Baruch Lev and Feng Gu on Data Driven Mergers and Why Most Deals Fail

Welcome back to Data Driven, the podcast where we explore the big ideas in data science, AI, and all things data engineering. Today, we're diving into the complex world of mergers and acquisitions where data meets corporate strategy and not always in the friendliest way. With us are 2 top tier experts who know this landscape inside and out, Baruch Lev, professor emeritus from NYU, and Phong Gu, professor of

accounting at the University of Buffalo. We're going to unpack why up to 75% of mergers fail and how to spot the ones that succeed. Buckle up. It's data driven insight at its finest. Hello, and welcome to Data Driven, the podcast where we explore the emerging fields of data science, artificial intelligence, and, of course, data engineering. Today, we're gonna talk about a branch of, I guess, applied analytics, where we analyze how mergers and acquisition data, goes through. And with us, we

have 2 esteemed guests today. It's not every day we have 2 guests. So I'm gonna read the bio of 1, and Andy will read the bio of the other guest. With us today is Baruch Lev, a professor emeritus at NYU Stern School of Business, where he taught and conducted research on mergers and acquisitions for decades. He worked formally at UC Berkeley and University of Chicago. His work has been widely cited in academic and professional circles, and with over 63,000 Google Google Scholar

citations. He's a leading authority in corporate finance and valuation. And also with us is Feng Gu. He's a professor of accounting at the University of Buffalo and has extensive experience in analyzing the financial aspects of corporate acquisitions. His research focuses on the economic consequences of corporate decisions and has been published in top tier academic journals. Welcome gents. Thank you. Thank you for having us. Yeah. Thank you for the invitation.

Yeah. No problem. We're we're always great to to to have you here. And and part of our listeners are wondering, hey, I thought this was a data science podcast. And and I would say that if you are having an IT career, not just a data career or any career, you are gonna be impacted at some point along, by a merger and or acquisition. Sure. And I don't have a lot I don't know about you, Andy.

I don't have a lot of fond memories of them all working out. It's always been a change, and, you know, change change is always, brings challenges. Yes. And I'm sure these gentlemen, study those challenges and have a lot to share with our audience, and us. You work for a large company in IT. I own a small boutique consulting company that that provides data engineering and and and similar services. So I'm excited to learn what you got going on. In case someone wants to acquire, my

company. And I'm sure you're keeping an eye on this, Frank, in case someone wants to merge with yours. Well and, again, I wanna be clear. The current company I work for, I joined post IBM acquisition. Right? So all of these horror stories are actually the worst merger I was ever privy to was, as an employee, was, well, I guess I can say it now, the Bankers Trust Deutsche Bank acquisition, which, Deutsche Bank being a German company,

when they passed out and and Bankers Trust was an American company. When they passed out the cards announcing the merger or celebrating the merger, the I speak German, so the English sides called it a merger. The German side used the word Uber Nemen, which means takeover. That's yeah. I know just enough Latin to pick that up. Which was, which I thought was interesting because that's basically what it was. So to when I talk about my

merger horror stories, I'm not talking about where I am now. This is 20 years back. And, the other thing as a customer, when the the the companies I use have merged, I've not really been a happy customer. I think Sirius XM, XM Radio was a much better satellite radio provider than Sirius XM is. And that's just my opinion. That's not the opinion of anyone else. My wife seems to enjoy it, but it is what it is. So what really excited me about this

so before our listeners start, like, what the heck are we gonna talk about? These guys are gonna bring data to the table, and that's why I'm excited to have them there. So I'm gonna get off my soapbox because people don't wanna hear us banging on. They wanna hear you guys.

So starting with the data side, we have probably the largest sample of mergers and acquisitions ever assembled. We have a sample of 40,000 mergers and acquisitions worldwide, spending over the last 40 years. And on this huge sample, we have developed a quite sophisticated statistical model, multivariate statistical model with 43 variables to identify statistically, the attributes, the factors that contribute to success and failure of companies.

Excuse me, of mergers and acquisitions. So, basically, the entire work that we did, which is summarized in the book, of course, is heavily data driven. It's also supported by other study, which are always data, driven large sample studies of specific issues of mergers and acquisitions that, we didn't examine.

