All posts by EntertainmentStrategyGuy

Former strategy and business development guy at a major streaming company. But I like writing more than sending email, so I launched this website to share what I know.

Why You Can’t Use Data to Predict Hit TV Series Either

A few weeks back, I explained why “small sample size” dooms any effort to use big data to predict box office performance of feature films. But what about TV shows? What about streaming services? Can’t they use advanced algorithms to predict success there?

Nope.

As “No, Seriously, Why Don’t You Use Data to Make Movies?” explained in  a “mini-statistics lesson” how small sample size and multiple variables combine to make forecasting very inaccurate in movies. Today, I want to take the lessons of that article and apply it to making TV shows in the streaming era.

Here are the key reasons why “big data” can’t solve making hit TV shows.

1. It’s also data poor environment.

To start, TV has long had fewer data points than feature films. Only recently did the number of scripted TV seasons pass feature films (depending how you count it). Currently, there are over 500 scripted TV series per year in the US. As I wrote last time, that’s still a small sample size.

2. It’s even smaller when you factor in returning series.

Most new “seasons” aren’t brand new, they’re returning seasons of TV series that have been on for several years. That kills your forecasting model.

Take Game of Thrones season 8. Yes, you could call “season 8” a unique data point to study. But with TV shows, to have an accurate model, you’d need to introduce a categorical variable, “has had a previously successful season”. The answer for Game of Thrones for that categorical variables is “Yes!” In other words, it’s super easy to predict that subsequent seasons of Game of Thrones and The Walking Dead will have high ratings because their previous seasons had high ratings. (Though not always.)

The challenge is predicting successful new shows, and that data set is much much smaller than the 400 or so scripted seasons produced every year.

3. The number of categorical variables for a TV show at “pitch” is near infinite.

When a TV show is being pitched or is at the script stage, it has a huge number of categorical variables still in flux. Each of these could influence the final independent variable, which is viewership (depending on if you’re a network or streaming platform you could define this multiple ways).

Everything from the director who ultimately directs the first episode or the acting talent who signs on to the story plan for season one could impact the ratings. Even variables most studios don’t care about like “who is the production manager?” or “can the showrunner manage a room of people?” are categorical variables that could affect the final outcome. Without a large sample size, it’s just tough to predict anything. (And some of them are super hard or very, very difficult to quantify.)

Many good or great scripts or TV pitches become bad TV series. For a lot of reasons that don’t have to do with the script. This is why “algorithms” can’t predict things with high confidence. This explanation also definitely applies to feature films, though I didn’t mention it last time.

4. Most pitches/scripts/pilots will never get made. Hence no “dependent variable”.

Most claims to use advanced metrics or analytics or data to pick TV series utterly discount this key fact. Sure you get thousands of pitches and scripts to read, but they don’t become TV series. Replace “dependent variable” with “performance” and you see the challenge. You have three scripts, and you pick one to become a pilot. The other two scripts don’t get made into to TV shows. So can we use them in our equation for forecast success? No, because they don’t have the same dependent variable to allow us to use them as data. All you can say is you didn’t make them into TV shows. But that’s not a data point.

5. Finally, most of the time, you can only control your own decisions.

The best way to control a data-driven process is to own all the data. And for a TV studio or streaming service, that means understanding all the decisions that went into making a TV show. So, if you don’t make it yourself, well, you can’t really understand what decisions were made. So for a streaming service, that “n” is very, very small.

So let’s use Netflix as an example. They made what, eighty TV shows to date? (Not counting the international productions, that again are their own categorical variables.) So the maximum for their sample size is eighty. Break it down even further by separating kids shows from adult shows and previous IP versus new IP and then you can break it down by genre. You see where I am going with this. The “n” is dwindling rapidly.

What about all the customer viewing data they had from the TV shows on their platform? Well, it doesn’t give Netflix that much of an advantage of traditional networks. Even if traditional networks don’t have Netflix streaming data specifically, they have Nielsen TV viewing data and box office data. Netflix uses that data too.

Which isn’t to say Netflix doesn’t have tons of data and doesn’t use it a lot. But they don’t use it to “pick TV shows” they use it broadly. The “data analysis” that Netflix does is pretty simple: it sees what is popular with its user base. So do traditional TV networks and studios. And what has Netflix learned? People like broad based comedies and dramas featuring crime and/or police. It also knows some people like quirky comedies and some others like arty-shows. (Netflix’ key advantage is it just pays a lot more for the same shows with less opportunity to monetize. That’s a problem for another post.)

