Category: Analysis

Disney-Lucasfilm Deal Part VII – Merchandise

(This is Part VII of a multi-part series answering the question: “How Much Money Did Disney Make on the Lucasfilm deal?” Previous sections are here:

Part I: Introduction & “The Time Value of Money Explained”
Appendix: Feature Film Finances Explained!
Part II: Star Wars Movie Revenue So Far
Part III: The Economics of Blockbusters
Part IV: Movie Revenue – Modeling the Scenarios
Part V: The Analysis! Implications, Takeaways and Cautions about Projected Revenue
Part VI: Disney-Lucasfilm Deal – The Television!)

In business school, as I said in my first article in this series, I was super bullish The Walt Disney Company. The Lucasfilm acquisition followed on the heels of the Pixar and Marvel acquisitions—which were already doing well—and at the time ESPN was a cash juggernaut. Strategically, they’d made a series of great decisions.

Still, those moves, while good, weren’t the core reason why Disney has succeeded so much over the last forty or so years. I believed then, and still do now, that Disney is one of the few movie studios that has a business model derived from a distinct competitive advantage. As others have written about, this competitive advantage goes back to drawings by Walt Disney in the 1950s.

Slide57

Basically, while having a great content is at the center of the plan, they develop and reinforce their relationship with customers through everything else. Or, to be cynical they make their money off everything else. Walt Disney created an iconic character in Mickey, then another in Snow White, then another in Cinderella, and so on to start. Then Walt Disney (the person and the company) would monetize the characters through music and books and comics and eventually television. Then they pioneered the concept of theme parks. Michael Eisner took this approach and applied it to home entertainment and acquiring TV networks.

When I was in b-school, I took the famous chart and summarized it in economic terms thusly:

Slide58

This is the simplest description of supply and demand in the marketplace, the core model at the heart of economics. Basically, along any curve, you maximize your price and quantity sold to yield the highest profit. I’ll cover this more when I write an article on “Transaction Business Models Explained!” (the sequel to my two articles on subscriptions) but for movies you basically can only charge the same price per movie ticket, regardless of movie. As a result, to maximize revenue you need to maximize customers, and hence Hollywood makes blockbusters.

Most studios stop there. But not Disney. They aren’t just selling movie tickets, they’re selling merchandise on top of that. And then, for the piece de resistance, they sell theme park admissions (and in park up-sales) in an experience they own outright. Other studios do this, but nobody does it as well as consistently as Disney.

In my adventures after business school, I’ve only become more convinced that Disney knows its business model, knows its competitive advantage and makes moves to sustain that model. They may be the only movie studio, er, “giant media conglomerate” that has a competitive advantage. To continue our series on Lucasfilm, I’m going to add in the rest of those boxes going up, starting with merchandise.

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Putting Numbers to the Oscar Best Picture and “Popular” Film

If you want to know why I started this website, just take a look at the furor unleashed on the Academy of Motion Pictures Arts and Sciences when it announced (via Twitter!) that it would add a new category called “Achievement in Popular Film”.

First came the questions: “How would this even work? By box office? By user reviews? By top 25 films at the box office?”

Then came the pondering: “Hey, what film would have won in the last ten years? What will happen to Black Panther?!?”

Then came the criticism: “Hey, this won’t work. This won’t solve the problem.” Or summarizing Rob Lowe on Twitter, this will just plain suck.

Throughout all those takes, the data was largely missing from equation. In data’s place lived assumptions. Assumptions from which the rest of the arguments derived. Consider what a flawed world that is: how can we fix something if we don’t know what the problem is? Or worse, when we don’t know what caused the problem?

Well, no more. Let me step into the void with as much data as I can muster to challenge the assumptions permeating the Oscars debate. Let’s separate fact from fiction. Call out our assumptions. Review what we know from what we can only guess. Let’s do this.

But first, my usual warning on data when it comes to media & entertainment.

Warning: We’re in small sample size.

I’m not going to go into as much detail as I did for my series on Mergers & Acquisitions in media and entertainment on my data, but the same admonition that drove that series drives this one: we’re firmly in the realm of small sample size.

Box Office Mojo tracked “Oscar bumps” going back to 1982, so that’s the sample of Best Picture nominees I used. So that’s our starting sample size: 216 films. However, drawing conclusions from 1982 data just seems wrong. Too much has changed since then. From DVDs to going from 200 or so films per year to over 500. As a result, we’ll leverage the last 20 years of data, which is only 136 films, 81 since 2009 and 55 from 1998-2008.

Assumption 1: The Oscars feature fewer and fewer “popular” movies.

Rating: True.

The ostensible reason the Academy needs to make a “popular film” category is because popular films aren’t being included in the nominees for Best Picture. This statement seems obvious which is why so many people said it on podcasts or in articles summarizing the issue. Narratively, this is an easy case to make: In 2017, only two films grossed over $100 million dollars, Dunkirk and Get Out. Worse, the winners in 2016 and 2017 grossed under a $100 million dollars combined, and had a combined box office of $45 million when they were nominated. No films have been nominated since 2014 that we could call a “blockbuster” meaning it did over $250 million at the box office.

(I defined movies as either “popular” with greater than $100 million in domestic box office or “blockbuster” with greater than $250 million. “Popular” and “blockbuster” are my definitions, but they work pretty well.)

The problem with easy narratives is they can often be countered with an equally compelling counter-narrative. If I squint at 2015, it’s hard not to call The Martian a near blockbuster since it did $228 million in domestic box office and more at the international market. I could also point out that La La Land, which was so close to winning it was even announced as the winner, did $400 million in total box office. Or I could just play with the timing: The Oscars don’t have a problem with nominating blockbusters since Star Wars was nominated in 1997, ET in 1982, Beauty and the Beast in 1992, Titanic in 1997 or even Avatar as recently as 2009.

So let’s go to the data. I plotted this a few ways, and they all tell roughly the same story. First, the raw counts:

Slide01Chart 1: Count of “Popular Films” and “Blockbuster Films” in Best Picture Nominees. Data: Box Office Mojo

Even that doesn’t really tell the story right, since the number of films eligible doubled in 2009, as the Academy expanded from 5 films per year to “up to 10”. So here is the percentage of films defined as popular or blockbuster for all films nominated.

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Chart 2: Percentage of “Popular Films” and “Blockbuster Films” in Best Picture Nominees. Data: Box Office Mojo

But even that doesn’t tell the whole story. That’s about one or two movies passing a certain threshold. Arguably, the more important fact is the average box office performance of the nominees. How has that trended?

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Chart 3: Average Domestic Box Office per Best Picture Nominee. Data: Box Office Mojo

Does even that metric tell a misleading story? See, a dollar in box office in 1998 isn’t equal to a dollar in box office in 2017. According to Box Office Mojo, a 1998 ticket only cost $4.69 whereas on average in 2018 that price has jumped to $9.27. So we need to make the three tables above, but adjust for the price of a ticket in a given year. Fortunately, Box Office Mojo does this for us.