So, we combine all of this to a set of of observations and recommendations of why 70 to 75% of all mergers fail fail to achieve sales growth, fail to achieve synergies in in cost of sales efficiencies, failed to maintain the share price of the buying, companies. It's an amazing number that surprises most most people who see it. That that is a large

number, and I'm kinda shocked to learn that. I would have thought that, you know, it would have been on the positive side of that that 5050 mark that, that the mergers and acquisitions succeeded, and there were benefits enjoyed by all. But it sounds like what you're saying is no about 3 quarters of those fail on some or, you know, some or maybe all, of those desired outcomes. Yeah. I'm

actually not surprised. I had heard that statistic before and kind of based on based on my anecdotal kind of personal experience, I think that that sounds reasonable. But the question I have is if if it if the situation is so bad, a lot of questions, How do they how do these companies convince their respective boards to take the buyout? Is it just a, how did how do they pull that off? The way to an acquisition is usually a failure of the acquiring

company. Sales slow down, earnings turn to losses, market share is lost, and everything gets excited, particularly investors who are, of course, losing money and influential investors who have a a big say on company. Directors are are looking, and the call gets out of we have to do something big. And, usually, the something big is a big acquisition. And that's how that's how that's the usual way of getting, to this. Managers, are optimists.

Lots of psychological studies have shown that managers are much above average optimists. Some of them are overoptimists. They may be they may be aware that many most, m and a, fail, but they are convinced that they will make it. And they are convincing their board of directors and sometimes even shareholders to, support it.

Yeah. So the persuasion and the pressure to acquire also come from frequently, investment bankers, financial analysts, and consultants. These people, of course, say, you know, have obtained financial benefits from, completed deals. They always pressure the acquiring company to by pointing out, hey. This is a good deal for you, and we can help you, you know, go through this and clear all the hurdles and everything will work out fine. And, so this is really the

best decision for you to make. They're really play these consultants and investment bankers really play a very important role in convincing, both sides of the acquisition to complete the deal as soon as possible. Gotcha. That sounds like sorry. Go ahead. I just want to say in conclusion, you know, some, m and a proposals are being rejected. Not everything is accepted. Just recently, an Israeli company got, an acquisition proposal from no less than Google for $23,000,000,000. Goodness. After

after consideration, they, they rejected it. So not everything is accepted. But many, many acquisition strongly supported by the CEO are indeed accepted. Well, it sounds like there's financial incentive, for the people around the process for the process to conclude? Because I imagine they don't get paid unless the acquisition goes through. Correct? Yes. And there are

also there are also quite large, incentives for managers for concluding the deal. A recent study showed that, many managers get, acquisition bonuses between $5,15,000,000. Got it. And that's for concluding the deal, not for succeeding, but for just concluding the deal. Wow. And, we have we have in the book, we show statistics, which I've never seen anywhere else, that, serial acquirers, their tenure is 4 to 5 years longer than CEOs that don't acquire or acquire just few companies.

My guess is that, directors are somehow satisfied with very active CEO who try to change the course of the company, let them acquire our companies, and then they give them, more time to to somehow somehow, complete the complete the deal and complete the integration. But I was,

someone someone just recently asked me, what surprised you most? One of the things that surprised me most in researching the book was this 4, 5 year, 10 year edge of serial acquirer CEOs, irrespective of the success of the mergers. Yeah. And this difference of CEO tenure by 4 to 5 years is obtained after we have controlled for other contributors to CEO tenure, like

corporate performance and other important factors. So in other words, our conclusion basically says with everything else equal, if you make a series of acquisitions, your CEO tenure is going to be extended by 4 to 5 years on average, which is really a long, long extension. Acquisitions are almost, tenure insurers or CEOs. So it sounds like the, the, incentives are a little bit lopsided.

Yeah. Definitely are from all sides. As Frank mentioned, the, commission hungry, investment bankers, and consultants benefit from the deal. CEOs benefit from, the deal. The only one who paid the price are the shareholders. And and many times, employees are being fired. Customers, suppliers, suffer. Mergers have an overall effect on the entire economy on the Which I think this. Yeah. Which I think, like, begs the question,

like, if you play this out long enough, more people lose than win. And, like, what's the effect of this in the global economy? Because a lot of during times of uncertainty, a lot of the star performers leave because they're not sure what's gonna happen to them. Yeah. Right? Because usually usually, the the acquiring company tends to keep more of their people. What and and and I think that's probably a different game if, you know, if

a if an £800 gorilla buys a small start up. I think that's one type of dynamic. But if you have kind of these 2 industry titans that buy each other, right, something more akin to Deutsche Bank and Bankers Trust, right, there's probably a lot of because they see each one of them sees sees each other well, one side sees itself as a peer and the other sees it as superior itself as superior. And that's gotta lead to all kinds of weird personal interdynamics.