So is Netflix is “using data” to decide on TV shows? Yes, but it isn’t that much better than the rest of the industry. Do they have an algorithm that tells them which shows will do well on their platform? Yes, but it is wrong a lot of the time.

Weekly News Round Up – 22 June 2018

Enjoy this week’s updates. A little calmer than last week!

The Most Important Story of the Week – Disney Increased Its Fox Bid to $70+ Billion

So I think I mentioned it before, but if you’re enjoying my long “analysis” article on the Disney-Acquisition of Lucasfilm, you’ll love a sequel coming in a few months: “Who Won the Deal, Rupert or Bob? Analyzing the Disney merger/acquisition of 21st Century Fox”. I started it a few months back, and back then the deal was only worth $50 or so billion (with a b) dollars.

Then a judge cleared the way for the AT&T-Time Warner merger, the topic of last week’s update. With ostensibly the path clear for distributors to acquire content creators, Comcast put in a bid on 21st Century Fox (though Comcast itself proved the government wouldn’t stop these deals six years ago). Not wanting to lose, Disney increased their bid.

Honestly, the higher price both makes sense and will likely cause the winner to lose money on the deal. How can both things be true? On the one hand, when I had done my analysis comparing Time-Warner to 21st Century Fox, the difference in value seemed more tied to market capitalization than the value of the existing assets, especially the value of those assets under Disney’s management. (I’ll write about stock prices at some point and whether they reflect economic reality. They do and don’t.)

The downside is paying too much for the underlying assets. Which can absolutely happen in a bidding war and is called in economics the “winner’s curse”. It’s not just an economic theory: when you have multiple bidders on assets with uncertain value, you increase the risk that someone pays too much and it happens all the time. For the winner here, the margin for error in the acquisition has shrunk to almost nothing.

There is one clear winner, though: Rupert Murdoch makes more money either way.

Long Read of the Week – MGM’s $260 Million Payout: Making Sense of CEO Gary Barber’s Eye-Popping Exit

This isn’t the longest long read I’ll ever recommend, but it’s worth it for executives at the top of corporations to really understand the dynamics of this industry. Read Stephen Galloway in the Hollywood Reporter on the $260 million being paid to MGM’s ex-CEO Gary Barber here. Put in complicated terms to put a shine on it: entertainment conglomerates are currently and have always paid top executives well due to market conditions. Put in layman’s terms, top studios bosses get paid a ton, and it’s cause of all the other guys.

I would love to say, “On one hand I get this” but honestly I still don’t. Being a development executive is one of the most in demand jobs in America. There are thousands of qualified applicants. Same with aspiring CEO types. So why are salaries so inflated? And why do they go to executives who aren’t truly revolutionary? As the long read shows, many times these exorbitantly paid people are paid even more to leave.

(It’s also interesting that in this case it wasn’t so much for firing someone for incompetence, but because of a threatened hostile takeover. So it’s not quite the same thing.)

Lots of News with No News

Man, I guess the theme of today’s update is reflecting on future articles. Especially, my long form ones. Well, in another great long form article in progress, I’m going to compare Amazon’s Lord of the Rings and HBO’s Game of Thrones. Trust me, you’ll like it.

So I read a lot of news about HBO and Amazon Video/Studios/Prime, including this one about Jennifer Salke’s new approach to Amazon Studios/Prime Video. My takeaway is she has a ton of relationships so is taking a lot more pitches and hands-on approach then the previous head of the studio. Coincidentally, while reading I saw this article about Warner Bros. new approach to the DC cinematic universe. (As a fan boy I tend to read anything about comics too.)

In both cases I see the same general story that appears in the pages of the trades every few months: a talented and self-confident executive is taking it on their own shoulders to turn around development, and hence the finances, of a movie studio. Ironically, these same stories were written about their predecessors. So, that’s all to say, these types of stories offer a lot of news without a lot of actual news.

Data of the Week

And my final bugaboo, Netflix! Hat tip to BGR for this article from the Exstreamist where they polled how many Netflix users are sharing passwords. In short, a lot of people don’t pay for Netflix and share passwords. This is unlike traditional TV or cable, and honestly, and I’m rare in this opinion, I think this is a bad plan by Netflix. But more on that in future posts.