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Chart 4: Count of “Popular Films” and “Blockbuster Films” in Best Picture Nominees, in 2018 adjusted dollars. Data: Box Office Mojo

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Chart 5: Percentage of “Popular Films” and “Blockbuster Films” in Best Picture Nominees, in 2018 adjusted dollars. Data: Box Office Mojo

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Chart 6: Average Domestic Box Office per Best Picture Nominee in 2018 adjusted dollars. Data: Box Office Mojo

Accounting for ticket price inflation, everything looks even worse for the Academy. The worst measure is the average box office per film. It was on a downward slide that was only arrested for a two year period, then it has gone back downward. The number of blockbusters per year looks equally bad, as the period from 1998 to 2004 regularly featured blockbusters, then again besides the period from 2009-2014, they haven’t featured any.

Taking those six charts together, we see a narrative forming that in our time period, we’ve seen two slides away from popular films and towards smaller films. Starting in the 2000s, the popularity of the films started dropping, bottoming out in 2005, and staying in that low period through 2008. So in 2009, the Academy expanded the field to 10 films to hopefully get more “popular” films. It worked, and the number of popular films, blockbusters and average box office jumped right back up.

But after this initial surge of popular films and blockbusters, the voters returned to form and the number of popular films, blockbusters and average box office per film plummeted again. To show this, I combined the data of films through these three time periods:

Slide07Of course, the key question for the Academy is how a lack of popular films reflects in TV ratings. (I’d personally argue that ignoring blockbuster films means the Best Picture category isn’t truly representative of the quality of films in a given year, but I can’t quantify that.) So let’s test that next.

Assumption 2: Featuring more “popular” movies will drive TV ratings for the Oscar telecast.

Rating: Maybe, leaning towards true

Again, narratively this is a really seductive argument. Basically, if you feature really popular films, people will tune in to see those films rewarded with nominations and wins at the telecast. Of course, the counter-narrative is also persuasive and I heard it on two different, influential podcasts (The Ringer’s Press Box? and KCRW’s The Business with Kim Masters): the types of people who watch the Academy Awards don’t watch/like popular movies anyways.

So here’s the one the chart that implicitly everyone referenced but I never saw: TV ratings plotted against average box office per film. (I also did the percentage of popular films, but it was even noisier than this line chart.)

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Chart 8: Average Domestic Box Office per Best Picture Nominee in 2018 adjusted dollars versus TV Ratings, in Millions. Data: Box Office Mojo, Nielsen data from Wikipedia.

So what can we draw from this? Honestly, not much. Technically, I should plot this as a regression model using a time series analysis, but I can tell you ahead of time it won’t be statistically significant so I’m not going to introduce that bad data to the world. Instead, the strongest conclusion we can draw is the Oscar telecast peaked in 1998 at 55 million people and have been sliding down ever since.

As for whether popular movies have slowed this decline, you can cherry pick data either way. First, let’s make the “popular movies matter” case. In 2008, ratings hit their nadir at 32 million, just above 2017’s 32.9 million. Each of those years represented near low points in average box office per film. Then in 2009 and 2010 saw increases in the TV ratings, and those two years were the highest average box office since 1998.

I can also make the case that “popular movies don’t matter” pretty easily. 2014 had the highest ratings since 2004—remember this for a minute, I’ll get back to it—and it only featured 1 movie with a box office over $100 million dollars. 2015 saw an increase in the number of popular films and average box office, but ratings still fell from 2014. 2003 had a huge average box office per film, but TV ratings ticked down that year. Moreover, even blockbusters like Titanic weren’t enough to bump up TV ratings, if you look back that far.

How do we draw a conclusion from this? Well, first we admit that one variable like “popularity of films nominated for Best Picture” is just one variable among hundreds. Other variables like the host(s), the date of the telecast, the length of the telecast, the quality of the broadcast, major a-list celebrities nominated and more could all impact TV ratings. Focusing on one variable to explain all our conclusions is a fraught enterprise.

Assumption 3: The Oscars haven’t featured diverse films in the recent past.

Rating: False.

Okay, so I didn’t actually read anyone who wrote this specifically. Or relating it to the move to make “Achievement in Popular Film” a category. But you can’t talk about the Oscars since 2015 without addressing the #OscarsSoWhite controversy.

Arguably, no groups has benefited more from the move from 5 films per year than diverse films and filmmakers. (Except maybe science fiction films, which I’ll get to.) Take a look at the number of films featuring African-American characters or themes measured before and after the expansion in number of films:Slide 9The difference is stark.

In the 9 years since expanding the field, a film featuring African-American characters or themes has been nominated every year except 2015 and 2010, and 2009 featured two, if you count The Blind Side. I debated it since arguably Sandra Bullock is the main character and it is her story, but if we exclude The Blind Side, I’d rule out three of the films from the 2000s for the same reason, which would only make my case stronger. Of the four films in the 11 years before the change that I counted, three are ensembles that don’t really focus on African-American themes. I could easily say that only Ray really qualifies. Before the Academy expanded the field it really ignored African-American characters.

(And obviously I’m only dealing with Best Picture here, not the acting categories, which in a lot of ways was the key driver of the #OscarsSoWhite movement.)

Of course, I could define “diversity” in a variety of ways. Take “global diversity”. Has the expansion of eligible Best Picture nominees helped foreign language films? Not really. From 1998 to 2008, four foreign language films were nominated for Best Picture (Life is Beautiful, Crouching Tiger, Hidden Dragon, Letters from Iwo Jima and Babel) and since then only one film has been nominated (Amour), which is even worse when you consider how many more films are nominated since the expansion.

(I’d also say you could look into Latino-American-themed films, but you’d basically find zero examples of any films in any time period. The Oscars may be so white, but they’re even less Latino-representative than African-American representative. Asian-Americans fair similarly poorly.)

Assumption 4: The Academy wasn’t nominating enough different types of films.

Rating: False, but trending towards true.

One of the biggest topics around this year’s nominees was the bugaboo about Get Out. Specifically, I heard people claiming it was unique because it was a horror movie, and those types of films never get nominated.

Well…

To test this, I wanted to see how many “genre” films were nominated before and after this change. I defined “genre” films as any film in the following genres: science fiction, fantasy, war, musical, comedy or animation. And if a film in Box Office Mojo was more a drama than a fantasy (say, The Curious Case of Benjamin Button) or more a drama then a comedy (Juno was a tough edge case here), then I excluded it. Again, we’re looking for films known as genre films, not films that happen to have genre elements.

Slide10

In this case, nothing much has changed. From 1998-2008, roughly 69% of the films were “drama” films. From 2009 to present, 62% were “drama” films. So it’s gone up by 7%, but that’s within the margin of error. Even the last two years which “felt” like not a lot of genre films were featured still had one-third genre movies.