Yeah. You're perfectly right. I mean, acquisition of large targets relative to the size of the acquiring company are almost, a recipe for failure. We analyze in the book the examples, several years ago of Sprint acquiring Nextel. That's the 3rd and the 5th, at the at the time. The 3rd and the 5th, wireless operators. This was they were about the same size. Sprint was a little larger. This was an unmitigated, disaster, the whole thing. They they completely failed in in, merging the employees.

They even they even kept the separate headquarters of the 2 companies and the separate operating systems. Customers will ask, do you want to join the operating system of Nextel or Sprint? I mean, huge churn, huge desertion of customers, and then the whole thing, collapsed. Yeah. Acquisition of large targets are very, very difficult to integrate. And you indicated most of the reasons, with your example of Deutsche Bank. Right. Right. And I'm a former Nextel customer. Same. And I was not

I think I think the Sprint acquisition could have been worse. But if that's your metric, it could have been worse as from a customer's point of view. Yeah. I I suppose based on the numbers you're telling me, it could have been worse. It sounds like a pretty good pretty soft pretty safe outcome. I'm doing the low bar symbol. If you're watching the video, you could see that. But I'm the bar is down here for could have been worse.

Frank, you asked about how this type of deals may affect employees of the target versus the acquiring company. I I think it's a great question. In the research for this book, we spent a lot of time looking into how acquisition deals may affect, employees. And we did look at, the reaction from the target company's employees, and we find that as soon as the news of mergers acquisition comes out, a growing number

of target company's employees decide to leave the company. And this happened even before the merger, gets completed. So they learn from their experience or maybe from your experience involved in this 2 large bank merger that the target employees always get, you know, relatively unfair share in the post acquisition termination, for the purpose of creating synergies, cost savings, and so on. So on average, mergers acquisitions have not been

friendly to employees. We're documenting one chapter of our books, the loss of job positions on average is about 5 to 7% of the combined entities workforce, which is a significant number. Yeah. You know, it sounds a little low when you put it that way. 5 to 7% doesn't sound like a lot. But I can imagine, you know, in these, you know, in in Frank's Bank, acquisition scenario. Yeah. That's, you know, that's across thousands of employees. Yeah. That can be a large number of

people. Well, there was also the rock stars. You know? Yeah. I don't know how it is now, but back then, you know, Wall Street was very aggressive about getting you know, they would basically go to the top trader at, let's say, BT Bankers Trust, and say, hey. We know you're feeling a bit uncertain now. Why don't you have a conversation with us? Right? And you can you you'll make more you'll make, like, 20% more or twice as much and bring anyone you want over

to. Right? So the I suspect the numbers are actually higher, but the published numbers in terms of layoffs are probably 5 to 7%. But I think the star performers, I think you kinda lose the star performers almost right away. Right? Yeah. Yeah. You're you're perfectly right. That that's what economists call moral hazard, which means the employees employees you lose. It's not

just a matter of numbers. You lose the best employees, those with the best alternative outside, and you are left with those without any or very attractive alternatives. So the degradation of the work workforce is much more serious than just the numbers. Yeah. Yeah. You

know, I have an experience like that too. I I just, for some reason, it escaped me earlier, but I was a manager at Unisys, and I was managing the, the data engineering team. We called it the ETL for extract, transform, and load, team. There were about 40 people who were a combination of full time workers and then an extended collection of subcontractors. And we went through a merger and I'll spill the beans on this one too with Molina Healthcare that was headquartered in, out in California. And

we had some of that. In fact, my my boss who was a director, he was a fantastic example of this, definitely a high performer, published 5 books, a known entity in the data field, and just an excellent, leader in my opinion. In the ramp up to the merger, or it actually was an acquisition. In the ramp up to that, he when he got wind of it, he began putting out feelers, for, you know, making a move to another company. And eventually,

he did. And this was excuse me. His move, him leaving was a huge hit to the company, a huge loss. And he did this months before the deal was concluded, like a full quarter ahead of time. And does that I'm curious. Does that count in the 5 to 7%? Would his leaving count in that, or would you would it be post acquisition? In in some cases, it it is included. In other cases, it it may not be included. It all depends on the relative timing of acquisition announcement versus k. The

fiscal year end. Because as you probably know, companies don't disclose the number of employees all the time. I think right now, they, you know, provide this number once a year in their annual report. So there's always some discrepancy, in the number of in the exact number of employees, you know, between fiscal year end and, the announcement of the acquisition. Gotcha. But on average, it should be, you know, around that number. You mentioned the importance of

losing key talent. Frank also made the key point here. We completely agree with you. Actually, in one of the chapters in your book, we have a graph showing, clear evidence, supporting this effect of losing talent. We document that after the acquisition is completed, 2 to 3 years down the road, there is a clear pattern of declining employee productivity. So that's normally a sign of losing key talent.

