a
Jon Thompson:difference. Any cases, you've seen where data or lack thereof, killed a deal or greatly diminished it? You
Kison Patel:know, I almost feel like to me, to be honest, the friction point is the process of getting the data when you're working on a deal and you feel like it's hard and pain in the ass to get the data that starts souring the relationship. And I feel like that's what ends up triggering you not doing the deal. You know, the more access you get, the more you can get your analysis and get comfortable doing the deal. Like things are good, you're happy you're taking care of the you feel comfortable doing the deal. You know, and we generally get along and have a beer together whatever it is. But yeah, I just I feel like that's that's the thing that caused friction points where you don't want to share the data.
Jon Thompson:So waiting a little deeper into the geeky end of the pool. The data lake house and I know there's a lot of buzzwords out there but data lake house really mean something to those that build them. It's it's a model in architecture for a data platform for running a company that we've found to be ideal for speed and scale. When ramping up a company's data capabilities. It's also less expensive than your traditional data warehouse which has been a mainstay for blue margin and our industry counterparts for a long time. And until actually, very recently, there's been a revolution in how efficient it is to build these systems and how scalable and so on. Interestingly, the data lake house, its its greatest potential as a platform for enabling the power of AI analytics, generative analytics is still sort of out for delivery. It's impending, I think it's going to come faster than we think. Microsoft did a pretty impressive demo and may have their copilot product combined with Power BI and just asking things like which of my clients is most likely to churn? What happens if I adjust my pricing in this way or that way, and all sorts of very quick interactions. Generative analytics is analogous to chat GPT, but with your numbers for your company. And it's hard to appreciate the potential if you haven't played with GPT. For for myself, my brother came along one day and said, What is it you're trying to do, and it was a piece of research or an article or something. And he just started typing in loose prose to some machine genius on the other end of GPT, what he wanted and who he was and how he was thinking about it, and it produced a piece of content that was mind boggling. I have since worked with it more and more. And there's the opportunity to have successive prompts where you're digging deeper and deeper on a subject and adjusting the nuance, that same thing is coming to data for accompany the ability to, to to or I guess the potential for that kind of fluid, insight and research and insight and an analysis is is hard to exaggerate. First of all, interim question, are you using chat GPT? Much yourself?
Kison Patel:Yeah, I do. I use it for a handful things like a lot of briefing documents. You know, I actually the other day, I had a draft investment thesis, and you prompt it. So it prompts you back and said, Hey, I'm gonna build this investment. This is for this scenario type of deal. Feel free to ask me any questions you need to get there. And it gives you a good working, because then you from there, you can start editing things. So we find it really useful for a lot of that. And now there's a couple of trips, I took John, I went and took a road trip and outside of Paris in the Champagne region. And I had jet GVT playing the tenor and almost stuck to it to a tee. And then I also did a nice road trip over there and north of Boston along the coastline from Portland, Maine, down the Salem church EBT plan the whole itinerary to the tee. So those travel agents out there, I think they're gonna take a hit pretty soon because I find the better than anything else I've done. It's pretty
Jon Thompson:amazing. And you can actually feed into it. Here's who I am. Here's our company, here's our position in the market. Here's the tone I like to use. Here's an example of my work. Here's generally the types of things I'm trying to communicate just as background. So it can understand that persona. translating that to analytics for a company is your take on this impending generative analytics revolution, that it's it's going to be a disrupter, that it's a gonna have a major impact. Do you have much of an opinion on it? What are your thoughts?