Of course, for certain categories, the change has really helped. As I mentioned above, since the change 9 clear “science fiction” movies have been nominated: Avatar, District 9, Her, Inception, Gravity, Her, Mad Max, The Martian, Arrival and, last year’s winner, The Shape of Water. Some of those films were also blockbusters or popular. Though the three biggest science fiction films since Avatar (Star Wars) haven’t been nominated. War films have also done very well, but have been generally less popular than the science fiction films, including American Sniper, Hacksaw Ridge and Dunkirk. The other categories all had smaller changes which are more likely noise than genuine signal.

The last category I’d call out is animation. During the first two years when the Academy expanded it’s number of films, two Pixar films achieved Best Picture nominations, Up and Toy Story 3. Arguably, Pixar’s quality the last few years has been at its highest with Inside Out and Coco being frankly, masterpieces (Both were “universal acclaim” on Metacritic.) but it hasn’t seen the respect from the Academy. I would argue that if the Academy really wanted to train a new generation to love movies, putting films like Coco and Inside Out (and even Frozen) would help a lot.

Assumption 6: Politics hurts the Oscars.

Rating: True, but not for the reason you think.

Well, maybe for the reason you think.

The one narrative that was hinted at throughout the #OscarsSoWhite campaign was that featuring non-diverse filmmakers would keep a diversifying America from watching the campaign. As we saw above, though, diverse films have regularly been featured as Best Picture nominees. Instead, the arguably bigger lack of diversity comes from the lack of political diversity in the films.

To explain this, I go back to the biggest discrepancy in the data comparing TV ratings to average box office: why were 2014’s ratings SO high? Again, this year only featured one movie grossing over $100 million dollars, which happened to be its lone blockbuster. So it was an unpopular set of movies that also lacked any major A-list talent. What happened?

American Sniper was the one film.

American Sniper was a major topic on Fox News and other right wing new sites. That’s right, if the Academy is looking honestly at its whole slate of Best Picture nominees, this is pretty much the only film that could be labeled “right leaning” that has been nominated since 2010. (Maybe Zero Dark Thirty too.) The conclusion here is that far from “popular” films, the Academy needs popular films that also appeal to the political-cultural right. (I’m not necessarily recommending that, just acknowledging that this data says.)

Conclusion: A Summary

So here’s my short line summary of the history of the Academy Awards as it relates to popular films and TV ratings.

– By the end of the 1990s, the Oscars featured generally popular films such as Forrest Gump, Titanic, Gladiator and The Sixth Sense, while also featuring critically acclaimed but unpopular films such as Chocolat or The Cider House Rules. 
- Then, from 2000 to 2008, the Oscars featured increasingly fewer popular films, as shown by multiple metrics. The nadir was 2005 to 2008.
– In 2009, the Academy expanded the number of films from 5 to 10 and changed the voting system. As a result, from 2009-2012-ish, the were more popular than the previous five year period.
– Since 2014, the Oscar films have trended downward in popularity, especially among “blockbusters”—films grossing over $250 million—which the Academy hasn’t nominated since 2014.
– The TV ratings have been on a general downward trajectory, though limited evidence (2009, 2010, 2012 and 2014) indicate that popular films can help increase TV ratings.
– The expansion in number of Best Picture nominees helped African-American films/film-makers more than any other category.
– While the films have decreased in popularity, “genre” films have been represented at roughly the same rate. In other words, “dramas”, which tend not to be “popular” have earned about 62-67% of nominations.
– Finally, the biggest discrepancy in the “TV ratings to film popularity” came in 2014, when American Sniper arguably drove the biggest TV ratings in the decade, a film that was the personal favorite of Fox News and its viewers.

I still have a ton of questions to answer on this (some fun, some business) but I think that’s enough for this post.

Debunking the M&A Tidal Wave: Part V – Other Thoughts That Didn’t Make It In

Was I too strong in calling this series, “Debunking the M&A Tidal Wave in Media and Entertainment?

I don’t think so.

I want emphasize the “think” in that previous sentences. I’ve thought about this a lot. I mean, asked myself many, many, many times, “Wait, are you sure there won’t be a tidal wave surge in M&A now that the Justice Department lost in its AT&T battle? Even if M&A has been high the last few years, couldn’t it get higher? Are you implying you think M&A could go down in the future?”

Upon reflection—all that thinking, hat tip to iFanboy—and yes, I think I am appropriate in calling this a debunking. Certain narratives catch hold—coincidentally, like a tidal wave—and most everyone tends to repeat that narrative. The internet started this phenomena, but social media like Facebook and Twitter and Reddit amplify it.

To sum up all I’ve learned, here’s the brief history of M&A in media and entertainment:

– Media and entertainment (and communications/internet) has been consolidating since the 1980s, like all industry.
– After the recovery from the Great Recession, media and entertainment companies began consolidating again.
– By my numbers, the growth in M&A was somewhere between 8%-25% per year in total deal value (depending on the years you pick) and mega-deals (deals over $1 billion in value) increased from 8 in 2011 to 18 in 2017.
– These deals included both horizontal mergers (within the same industry), conglomerations expanding (conglomerates continuing to acquire new businesses) and vertical mergers (within different industries, including distributors such as cable or internet firms acquiring media and entertainment companies).
– Consolidation likely would have continued even if the Justice Department had won their lawsuit preventing the giant and horizontal AT&T-Time Warner Merger.

Phew. Sorry, I had to get that out.

Knowing what we know above, we can use that as our baseline. For any new information on M&A, we can update our priors, to use Nate Silver speak. Basically, if a new deal is announced or a current deal opposed by the government, we have a solid context to understand its impact on the larger M&A in media and entertainment (and communication/distributors/tech/social media) landscape.

For today, any time I dive super deep into a topic, I end up with a bunch of stray thoughts that don’t quite fit in any of my other articles. Today is the catch all round up of those pieces.

Other Thought 1: The Chaos of the Trump Administration

Ignoring the politics of the current administration—and how can you?—what does President Trump and Gary Cohn/Ajit Pai/Wilbur Ross mean for how entertainment companies conduct business? Really, the daily tweets and outrages for liberals or the perceived economic boom times for conservatives matter much less for how he had changed the regulatory environment for business.

But some of those Trump tweets matter. Like how he tweeted opposing the AT&T merger, praising the Disney-21st Century Fox merger and supporting Sinclair/Tribune. In each case, either base political calculations or personal relationships determined his support, not larger idealistic concerns (either free market or pro-consumer). In AT&T’s case, he hates CNN. In Disney’s case, he loves Rupert Murdoch, whose Fox News also supports him. In Sinclair, I think he knows he has a friendly voice supporting his policies.