You know, you know, you have lost the most important human capital component of your combined workforce, and there's no way, your workforce productivity is gonna be as strong as they used to be. So that's clearly a consequence, on the on the employee side after mergers, acquisitions are completed. So I wanna mention we're recording this on the 18th October 2024, and the book is named the m and a, m ampersand a,

failure trap. And the subtitle is why most mergers and acquisitions fail and how the few succeed. And that book is due out according to Amazon today. They're projecting November 15th. So a little less than a month from now is when that book is due to be available. Is that accurate as far as you know? Yes. Excellent. Now I'm gonna buy the book. So I wanna know more. Thank you. Thank you, William. Yes. We have one say say 1. Yes. We

made it. Make it 2. Your order is going to be the most special one because it's the first one. And, and since you bought the book, you can all, you can also give us, high recommendation. Okay. As for And we'll do that. Yeah. Yeah. Well, both Frank and I, you may not know this, but Frank and I are published. We've written I Frank, you've written a couple. Right? Couple 3? 3. 3. Yeah. Mhmm. And I've been involved either as the sole author or a member of a team for 14.

But I started way before Frank to be fair. That's a great number. Well, it warms my heart to hear smart people say that, but I have to share. I have to share that it has way more to do with insomnia than intelligence. Just just so you know. That's even more incredible. I I recall holding, my my youngest is 17 years old now. But when he was a baby, I did that year. I wrote 2 at the same time. I just wrote chapters in a book on a team, just a few chapters, but I'll never do that again.

And I haven't since. But I was holding him and had, you know, my arm had his head in my arm here and holding the bottle, feeding him at, like, 2 AM. And I'm typing on the laptop with my other hand. True story. That's quite a story. Yeah. This looks like an an amazing book. I've yeah. I'm a data, you know, a data weenie, being a data engineer, and I've worked around financial data of all my career. What we did at Unisys was Medicaid, driven data.

And so you get a lot of finance in there. So we get it you know, we dabbled in that part of it, and there's just so much financial data out there. And I've seen so many ways to analyze it and then ways to, you know, not intentionally, but misanalyze it. You you look at the data, an old story intentionally and intentionally. Well, I imagine there's some

intent. I was trying to be nice, Frank. But I have an old story that I share with data engineers. It's not, you know, it's not a real life story, but it's an analogy of the misapplication of thinking that sometimes goes along with this. It's kind of a, you know, getting the cart before the horse or

miss you know, misunderstanding cause and effect. And the analogy that I use is, if you analyze the altitudes of aircraft in flight, you will find that the altitudes drop as they near an output sorry, an output, an airport, and everybody says, well, duh. And I'm like, so one conclusion you could draw from that is in placing airports, someone did an analysis of this data and said, where the craft are lowest, we'll build an airport there. And we all know that's not

true. You know? But Yeah. Yeah. That happens. That kind of thinking happens a lot in analysis. And I'm wondering if that kind of mistaken analysis, if mistaken cause and effect plays into some of the thinking early on. Is that any of that leading to the 75% failure or failure to achieve result rate? There are lots of studies that are done by particularly done by, consultants, and they are based on,

simple correlations. For example, companies, high on the ranking of, ESG, made it through the COVID disaster better than, others. Gotcha. I, with a group of, other researchers, rather than looking at just the correlation between ESG and success, we used a big model that looked at, that had representation of the industry, other variables there. Turns out that, most of these high up on the ranking of, ESG, were high-tech companies. They were extreme they were extremely successful as we

know, many of them. Yeah. And this, of course, was reflected in share prices and profits and others. And they also had the means

to contribute to the community and do other things that those who rank companies on on ESG like. So this is a this is a clear example in statistics of the missing correlated variable. The variable that that really went in was the industry of, of, this. And and and these these, people who just ran the simple correlation, missed it. That's why we built we built a humongous model of 43 variables that attempts to take everything into account.