Disney-Lucasfilm Deal Part III: Movie Revenue – The Economics of Blockbusters

(This is Part III 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: The Television!
Part VII: Licensing (Merchandise, Like Toys, Books, Comics, Video Games and Stuff)
Part VIII: The Theme Parks Make The Rest of the Money)

A peak behind the curtain into how long I’ve been writing this series. I started sketching thoughts on the Lucasfilm acquisition back in February, then started writing this article in April. I’m still finishing it in June. Fortunately, this extended timeline managed to teach me a rare lesson in humility about forecasting.

See, when I started writing, Solo: A Star Wars Story was still three months away. At the time, I needed to estimate for its box office performance to calculate the movie revenue. So I searched for projections. In early March, I found forecasts for Solo’s US opening north of $150-170 million, ending in a total US run of $350-$475 million. At the time, we all assumed Solo would be a sure thing at the box office. So I put that in my model. So I put into the model that Solo would do a bit less then Rogue One, at about $800 million in total worldwide box office.

By May, the estimates had lowered but still projected an opening weekend of $150-170 million. Days before the launch, the estimates had dropped even further, to $130-$150 million. The estimates continued to decay even as the weekend progressed, as this updated article from Deadline shows. Ultimately, Solo ended up with an opening weekend of $101 million domestic on track for about $350-360 million worldwide. (The March estimates had it at $350 for just the domestic market.)

Using the tracking data above, I see a “floor” domestically of about $300 million for Solo.

There’s a very famous quote about what people do and don’t know about movies. I won’t repeat it here, but I will agree that forecasting box office returns (and hence all revenue for a filmed project) is incredibly difficult. Even a movie as closely watched as Solo: A Star Wars Story missed most of the “ranges” offered by box office projections. My initial assumptions about Solo were way too confident which meant my entire analysis was off.

So we need to go into today’s post with a little humility and our eyes open. Last time, I asked a few questions:

Lucasfilm and Disney are doing well, but how well? More precisely, how much cash did they make on these movies? And how well do we think they will do going forward? Could they start losing money?

I answered the first two questions (doing really well; $3.7 billion in unadjusted gross profits or $2.5 billion in 2012 adjusted terms) in the last article. That was the easy stuff; what I called, “What we know” last time since I just had to plug the results into my model with some sleuthing.

Now comes the hard part. We have to estimate how future films will do and then guess about the rest of the potential slate. Which I’ll try to do this week. But first, I need to illustrate how hard this is…

Difficulty in Forecasting Box Office

To show the difficulty, let’s start with the difference between Solo: A Star Wars Story and The Force Awakens. One grossed $2 billion dollars at the worldwide box office. The other did under $400 million. That’s a gap of more than $1.5 billion dollars. That gap also roughly equates to how how large our confidence interval could be when projecting box office for films that aren’t coming out for two years (or longer). To capture that, look at this chart I made (using approximate numbers):

Slide 15a

I put in these numbers as placeholder estimates. What they are are the fictional “90% confidence interval” for The Force Awakens. In other words, at greenlight, you could have guessed that a new Star Wars movie would do $2.4 billion in total box office or $400 million, and you’d be right, 90% of the time.

Read More

Has Hollywood been “Moneyballed”?

No.

That’s a quick article! Just one word and you can continue on to elsewhere in your internet with too many articles to read.

Oh, you’re still here. Well, I do have some more thoughts, so let’s keep going. Since this website is still gaining steam, I haven’t had time to dig into my thoughts on the entertainment press. The “trades” if you will.

I love the trades. They provide tons of great insight to this industry. That said, like much of the media in general, they can get repetitive. Here’s an example (from an upcoming long, long article on this topic) from back in November: Deadline reported that Amazon Studios had closed a deal for a Lord of the Rings TV show. Then every other outlet reported the exact same thing. The article on The Hollywood Reporter as it was on Variety as it was on The Ringer or A.V. Club. It goes on. I hate that.

That’s why I loved the article by Derek Thompson in The Atlantic called, “Why Amazon Just Spent a Fortune to Turn Lord of the Rings into a TV Show”. Thompson took a stand and made an argument, unlike much of the other coverage. What he said was that this LOTR deal showed the limits to Amazon’s previous strategy, and now they were moving into blockbusters. I liked how he describes their shift in strategies.