Uncertainty is the key, along with the certainty that the key is winning Trump by pledging allegiance to him. That’s how companies can win in the short term, while America’s economy moves towards a rent-seeking/crony capitalist future that curtails economic growth. In the mean time, M&A will proceed apace as the key to execution means wooing Trumps favor. Before you decide to do a deal, think, how do we spin this to make Trump look good, while crossing our fingers Democrats don’t punish us for that?

Speaking of decisions…

Other Thought 2: What decisions can we make off this information?

Imagine you’re a major executive at a major studio, communications provider (cable, satellite or telco) or production company. Maybe you’re the boss or the head of his/her corporate strategy or business development team. The key question following a judge’s approval of the AT&T-Time Warner merger is: how should this influence our decision-making going forward?

Now, if you build it, they will come. In business, this means if you build a team with a mission, that team will recommend decisions in its interest. If you have a team devoted to assessing and executing mergers & acquisitions, they’ll probably recommend that you make a lot of mergers & acquisitions. That team—and its likely influential leader—would therefore recommend most CEOs be aggressive in their deal making. Hence, they probably read a lot into the AT&T merger decision as the green light for future mergers.

Ignore that team/leader.

If a merger with a competitor or supplier or other company makes sense economically, it probably made sense no matter which way this decision went. Now, it likely would change the probability on one line of the economic model—the line one the costs if the merger fails—but I would argue that would only apply to deals that almost exactly mimic Comcast-NBCU/AT&T-Time Warner style deals, meaning it would mainly apply to Comcast. And I don’t think Comcast CEO Brian Roberts has any desire to slow down his deal-making.

What about the information I’ve provided showing the huge surge in deal-making going on? Should this influence executives? Maybe.

The case would be strategic. If everyone around you is getting larger, then to continue to be able to negotiate with suppliers or be able to gain a presence in the marketplace, you may need size to compete. If you’re Discovery and Scripps, facing a world with shrinking cable subscribers, doubling the number of channels under negotiations may help keep your affiliate fees higher. Same with movie studios: Disney will be able to negotiate great revenue shares with theaters if they own 50% of the box office, so maybe you need size to compete with that.

But for that strategic case, I could trot out an early version of my “Theme 3: Strategy is Numbers!” At the end of the day, you don’t win in entertainment by simply being the largest player in the world. This isn’t a board game like Risk or Settlers of Cataan where simply being the biggest or lasting the longest means you win. You win by generating cash for your shareholders.

If the deals to get bigger end up costing you more in interest payments than they return in cash, then shareholders will lose money, even with the size. This is usually exacerbated by vertical deals, but all deals risk costing shareholders money. If anything, the frothier the M&A environment, the higher the prices paid, which increase the likelihood that deals don’t make economic sense.
In all, M&A in the larger sphere is interesting, but not determinative on the decisions you need to make as a decision-maker.

Other Thought 3: I’m even more skeptical of conclusions from the M&A data than I was before.

I can’t get over how noisy M&A data is. So noisy.

When you read sweeping conclusions in breathless reports about M&A, remember this. The biggest deals have a huge impact, but are really small in number, while the timing can fluctuate a lot over a given year, which can drastically change the conclusions. a lot, impacting any quarterly or yearly analysis.

The first impact of this uncertainty is to really hinder drawing trends underneath the data. Like say, “Oh look, the majority of deals are about developing innovation” or “achieving economics of scale” or that deals tend to be horizontal or vertical in nature. I’m also hesitant to point to explanations for why M&A is happening, besides that M&A is the natural state of industry in America.

Take the explanation that executives are fearful of the rise of Netflix, Youtube, and Amazon and disruptive business models. That’s a great narrative. But consider that Comcast tried to buy Disney in 2004, did buy NBC-Universal in 2010 and Disney has been buying big new businesses such as Pixar, Marvel, Lucasfilm and Maker Studios. If we tried to quantify this trend, we just couldn’t do it.

The second impact is I tend to look skeptically at any explanation that M&A is increasing year over year based on the most recent data. Again, it just won’t hold up to analytic scrutiny if you can move one or two deals and change the whole picture.

Other Thought 4: Explaining the M&A total number of deals in 2007/2008

In Part IV of this series, I pointed out that in 2007, the number of M&A deals exceeded 1,000. That’s huge, compared to the 800 or so deals we’ve averaged in most of the 2010s. Even though I dismissed this number when making my prediction, it doesn’t mean it doesn’t beg for an explanation.

I have two theories.

Theory 1: 2007 to 2008 was the peak of consolidation of bigger players of smaller mom and pop shops. Basically, this theory says in the 2000s small groups of cable providers, radio stations and independent broadcasters were swallowed up by larger groups. While we focus on the giants of cable, we forget that in the 2000s there were hundreds of cable companies, maybe even thousands. Yet a lot have been swallowed up by the larger players. Now, these deals were sometimes for cable providers as small as a few hundred thousand people, so they just didn’t rate a huge value. Now that they are gone, the number of deals has gone down, but the total value has gone up.

Theory 2: In the 2007 financial crisis, some businesses were divesting not merging. This makes more sense, and I believe one article mentioned this was happening, but honestly I don’t have the data to prove it. If I had Thomson-Reuters data, this is what I would definitely explore.
So I can’t prove either theory above, but they would make for interesting future study.

Other Thought 5: Horizontal versus vertical merger discussions were overblown.

One of the legal hot takes was that the Justice Department had typically ignored vertical mergers and this is what made the move against AT&T so bold. I have two counters.

First, the Justice Department didn’t do a great job of stopping horizontal mergers either.

In my data set, I see a ton of horizontal mergers that went through without scrutiny. In just 2016 and 2017, we saw announced horizontal mergers in broadcast (Sinclair and Tribune), theaters (Cineworld and Regal; Dalian Wanda with two theater chains), radio (Entercom and CBS Radio), conglomerate (Disney and 21st Century Fox), networks (Discovery and Scripps), and other cable/cellular companies, most of which passed scrutiny. So the Justice Department hasn’t done a great job stopping horizontal mergers, which makes the focus on vertical mergers…strange.

Second, the Justice Department has looked skeptical at vertical mergers. Namely, the Comcast-NBCUniversal deal, that it ultimately blessed with many conditions.

Overall, this is one of those data problems where we shouldn’t rely on the sparse data too much. There just aren’t a ton of examples and other factors may explain the conclusions better. Like say size. Many vertical deals just involve really small companies being acquired.

Honestly, just because this vertical deal was successful at trial doesn’t mean future deals will be as well. If cable and satellite companies keep increasing prices after deals clear approval—as both Comcast and AT&T have done—well a future government many decide that these deals aren’t great for consumers.

And wouldn’t that be something.

Debunking the M&A Tidal Wave: Part IV – Making My Predictions

One of the challenges of “big data” is that it is so…big. For any given subject, we have so many ways to measure things. I can pull one set of data to prove my point; you can take the same set of data and pull a different metric to prove your point.