And then when when one variable indicates success or failure, for example, in your case of Deutsche Bank, we have a variable of foreign acquisition. This variable comes out after the estimation with a negative coefficient, meaning it detracts. All the all other things equal, it detracts from the acquisition, success. So we can say with with, fair certainty that, this is indeed a contributing factor because we accounted for, for most of the others. Yeah.

Yeah. Brooke is absolutely right about, the special care we take to ensure that we're not just documenting simple correlation. We're actually, you know, the identifying the cause and effect relationship, In most of all performance related variables, we make very careful adjustment for industry average performance, at the same time. So this removes a lot of confounding factors from our analysis and gives us a lot of confidence in the validity of our results.

That makes perfect sense. And I can see, and

you've got the word trap in the title of your book. I can see the trap of, you know, making a correlation, which is a valid thing. It's a valid point in my example about the planes and the airports. It's a valid example. Apparently, you know, what you're sharing with me is you're seeing this, and somebody just picking up and focusing on a single correlation and making that the driving metric. And

that that makes perfect sense. And I as you were explaining that, both of you, I thought of, books I've read about Warren Buffett's, and his partner, and I can't nobody remembers his well, it's Charlie. Charlie Munger. Charlie Munger. Right. Him and Charlie work together, and they look at the fundamentals. And they just over and over again, they just pour through probably all of the things that y'all are recommending, you know, for

people who are interested in a merger or an acquisition. You probably recommended the same stuff. It's, you know, the fundamentals of what makes a business, you know, stable. And as you mentioned, Baruch, about, Deutsche Bank, that foreign acquisitions number, that's not something I would have thought of. But, you know, if it's stored in a data table somewhere, then I'd I'd look at the data, of course. Mhmm. But it's not I'm not a business mind.

I am a I'm an engineer, for better or worse. As someone who lived through it, like, there definitely was a lot of disconnect between American business culture and German business culture. Like, it was a very That makes sense. It was I mean, it was a massive disconnect. You know? Yeah. The joke we had at the time, I think Chrysler was bought by Mercedes or Daimler Group that year.

Daimler. Around that same time. And the joke was, thank God that that happened because we would be the biggest cross Atlantic disaster. You know, everybody was so focused on we were a distant second compared to what's going on there. And that, I mean, if you Chrysler's never really recovered from that. Well, the the joke I heard about that is, you know, how do they pronounce Daimler Chrysler in Germany? And it was they call it Daimler. Yeah. It's slightly Chrysler is slightly

yeah. It's true, though. Like and, you know, one card says take over, and the other side of the card in English says merger. Right? Like, it's it's it's, you know, a lot of people had a good laugh at that, but I mean, there was a lot of truth to that. And also too, like, there's a funny meme going around about this, where it was a professor basically saying a 100% of the people who don't understand the difference between causation and correlation will die.

That's a good meme. Yes. I'll have to dig it up and and reshare it. This was this was, many, many years ago, and I took it to University of Chicago, a statistics course. One of the first example in the first class was, the instructor showing a very high correlation between lung cancer and living in, Arizona. No way. Of course of course, the correlation is there, but that's not the causation. Arizona's weather is very good for the lungs. And that's why lung patients are going to a

result. Oh. So, the causation is exactly the opposite direction than what the correlation seems to show. Yeah. His his next example is that more people die in hospitals than at home, which means that which means that hospitals are extremely dangerous to people. I have to try to avoid try to avoid them. That's those are really good examples. And I I one of the examples I read a long time ago, I was gonna say it was from the it may have been from World War 2, but I'm not a 100% positive of that.

But there were aircraft engaged in combat, and they wanted to reinforce aircraft to make them survive, you know, the engagements better. And since they were pointing out, the bullet holes are showing up in these patterns, and they noticed that, you know, there's some here and there's some that we need to reinforce those areas. And someone thankfully pointed out that, wait, these planes are making it back. We need to put the reinforcement where the where the bullet holes are not. You know? So

yeah. Survivor bias. Right? I think that's That's yeah. Yeah. That's it. That's true. But, yeah, great examples. So you have to be careful with analyzing data, particularly in our case, and that's straight, into the topic of your, of your, podcast. Mhmm. I let I let, Feng briefly describe the many databases sources that we use and converge, to get this kind of a sample and statistical model. Yeah. Yeah. So this is, really, the most important part about how we did our research to write this