Kison Patel:I acknowledge the fact that our industry moves slow. Like a lot of this stuff we'll see impacted and fast moving industries first, like sales and marketing. And you look at m&a. It's a slow moving, step one, get people off of Excel. Step two, we can look at AI. So I don't see I see a lag, like a 510 year lag for a lot of the cool things you'll see in other industries before it's m&a. So acknowledge that, too. Yeah, there's a lot of sensitivity, like m&a, there's a lot of sensitivity around how data is exchanged and things of that sort. And we've had a lot of legal agreements around that. And there's things how US servers that we have to disclaim the stuff that customers. So ultimately, we really want a lot of the AI activity or the processing to operate in a private instance environment, or it's not a public server. And this stuff is really expensive. So there's sort of that component, but there are solutions in play with all the big cloud service providers to provide this sort of AI stack and that kind of private instance contained environment. I think seeing that is like the data management component, but then there's maturity in the how you train these models and what they actually deliver because it's not just plug and play with the agenda. Well, you know, chi VT API, for example, where you're plugging into general solution, I think there's a lot of specific logic to that applies to emanate that needs to be developed or templates. And then we'll see some interesting things happen from there. I think even when you click into that, though, a lot of hypothetical ways of using AI for m&a, we get pitched to a lot of this stuff to r&d and development cycle is driven through validation of use cases with the customer. And validating the demand so that we have a high level of confidence that if we build something, it will get used. And I think that's it like that, that needs to really get flushed out before you, you start seeing some rubber hit the road with AI in our industry. And PA and too, I'll tell you, the one thing, I'm just putting this out there from all these conversations, in m&a finance world, people in corporate finance, or people are hyper skeptical about AI. More than any industry I know of super skeptical. So that's another big thing to consider. It's, you know, even if just bringing it up, it's it's really very interesting. So I'm very pro AI. But I can tell you like this isn't gonna be isn't gonna be a game changer that soon in the, I think next five years, maybe in the five to 10 year period, it'll have an impact as Contrary to other industries where we're seeing some direct impact, especially those travel planners out there.
Jon Thompson:Yeah, well, you can tolerate some variability when it comes to text. But you can't tolerate much when it comes to finance. So and until we have artificial general intelligence, you won't be able to take you know, a generative AI engine, stick it on top of data room and say, Tell me everything. Because you know, those those generative tools, they're looking at the likelihood of the next word, based on the previous word and previous context and a bunch of stuff, based on a dataset of the entire internet, versus looking at a data set for a specific company, you're not going to say the most likely number after a six as a four. So you've got to have really good semantics to translate that data, something we're working on the generative AI spec, so that companies can take advantage of that early rather than trying to catch up. And for us, it's a little more immediate, because the whole mandates when a company works with us is we need to be able to ask questions and get answers now of our data.
Kison Patel:Have you have you looked at this with like, roll up specifically? Because I would I would sense the more immediate use cases with these roll ups that, like their process gets very templatized. There, it becomes very rinse and repeat. And you could build like templates in training AI to do specific things like, hey, I can distill this diligence summary report because it becomes a template, it's sort of the same things that you're extracting, and summarizing, you know, can I sort of train it to do that as sort of a primary use case?
Jon Thompson:Yeah, I like that. Because we have a lot of really, sort of early adopter nerds around here, we've been looking at every generative analytics tool out there, there's none really, that we feel we can say to clients, you'd be crazy not to get on this. They're still sort of beta. But I like that. That's a good suggestion, especially for a guy who knows how to branch to other areas of, of application within his industry. So appreciate it.
Kison Patel:Well, it's it's I think there's a lot more data there. What it comes down to is you have so much more data to train a model. Like if you had a customer that does 50 100 acquisitions a year and they've like archived a couple of years of data you got automate the shit out of that process. Versus you work with a larger strategic and no two deals are in remotely like it's just impossible build any kind of useful templates there.
Jon Thompson:Yeah, yeah. Yeah. Versus a platform company doing 12 acquisitions a year. Yeah.
Kison Patel:When we say template to like with this AI, it's like if this will do the work, you're just guiding it in a way that it could really do things in a specific way. You can write a full on diligence findings report and you could train it specifically. But you got to have that series of data set. To train it to do that, and that's that's the gap we see now, because otherwise you're gonna get a bunch of generic answers. You can go on chat, GBT asked you about all m&a stuff all day, you get a lot of general responses. But if you wanted to really train it on how to actually do some things, you got to have that that data set to teach training on. And that's the thing. That's who's given that up in m&a today, like nobody,
Jon Thompson:right? Right. Yeah. Interesting. So how do folks engage with you clients? And how can people connect with you? Otherwise, just to reach out
Kison Patel:LinkedIn, follow me on LinkedIn, connect me LinkedIn follower company, we're always on there. Yep,
Jon Thompson:we'll, we'll put those in the show notes and have enjoyed talking again, we've had a few chances in the past, but first time recorded here. And really appreciate your perspective and sort of transparency, actually rah rah honesty about your your process, getting to where you're at, and about the m&a world AI and how it's going to mix there or how long it will be. So very helpful and appreciate your time today, Kison.
Kison Patel:Always fun to talk shop, Jon. Thanks for having me.
Jon Thompson:Yeah, you bet. Thanks. Take care.