Take gun violence: gun control advocates have their set of data and analysis showing how guns increase homicides, suicides and violence in general. Pro-second amendment folks have their own data proving their own points.

This applies even for something as innocuous as picking TV shows for a streaming platform. In one recommendation I authored, I counted over 1,400 numbers in one powerpoint presentation. How do we figure these issues out with so much data to choose from?

Well, I have a way. It’s unscientific, as far as I know, but it works for me.

Anytime we come across a significant issue with tons of metrics and variables and data, we can employ this method. I call it the “as many measurements as possible” approach, and I’d love to find out there are other more scientific ways to do this. Here’s how it works:

Take as many measurements as possible and determine if they support or nullify the issue under question. If the majority, super-majority or vast majority support the case, then the phenomena is probably real.

The point isn’t to take just one measurement as our gospel but all the measurements we can. If 9 of our 10 metrics indicate that a phenomena is real, then it probably is.

Take global warming/climate change. If you measure temperature, in 95 percent of measurements or ways to measure it, the climate is heating up. Sure a handful of scientists can find one or two ways to show the world isn’t heating up. Meanwhile, 99% of the rest of scientists measure the data in hundreds of different ways from daily highs increasing to the average temperature averaged over the year from city temperature to countryside to oceans and say, “Man, no matter how you measure this, this impact is real”.

Same with gun violence. Guns lead to increased gun homicides and suicides.

Same with stream video: a show that does well in total viewers probably has the most hours viewed and attracted the most new customers and gets the best customer reviews and so on.

I bring this up to put us in the right mind to do our last dive through the data on mergers & acquisitions. I clearly have a hypothesis that a “tidal wave” of M&A isn’t coming because the tide has been coming in for a while now. Like most of the last decade. But now we need the data to really show us what has been going on. As I can see it, we’ve set the terms, reviewed the narratives, gathered the data and now we need to ask the data what it sets. Since we have so much data on M&A and so many different ways to measure it, we could easily pick one or two metrics and have them change our minds. I’d rather apply the “as many measurements as possible” approach: interrogate the data in as many ways as possible and let the overwhelming conclusions, if they exist, be our guide.

So here are our two final questions to answer:

– What is the historic rate of M&A? (Partly answered in Part I.)
– Is that rate increasing, decreasing or can we tell?

The latter question in particular gets to basically the question at the heart of making this prediction. If M&A activity has been steady for most of the last decade, or if it has been increasing, then we should use that knowledge to make our predictions of future M&A activity.

Fortunately, M&A lends itself well to “as many measurements as possible” analysis. M&A can be measured by total number of deals by the size of deals by the types of industry or by percentage of concentration. So let’s look at as many metrics as we can.

Metrics in Opposition

We should start with evidence that M&A has either been low or decreasing over time. And there is one data point that makes this case:Slide11 Read More

Debunking the M&A Tidal Wave – Part III: Reviewing the Data

It never ceases to amaze me how much more there is to learn about this crazy industry. I call myself the “entertainment strategy guy” and things still surprise me. Take M&A (mergers and acquisitions) in entertainment & media.

For years, I thought I closely followed the trends of mergers and acquisitions and all that jazz.

Then, I started to rigorously answer the question from two weeks ago, “How much, if at all, will M&A activity decrease?”. Naturally, I turned to Google to look for big M&A deals. I tried to build myself a little table with every deal I could find. I kept finding deals I’d forgotten about. “Oh yeah, Lionsgate bought Starz!”

There has been a lot more M&A in entertainment then you’d think. It has been a constant flow since the recovery from the great recession. That’s what my unscientific table showed and what high level summaries from PwC (and others) show. And it genuinely surprised me how many deals I’d forgotten about.

Today, I’m going summarize what I saw in the data and the shape of it.

Gathering the Data: Part 1 – My Own Data

Here’s a snapshot of the table I started filling out and will use a bit today.

slide07.jpg

Why build a table myself in Excel? Well, it’s the easiest way to click on a few variables and sort the data to discover descriptive details yourself. One of my pet peeves in data analysis is when someone doesn’t actually own the data themselves, so they rely on someone else to draw conclusions. (Also, sorry for the compressed lines. This table violates my “rule of 8”. Usually tables should never have more than six columns, usually  6 is ideal.)

My process for gathering the data was as crude as it was simple: I googled “entertainment and media mergers and acquisition” and the year to find the biggest deals per year. I later used CrunchBase’s data set to find smaller deals. I would sort by company, starting with the studios and moving to distributors and such.

I really recommend at least trying to collect data yourself whenever possible. It’s harder and takes longer, but by doing it yourself, you force yourself to figure out which variables you want/need per data point. In this case, by looking myself, I learned some thing about M&A activity, and the data set in general. Even when I later switched to using PwC’s summarized data, I could use these insights to understand PwC’s conclusions.

For example, I learned how important the timing of a deal is. A lot of the articles covering M&A activity neglect to mention what they are tracking in their coverage. Is it when a deal was announced? (For many articles, yes.) But what if a deal doesn’t close? So you sort M&A activity by closed deals, but that could be skewed by how long deals take to close versus the year they started in. If you are trying to summarize the previous year’s M&A, well you’d leave out a lot of deals if you only track deals that close.

Could this effect the data? Absolutely. The AT&T-Time Warner could swing one year’s data by $85 billion dollars. The Comcast-NBCU merger swung various year by year totals by $35 billion. The failed Comcast-Time Warner Cable inflated a few years totals by $45+ billion before it was abandoned.

I also learned that trying to distinguish between “acquirer” versus “acquired/target” is touhg. Most deals are usually one company buying another. But sometimes two companies agree to merge, and it isn’t really an acquisition, so who is the acquirer versus the acquired/target? Other times a firm is buying a majority stake in a company it has partial ownership. These little distinctions and difference can plague data analysis when you try to capture them as variables.

What about the deal value? Again, this would seem like a relatively straightforward number, but it can change depending on how stock prices move over time. Or if a company has to raise it’s offer due to competitor or shareholder pressure. Sometimes, the numbers differ by billions, swinging the total deal value by 25% or more. I tried to use the higher number whenever possible. In my scan of the data through news reports, deals rarely got less expensive.

The last five variables were less about the nuts and bolts of the deal (who bought what for how much) and instead about providing some flavor. The pieces I thought would be the most useful for data analysis/business strategy were: the industries involved (network, radio, studio, cable, etc) for both parties, the “direction” (horizontal or vertical) since this came up a lot in the AT&T lawsuit, the status (to account for failed deals), and the stake of ownership. I assumed the last piece was to full ownership unless clarified. Also, in this case industry and direction were my own subjective opinions.

If I could add a piece, I’d add PwC’s description of the business purpose of the deal: consolidation, content, innovation, capabilities extension, or other/stake ownership.