book. Everything, as Brooke mentioned earlier, is data driven. Our main conclusions are supported by, you know, analysis using large sample, not just a couple of,

case studies, some anecdotal evidence. No. To reach that level, we pull data from a large number of sources starting from a mainstream mergers acquisition database, which gives a lot of details about both the acquiring company and a target company, the time of the announcement, the terms of the deal, and other interesting details like exactly what the the acquiring company CEO said about, his or her expectations for the forthcoming acquisition and so on. So we use that as the starting point to,

collect as much data as needed. As Brooke mentioned,

you know, we try to avoid simple correlation kind of scenario. So, in addition to industry, level adjustment, we also look at entire history of the acquiring company and the target company, you know, 3 to 5 years before they get to the point of making a deal. Try to understand the circumstances of the acquisition.

And then that is completed by using financial statement data, which is obtained from the company's financial statements, across multiple years, both before the acquisition and after the acquisition. Of course, stock price, information plays a huge role in understanding, both investors' immediate reaction to the acquisition news, and the performance of the combined entity after the acquisition is completed over several years down the road. Not just a couple of

months, not just 1 year. We actually track, 3 to 4 years after the acquisition is completed in order to obtain, a more robust and a consistent view of how the value of the company has been affected by the acquisition, is that value creation or value destruction? Alright. I also mentioned earlier about, you know, employee turnover. You asked you

made a lot of good points about how mergers acquisition may affect, employees, not just everyday employee, but also key talent, of each organization. So we obtained very detailed employee turnover data from a database that is, I think, based on LinkedIn, information. So the original source is LinkedIn, which is probably, the most comprehensive database nowadays on employee turnover, very detailed real time employee turnover, not just, you know, once a quarter, once a year kind of information.

So, we had very detailed, you know, in details a very detailed data on the trend of employee turnover. We look at it month by month to see exactly, how employees decide to stay or leave, once the merger news, comes out. So that gives you a snapshot of, the variety of databases we use, to, you know, conduct our analysis and then to provide our evidence. It's it's really a very,

very comprehensive process. But you mentioned LinkedIn, and, I'm pretty sure the grain of their, to and from dates of employment, That that is a monthly drain that that they store that data in. That's something a data engineer would pick up on. But I I love the way you're describing how you acquired your data and, you know, in that it was a very macro process. You were looking at as many companies as you could

find. I like that part of it. I like the time span that you applied going 3 to 4 years after the merger acquisition occurred. It it really reminds me I mean, I'm more excited about reading the book now because it reminds me of the business books that I learned the most from. And I I won't mention the other books, but there's only a handful of them that take that approach. And I I think it bodes well for the success of your book.

So I'm I'm curious how, if how and if you, encountered data that you either decided was out of bounds? Did you did you have limits on that? Did you run into any data quality issues? Yeah. In some cases, because we require the post acquisition performance information to be available for 3 to 4 years after the acquisition. You know,

some companies don't survive that long. Actually, we have seen cases where the acquiring company later on, became too weak and eventually being acquired by other company. So those cases were probably not fully captured. We also don't have full information on some of the private targets. We don't know everything about their performance, before the acquisition like sales, profitability, and so on. And, of course, these private targets don't even have stock price information. So you

can't see how investors react, the investor of the target company reacts to the news of acquisition. You can't even measure, this frequently used metric called, acquisition premium. You know, in in case of, a publicly traded company acquiring another publicly traded company, you can easily measure this acquisition premium by comparing the stock price of the target before the acquisition use, with the deal, the the the acquisition price that the acquiring company decides to pay.

But in the case of a private target, you really cannot do that because, you know, they don't have stock treated, on the open market. So we had to be creative. Brooke and I developed a measure relating the acquisition price to the sales number of the target, which is actually very useful information because this allows us to get around this private target issue and make the metric much more comparable. And we actually developed a lot of insights from using this, different

measure of acquisition premium. Cool. That's interesting. That's interesting. I like the fact that you take a data driven approach to this. Right? Because you listen to Bloomberg or whatever, they always show the rah rah. Look how great this merger is gonna be. It makes sense in this point of view. And if you're lucky, maybe they'll spend 10 seconds on, like, the detractors of it and things like that.