Oh, and in the future I’d include “divestiture” as a final category. Not all deals are accretive and PwC/Thomson Reuter’s database tracks this. In down swings, companies spin off bad business units and ideally a good data set on M&A would tell you when that happens.

Gathering the Data: Part 2 – The PwC Data and Others

As I mentioned above, trying to collect all the information on M&A activity by myself was more time intensive then I thought. Let’s hope I can keep building it through the rest of the year to find additional insights.

In the mean time, I needed a better, quicker look. Fortunately, the good people at PwC using Thomson Reuter’s data were able to compile annual snapshots of M&A activity in the sector they called “media, entertainment, and communications” which I copied in my first post. I found every year’s study I could—in most cases using the articles on it—and compiled it into the table I ran in my last post. Here it is again for this who missed it:

3 Metrics MA Slide to updateI also found other articles about consolidation or M&A activity in other sub-disciplines in entertainment, again, usually through trade press articles. Take this chart from an article in Variety about M&A in TV production, which produced this table using IHS MarkIt data:

slide09.jpg

(Source: Variety/IHS Markit.)

In addition, I found articles about M&A in the Wall Street Journal, Hollywood Reporter and The New York Times. Where possible, I saved the numbers in the article to bolster my data. I’ve tried to provide links where possible, but I have so many I may save them for a future post.

Quality of the Data

So I have essentially two data sets at this point: my own from readings/capturing news articles and the PwC summaries. The question I had to ask myself—and you should be asking me—is how good do we think this data is?

Most people in data analysis miss this key step and it’s worth pausing to emphasize it. Just because you have data doesn’t mean it is any good. Do you see potential flaws that you should acknowledge? Or could cause you to throw out the data set? Do you see quirks in the data that signal bias? Always ask these questions of data (or ask your data scientists/consultants these questions).

From year to year and between data sets, M&A data on media, entertainment and communications (and I assume all industries) is plagued by discrepancies or opinions. The biggest unreliable variable was the timing of M&A deals. Announced deals by definition exceeded the number of deals that invariably closed. So every year’s annual report invariably lowered the previous year’s totals. Sort of like how GDP is invariably adjusted by the Commerce department in future reports. This can make each year seem like it exceeded the previous year’s totals, even if it just means that some announced deals won’t end up closing.

Just because we find flaws or inconsistencies doesn’t mean we have to throw the baby out with the bath water. The question is how much we need precision in this data. Since we’re looking for trends here, being off by a few days on when a deal was announced or closed won’t kill us. Same with being off several hundred million dollars in a price. Given that a few huge deals have the largest swings, being off by a few hundred million dollars won’t effect the larger trends. Even the trends for deals announcing or closing won’t effect the five year average of deals, for the most part. (Though, it helps if you keep you data consistent/apples-to-apples when possible.)

That said, I wouldn’t try to draw too many strong conclusions from the data set, given that it has inconsistencies. And two other issues I’ll discuss in the next section.

My self-made data set has one other HUGE flaw I don’t want to neglect: I made it by trying to find as many deals as possible so I missed a lot of deals. Rigorously reviewing the internet for deals isn’t a super reliable approach, which is why I opted mid-stream to change approaches to focus on high level summaries. I’d also add I mostly focused on US-based M&A, which is a mistake. These are global companies, but our focus naturally falls on places that speak our language. (Many companies had multiple Indian deals, but their total value pales in comparison to US-based deals.)

Initial Thoughts on the Data

So we have all these high level summaries and my table. What do we think of this data? What does it look like?

Summary: This is a noisy data set

Even if I had all of Thomson Reuters data at my disposal—I don’t—I’d still call this a “noisy” data set. Adopting The Signal and The Noise terminology, I mean that trying to draw conclusions about how individual variables impact the data set will be hard. Trying to draw precise predictions will be impossible.

Take years, for example. A year is a long time in business terms. But trying to draw conclusions about any given year’s M&A activity is fraught because deals could be categorized multiple ways. As we’ve seen, you could count the AT&T-Time Warner deal in 2016 or 2018 (or later if the appeal delays the deal further), which drastically impacts the value of the deals done in that year. Since timing could change the data set so much, we have to be careful drawing conclusions about any one year of deal-making. This is why I used the five year average to set our predictions.

Or take mega-deals. There are less than 18 in any given year. That’s a small data set. So trying to draw conclusions about mega-deals with our variables like “direction” or “industry” or “type of deal” is fraught. Or to be more precise, we can’t have statistical confidence in these conclusions.

Warning: Power-Law distribution amplifies the effects of small sample size.

This data set, and a lot of conclusions drawn from it, is power-law distributed. AT&T bought Time-Warner from an entertainment and media deal high of $85 billion dollars. And it was joined in 2016 by 15 other mega-deals of $1 billion or more. But according to PwC there were over 679 deals of any size in 2017. That means that the first two deals (Linked-In purchase by Microsoft being the second biggest deal I found) totaled $111 billion, so more than the other 677 deals that year.

As a side note, I love explaining “power-law distributions” to people. This type of distribution happens throughout entertainment. Power-law distributions mean that a small number of deals can have a huge impact on the data set, especially if you focus on the “average” without accounting for size. So if you’re counting/measuring impact by each deal equally (not weighted by value) you could miss a lot of trends.

Conclusion: We still need a prediction!

I know, I spent today just reviewing the data about M&A. As I’ve been editing this article, I’ve been asking myself a brutal question: is there enough meat on the bones for this article?

And you know what? I think there is. Every few months Deadline or The Hollywood Reporter or Variety publish an article summarizing M&A activity in media and entertainment. And it comes up on their podcasts. But trying to find an explainer or FAQ on where the data comes from? Good luck. It matters whether data sets are imprecise or noisy or flawed. And a lot of the reporting on M&A ignores that crucial context. Hopefully I provided that today.

I swear I’ll make a prediction tomorrow.

Debunking the M&A Tidal Wave – Part II: Reviewing the Narratives

(Check out my first post analyzing the M&A landscape here.)

If you think we’re about to ride a wave of deal making, then grab your corporate strategy surfboard and let’s hit the entertainment and media waters!

Tortured analogy aside, if you saw the chart from yesterday, you know one thing…

MA PPT Chart…if you want to do M&A you should already be in the water.

If anything, the tide has been rising on M&A for years now. Deal making went from around $33 billion in 2009 (one report had it as low as $6 billion in 2008 in the depths of the Great Recession) to a frothy $200 billion in 2016. Since deal making takes time, if you waited until a judge in D.C. approved the AT&T-Time Warner merger, you’re probably too late. (And I don’t think most executives were waiting.)

Yet, the narrative after the decision was one courtroom decision will “unleash a torrent of deal-making”. Why does everyone think that?

My theory: because it is hard to look back and observe trends, as opposed to respond to events. Court cases make for exciting events. Single events get a lot of coverage. Long term trends get one or two articles a year, maybe.

So as I collected my thoughts around M&A in entertainment & media, I reviewed a lot of the articles on M&A in Hollywood. Frankly, the idea that one court case cleared the way for M&A activity isn’t the only bad narrative in this story. The idea that “disruption” is “forcing” large cable companies to merge also doesn’t hold up, to me. And that narrative even influenced the judge in the AT&T-Time Warner case.

Today, I’m going to review all the potential causes for the rise in M&A activity we see in the chart above. Then I’ll put those explanations in context of the AT&T decision. Then next week I’ll review the data to make my final prediction.

Reviewing the Traditional Narrative

Let’s start by making the case of why mergers are more frequent after the AT&T decision. From what I read, it would go something like this:

1. The entrance of tech giants (Netflix, Amazon, Apple, Google, Facebook) is disrupting traditional business models.
2. This increases the need for industry consolidation to survive.
3. But anti-trust regulators have looked skeptically at past mergers.
4. With this deal approved, companies can merge as much as they want!

I saw two major pieces of evidence marshaled for the conclusions above. First, people would use the “Disney – 21st Century Fox – Comcast” love triangle as evidence. But if anything, all the decision did was allow Comcast to bid, which it could have done anyways, and raise the price. The rate of mergers would have stayed the same. Same thing with using Shari Redstone trying to merge Viacom-CBS, which is a deal already in progress.

Second, people love to just throw out names of companies and say, “Could they merge?” If the proposed deals don’t have sources, they’re just blind speculation. Even with sources, they’re mostly talk.

Separating the Good Reasons for the Bad

Instead of crafting a narrative to suit our prediction, let’s look at all the possible reasons  for M&A activity, from the broadest reason to the most minute and ask some questions to assess their impact:

– Was this factor present in the past 10 years? 20 years? 40 years?
– Would this factor have continued regardless of the ruling?
– How important is this factor?

Industry consolidation

As I’ve mentioned before, industry has been consolidating for forty years under a lax anti-trust regime in the Justice department and in the courts. I don’t mean the media and entertainment industry, I mean all industry from healthcare to finance to retail to beverages to airlines to you name it. If every industry is consolidating (sometimes massively) then predicting future consolidation in entertainment is less bold.

I did, though, get sucked down a rabbit hole looking at consolidation in entertainment and media specifically. Consolidation is happening in every single part of entertainment from broadcast channels to cable channels to movie studios to radio stations. Even technology. So…

– Was this factor present in the past 10 years? 20 years? 40 years?
Yes, going back 40 years.
– Would this factor have continued regardless of the ruling?
Yes, the Justice Department easily blessed the Disney-21st Century Fox deal. Donald Trump’s administration and FCC chairman Ajit Pai love industry consolidation.
– How important is this factor?
Very important. As a Hollywood Reporter article said, entertainment companies have been merging since the 1940s, going through waves in the 1940s, 1980s, 1990s and the current one.

It’s a good business environment for mergers and acquisitions.

The evidence for this explanation—which specifically refutes an argument later about “tech disruption”—is that the entire M&A market is looking good right now across the economy. Indeed this is true, as this New York Times article pointed out (using Thomson Reuters data!) and Kevin Drum clarified with inflation adjusted numbers. This differs from the above explanation in that it is really about the consolidation numbers for the current economic climate.

It boils down to a few things, summarized in the New York Times piece: the tax break provides higher profits, interest rates have stayed low and the stock market is booming so firms need other ways to drive growth (and high share prices can increase capital available for M&A). So our questions:

– Was this factor present in the past 10 years? 20 years? 40 years?
It is cyclical, but has been building since the recession in 2008.
– Would this factor have continued regardless of the ruling?
Yes, that tax break isn’t going anywhere…unless a recession hits. But the ruling didn’t effect that either way.
– How important is this factor?
In my mind, nearly as big as the industry consolidation.

Technology firms are entering the media and entertainment business.

Notice, I’m not saying that tech firms are “disrupting” traditional business models. This explanation is simpler: technology firms like the FAANGs have huge amounts of cash on hand and/or huge market capitalization’s, so they are on a buying spree. This increases the likelihood of mergers not because entertainment companies need mergers to survive (they consolidate because of the above reasons), but because entertainment firms want to avoid being acquired.

– Was this factor present in the past 10 years? 20 years? 40 years?
Yes, going back 10 years.
– Would this factor have continued regardless of the ruling?
Yes, the Justice Department isn’t taking on tech giants either. Except for Jeff Bezos.
– How important is this factor?
It depends. So far, the major tech companies haven’t actually saddled themselves with a legacy content company, but built their own platforms (Netflix, Youtube and Amazon) or bought other technology companies (SnapChat, Twitch, What’sApp, Instagram). So we’ll see.

Tech companies are disrupting traditional business models.

This is the ever pervasive idea that streaming is disrupting pay and broadcast TV and music buying and radio and everything else. Oh and advertising is being disrupted too.

I look most skeptically at this explanation. The fairest way to describe this—and I’m trying to be fair—is that the new business models are cutting profit margins of traditional firms, so companies need to bulk up to maintain their profit margins. And it really is true that new entrants like Netflix offer much cheaper alternatives then traditional models, though, Netflix is less profitable in cash terms.

Like I said above, this is the explanation I value the least. Not that it doesn’t have an impact, but it has the biggest “hype to reality” ratio. Industry consolidation allows firms to increase their profit margins, which they do regardless of new entrants. Since this is happening across all industries, it seems like an explanation fitted to the data, not the true driver.

– Was this factor present in the past 10 years? 20 years? 40 years?
It is the one new factor of the last ten years.
– Would this factor have continued regardless of the ruling?
Yes. Netflix is still scary.
– How important is this factor?
It is mainly important for the mentality. It scares executives so they want to bulk up to ward it off.

Anti-trust regulators and the FCC plan to prevent further consolidation.

If you think the Trump administration had/has a plan to prevent consolidation in industry, could you please point it out to me?

Let’s be honest, the government under Trump and Republican leadership really doesn’t care about industry consolidation. Trump actually praised his friend Rupert Murdoch for making such a good deal with Bob Iger. He called it “great for job creation”. Under a Democratic President, maybe the FCC and Justice Department look skeptically at consolidation, but for all their efforts, the Obama administration only stopped three mega-mergers (Comcast/Time Warner Cable, Sprint/T-Mobile round 1, and AT&T/T-Mobile), and it only delayed the consolidation not stopped it.