But, you know, looking at this data all up, like, it it seems that and also think, too, the other thing to double click on is, if it's a private company, it's probably going to be way smaller. So I think a bigger fish eating a smaller fish is less likely to have indigestion, so to speak. Whereas if 2 big fish eat each other, there there's a lot of territorial fighting. Yeah. That's that's exactly, what Brooke mentioned earlier.

Acquisition of a larger target is much more difficult to succeed because the integration process can become very contentious. Fight of egos and, a lot of, you know, emotional issues can get into the way to prevent the integration to be fully successful. Right. That Right. That makes sense. And it it gives me hope as a, you know, as a smaller company that maybe one day someone will come along. And I keep up with a touch of newsletters on this, not

not a lot. I really didn't start looking into it until we started approaching, the 10 year mark. And one of the things that shocked me was the size of of companies. And and when I talk about the size, I mean, how small companies are, revenue wise. I mean, I I saw one newsletter that was talking that a I don't know how big of a segment this is for targets of acquisition, but they were half a1000000 to a1000000 and a half in gross

sales. And that was shocking to me. I was like, I would be thinking they were looking at 10, 20,000,000, you know, size companies. But according to this one newsletter, it was a hot thing, you know, going after companies that that size in revenue. And I was shocked. Can you still hear me? Yeah. I can still hear you. No problem. We you disappeared a little on the video, but Yeah. Because I I I got the phone call. No. I wonder. But if you if you can hear

me, that's that's okay. Yeah. That's good. We can hear you. Strong. Yeah. Yeah. Yeah. So so speaking of small acquisitions, what you said is exactly chewing some specialty sectors. Like, in our book, we mentioned large pharmaceutical companies acquiring much, much smaller, biotech firms in order to beef up their

product pipeline. You know, the smaller size of this target is really misleading, you know, when you mentioned sales because, these are basically start up companies and they focus on developing technology.

Especially if you look at the earnings, many of them don't have profit for decades. But that doesn't mean they're not valuable. We actually have some cases showing that a large pharmaceutical companies are often willing to pay a very high premium to acquire these, startup biotech firms because they see the value there. So, you know, acquisitions coming all color and shades. It's it's a huge phenomenon no matter

what type of industry you look at, not just in tech industries. If you look at some of the highly matured industry like food, energy, Every year, you see large and small deals all the time. So that's that's what really, you know, interest Brooke and I when we decide to, write a book on this topic because it's ubiquitous and affects everybody, not just shareholders, affects

employees. In some cases, affects consumers, customers as well because, you know, a merged company may decide to increase price in order to show, the value of the acquisition. Right? Or decrease their services or downgrade their services. Move one of the levers on the seesaw there. Yeah. Yeah. Yeah. We have we have, on this point, we have a a brief chapter in the book, titled killer acquisitions. And these are the cases.

Yeah. And we give examples. These are the cases in which the acquisition is made, basically, to kill the target in this case too. I've heard of that. Yeah. Yeah. The most the most probably the most the most famous case is Visa, trying to acquire Visa Visa debit, not Visa credit. Visa debit, which has a huge market share. I think they have 70% of, all the all the US market in this case. And here comes, a small start up, which is much more efficient in obtaining data,

linking to customers and things like this. Mhmm. And they, they try to, they try to acquire this company, with the with the clear it was. It it came out in an email from the CEO with a clear intention to basically, terminate the, the product. The whole thing the whole thing was litigated by Department of Justice and then Visa retreated.

But, we quote a study on the pharmaceutical industry, a very, very in-depth, study that, that track the products of the acquired company match with the products of the buying, company, they concluded about 70% of acquisition in the pharmaceutical industry, killer acquisitions. Because if you look after the acquisition, all of a sudden, you see that the product of the of the target disappears. And Gotcha. What are what are regulators' thoughts on that? Like, I imagine that Very, very negative.