So when it comes to the question, “What if the judge had ruled against AT&T?”, would that have encouraged the Justice Department to go on a spree of trust busting? I doubt it. Would they have stopped additional deals? Probably not. I think most of AT&T law suit was more about CNN then it was about the size of the deal. Consider, T-Mobile and Sprint merged before the final judgement. They weren’t worried about anti-trust. So the questions:

– Was this factor present in the past 10 years? 20 years? 40 years?
No. The government tried to stop the Comcast-NBCU merger and successfully dissuaded Comcast from the Time Warner Cable merger. But in the last 18 months? Yeah it hasn’t been a thing.
– Would this factor have continued regardless of the ruling?
Yes, it would have. Even when Obama tried to stop some mergers, over time the Justice Department was worn down. So AT&T bought DirecTV, Charter bought Time Warner Cable, and now cellular providers are merging. Again, that was all before the decision.
– How important is this factor?
Not important since it really didn’t effect the behavior of companies.

Mergers and Acquisitions are good for CEOs individually

Here’s the simplest, most human, most “economic” (or Freakonomics?) explanation for the frequency of M&A activity.

CEOs make bank off mergers and acquisitions.

In other words, if humans are self-interested, sometimes they pursue goals and outcomes that don’t align with the incentives of their company or firm. Making better products is hard. Cutting costs in painful. Merging with another company? Relatively easier and more profitable.

The trades are reporting that after a successful merger—meaning it goes through, not that it makes money—Jeff Bewkes made $50 million dollars last year, some of which was driven by Time Warner’s merger-inflated stock price. AT&T CEO Randall Stephenson can now demand a higher salary with his larger company to run. In the short term, M&A activity is rewarded by share price increases, even if the deal bombs, as happens about 50% of the time.

– Was this factor present in the past 10 years? 20 years? 40 years?
Yes.
– Would this factor have continued regardless of the ruling?
Yes, it would have. And honestly, the economy is set up to allow it to continue.
– How important is this factor?
Very. These are the self-interested decision-makers running the system. They’ll make deals to make money, and convince themselves it’s a good thing.

Playing Devil’s Advocate: Why could M&A decrease?

To summarize, the traditional narrative says M&A activity—the rate—will increase. I think it will still grow, but at the same historical rates. At worst, I’ve set a floor of “M&A activity will stay flat”, meaning it has zero growth. So at worst it will maintain its value of $140 billion in deal value per year with 16-18 mega-deals.

But could we make arguments in the opposite direction? That M&A activity will actually slow down? Sure. And if we’re building a 90% confidence interval for the future, we absolutely should give this more weight. What could stall M&A activity?

An economic slowdown

This is what stopped the last wave of consolidation. Basically, the 2008 housing crisis and Great Recession. When no one wants to lend, and share prices fall, and you don’t have profits on hand, then it freezes the market. This would slow or stop consolidation temporarily (and as I saw first hand motivate a lot of investment bankers to go to business school).

Tech continues to build not buy.

I actually don’t hate this strategy and the Bloomberg link at the top makes this case too. Amazon, Netflix and Youtube have all created businesses from scratch. Why buy a legacy company with lots of infrastructure when you can build it yourself? You potentially save a lot of money and, in some cases, it’s unclear what value traditional firms bring to the table.

But they do have some value. Disney doesn’t have its tremendous licensing and merchandising and theme parks businesses without some know how. They’ve kept these characters relevant and popular for decades. The question is: do you have to buy that company or buy those people, which is what Netflix is doing? So what tech firms collectively decide will impact the number of deals.

The number of potential partners dwindles.

This is actually the most persuasive reason for me: if everyone keeps consolidating, after a certain point there is no one left to partner with. This would easily impact the number of “mega-deals”. That said, we have a bit to go until we have complete consolidation, especially counting vertical mergers.

The market turns against conglomerates.

When I built a table to help me analyze M&A activity (collecting the data myself), I had to come up with a term for the big six movie studios. Honestly, “movie studio” sells it short and “conglomerate” makes more sense. It’s the best way to describe a company with television networks, a movie studio, TV production, gaming, theme parks and whatever else the “big 6” studios own.

Yet, in the 1980s, it meant companies like General Electric, which bought broadcast networks and made everything from industrial equipment to microwaves to Cheers. Then the market turned against conglomerates because most of the time big companies didn’t run all their different business units that well, and the market assumed that splitting conglomerates into their individual pieces would have better value.

Tech firms defy this logic. Maybe it is because the wise leadership of Jobs/Cook, Bezos, Zuckerberg, Hastings/Sarandos and Brin/Page truly does turn everything they touch to management gold. Or, they have such huge valuations being “tech” that they can enter any industry and it doesn’t matter. I’m not saying they won’t continue to be valued incredibly highly, but this state of affairs could end sooner then you think.

The political winds change

I want to put this out on Twitter, but wouldn’t the best political campaign for Democrats in the fall be to micro-target cities that have seen huge cable bill increases post-Comcast merger and post DirecTV merger? Just hit Republicans on the issue like this:

Republicans like Mitch McConnell and Donald Trump (and his lackey Ajit Pai) want to increase you cable bill to make their billionaire friends rich.

I’m not a pollster, so I don’t know. But does anything make people more angry than cable bills? Democrats use this! Either way, if a Democrat takes power in 2020, they could restart FCC scrutiny on issues related to anti-trust and merger scrutiny. It might slow the rate of mergers, but like Obama probably not stop it altogether.

We still don’t have a prediction yet

And yet we have 2,800 words on top of the words yesterday. But now that we have the explanations, on Monday, we can dig deeper into the numbers, beyond what the top line data said from Tuesday’s post.

 

Debunking the M&A Tidal Wave – Part I: Setting the Terms

After the Justice Department lost their anti-trust lawsuit against AT&T–which allowed the merger with Time Warner to go through–a consensus emerged in the entertainment press that I would summarize like this:

“The approval of this merger will start a wave of acquisitions and mergers in entertainment.”

This isn’t a straw man argument: I saw this in the Hollywood Reporter, Variety, The Washington Post, Deadline and Bloomberg. (And probably more I just didn’t capture in link form.)

Natural skeptic that I am, in my initial reaction that week I wondered, is this true?

And if we’re asking if it’s true—and we don’t know because it will take place in the future—that means it’s a prediction. And if you agree with the above sentiment—meaning you find it true—you’re predicting the future too.

Predicting the future is hard.

Let’s play a game. It’s the prediction game. Many writers made the prediction above. Many analysts echoed those in stock price moves or recommendations. And you likely agree with it. So answer this: If mergers and acquisitions are increasing, what do you think the percentage increase in mergers and acquisitions in entertainment will be in 2018? 2019? For the next five years?

Write it down if you can. Or lock it in your head. We’ll return to it at the end.

Once I started thinking about this question, I started scouring the internet for data on M&A activity. Then I started writing. Then the article kept going. And going. All of which is to say, I’m going to dive deep into this topic and hopefully return over time. The merger of AT&T and Warner Media Group is probably the first or second most important news story of the year, so we should understand it.

Making a prediction about the future is a good way to understand it. However, my prediction will be quantified and written down on this website by Monday. But we have some work to do to get there.

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