Very negative. In this case, of course, of pharmaceuticals, it affects health of people. Right. Yeah. It it harms it harms, innovation. And, this this is this is an interesting chapter. Killer acquisitions. I'm so looking forward on its own. Right? I'm I'm definitely looking forward to this. November 18th, you say? It's 15th. 15th. 15th, 15th. November 15th, according to Amazon. Oh, no. Actually, now it just changed. I am not making this up. 13th is what I'm seeing now. It's nice. Oh,

nice. Okay. 3 to 6. I maybe I misread it before. I thought you said 13, but it says 13 now. That's the, the date given by the publisher. Yeah. So the the book is now being, I believe, produced in the last, phase of production. And then at the end of the month, we'll leave the warehouse. And around 13th November, it will be available for shipping. Yeah. Very cool. And then What I'll do what I'll do is I will put the link to the Amazon, page for

the book. I'll put that on a calendar note and schedule it for, like, 5 AM or something on 13th. And so I can go over and buy it right away. You you you really wanna be the first one to order. I well, I won't order it, but I'll buy it first once it's once it's released. I would I don't do the preorder so often because and what I'll do is I'll check probably on 10th to see if it's available by Kindle because I know sometimes they send those out a little earlier. Oh, yeah. That's true. And, yeah,

I'll grab it then for sure. But, yeah, those If you're going to buy it, what are you going to do with the baby? No. That's true. That's a good thought. I don't I don't think the baby's a baby anymore. No. He's he's driving now. So that's a good good point, though, Baruch. Thanks. Thanks for reminding me. I need to stay on top of that sort of stuff, and I need all the help I could get. Yeah. We think we

ran out of time for questions, but that's fine. I think this was an exciting conversation that I think explains a lot of what we're seeing in in our careers where we we start one company. You're also starting to see a pattern of, you know, let's say Microsoft buying LinkedIn. LinkedIn has largely been left alone. Yeah. Yeah. Yeah. Well, they were doing a lot right to start with. Right. Right. Right.

Right. I think that's that's an interesting thing is that, you know, smart companies, they know if it's if it's big enough and it's doing the right thing on its own Mhmm. Leave them alone. Yeah. That's that's that's the story of Google and YouTube. Yeah. Yeah. Yeah. Yeah. Yeah. I'm here in the country. I I live out in the woods in Virginia, and we say if it ain't broke, don't fix it. Yeah. But on on the other hand May I may I

mention one thing that, didn't come up with, in the discussion? We developed in this book and with about a large chapter to it something which I think is really unique, and that's a a 10 factor

scorecard for acquisitions. Nice. Everyone knows that, lending decisions, credit card decisions, largely made by looking at at, the credit scores of people, we developed, based on the 10 most influential variables of our model, we developed a very easy to use, friendly to use scorecard that, you can you can before the acquisition, you can get a a a the likelihood of success of this acquisition, a percentage, which will indicate the likelihood of success.

I guess that this would be very useful, both to managers in somehow early screening of several acquisition candidates and to, investors who are often asked to vote on acquisitions without any information. Mhmm. So this this, acquisition scope, is something which is, really unique to our book. Yeah. Yeah. And I would say as a entrepreneur who, you know, wouldn't mind somebody sweeping in and acquiring the company, this could help me improve my score. Yes. You know, make me a more

attractive target for acquisition. Yep. You know, I'm not not saying any of my customers listen. I'm not selling. Yeah. But, well, we'll let you know if that happens. But the, but, yeah, I mean, it's a an I think all around, that's just great. And I look again, one more reason to look forward to the book coming out. Awesome. Cool. Well, this has been a great yeah. Yeah. This is great. I'm I'm really glad we got into this, and you've answered a lot of my questions about how acquisitions get

approved, who wins, and who loses. Usually, it's the employees and the customers, and and who wins. And turns out that the people calling the shots are the winners. Funny how that works. I know it technically speaking, it's a correlation. But I see what you did there, Frank. You see what I did there? Where can people find out more about the book? Do you have a does the web the book have a website, or do you guys have LinkedIn or anything? Not yet. Maybe maybe

maybe we should, create it. Okay. Yeah. Amazon Amazon gives, a short description of the book. Okay. Mhmm. And and the endorsement. We have some great endorsement, about this book. And, yeah. But may maybe the first, place to go is really Amazon and get the description of the book. Awesome. It is the m ampersandamanda failure trap. That's Got it. We'll make sure to put a link in the show notes. And anything else, Andy? No, sir.

Alright. Well, with that, well, let's And that's a wrap for today's episode

of data driven. We hope you enjoyed this deep dive into the data behind mergers and acquisitions, whether it's a friendly merger or an Uber name and take over. A huge thank you to our guests, Baruch Lev and Foam Gu, for their fascinating insights. If you've ever wondered why so many mergers fail, now you know data doesn't lie. Be sure to check out their upcoming book, the m and a failure trap, for even more data driven revelations. As always, thanks for tuning

in. Don't forget to subscribe, leave a review, and join us next time for more data centric discussions. Cheers.