Then he added this sub-title, “There is no “moneyball” for media. In entertainment, overkill is underrated.”

Why, oh, why did he have to bring “moneyball” into it?

Long time readers—or new readers who’ve gone back to read every article (it’s not that hard)—know that my first and second articles both said I’m here to help Hollywood “make better decisions”. I love talking about decision-making (especially when bolstered by data). Hollywood and the press coverage do it the other way. They obsess about data and algorithm (ignoring the decision-making).

Naturally then, I’m a fan of “moneyball” even though I didn’t use that term in that first article. Unfortunately, like its cousins, analytics and data, “moneyball”–the term derived from Michael Lewis’ excellent book on the subject–is horribly over-used and abused. Most people use “moneyball” to mean “using data” or in Hollywood’s case, using “algorithms”.

Sigh.

So I’m going to define what a “moneyball” approach to any field would look like. Then I’m going to re-analyze Amazon Studios/Video/Prime as Thompson did to determine if they were using a “moneyball” approach to content selection. As we’ll see, it turns out that Amazon has actually embraced data by moving away from smaller, critically-acclaimed projects. Basically, the opposite of Derek Thompson’s hypothesis.

A “Moneyball” Approach to…Anything?

Let’s start by synthesizing the ideas of “moneyball”. I used three books about decision-making: Moneyball, The Undoing Project and The Signal and the Noise. (I’m going to differentiate the book from the philosophy of moneyball by italicizing the title and putting the philosophy in quotes.) You could add other books like Thinking Fast and Slow or Superforecasting, but those three provide the core. And really those other books amplify the lessons in the first three books. (I would also appreciate any recommendations for books on decision-making you think should be included!)

I’ll add, there isn’t an official “moneyball” manifesto, so these are my opinions. What I’m doing is trying to summarize “moneyball” into a process, impacted by my own opinions on decision-making. For instance, the financial theory of maximizing “Net Present Value” is basically undergirding all of those books, but I’m not going to recommend a finance textbook right now or you’ll all run away. (The introduction to The Undoing Project contains probably the best summary of the ideas.)

The Principles of “moneyball”

Make better decisions

The whole point of moneyball is to make better decisions. The implicit assumption of why you have data is to help you make decisions. In baseball this is about which players to sign, trade or draft. In elections, it’s about where and how to spend campaign money. In finance, it’s about which equities or bonds or companies to buy or sell. And when to buy or sell all things. Those are concrete decisions.

Make better decisions towards a goal

Sometimes this is very clear. Billy Beane is trading baseball players to get “wins”. “Wins” in baseball are the currency of success. He makes decisions to increase the odds they get more wins, which translates to success. However, as was noted in most reviews, Billy Beane had an extremely limited budget…

Making better decisions towards a goal within a budget

That’s why so many people who brought the revolution to sports came from finance or investment banking. They were already used to making decisions with a limited amount of capital to invest. If you raise a $100 million dollar venture capital fund from investors, you can’t get more (unless you borrow more, but then you have to pay interest, so just go with it for now). You have $100 million to invest. It also changes how you phrase decisions from “is this a good deal?” to “is this a good deal for X amount of money?” The answer for many deals could be yes to the first, but no to the second. Price or cost is always an issue. This is also why both the Oakland A’s—a cash poor team—and the Red Sox—a very wealthy team—could both pursue “moneyball” strategies and be correct, and have totally different approaches.

Make better decisions by using data

This is the part that most people know about “moneyball” and where most people stop. They assume that all you need is data and, wham, you’re doing moneyball. But I put it fourth because without the decisions towards a goals in a constrained budget, the data doesn’t matter. You can use data a lot, but switch goals regularly, so the data won’t help you. The key difference is that the data is used to make better decisions. Then tested to keep having the most accurate data to make the best decisions. Data is key to making the whole thing run, but the purpose of the data is to make decisions which lead to the desired outcome.

Test conventional wisdom

Of course, once you start using data, you naturally see how many decisions are justified not with data but conventional wisdom. And it’s what really what separates a moneyball approach from a traditional approach. (And the part that generates the most anger.) “Moneyball” doesn’t take anything for granted. Before you make a decision towards a goal within your budget, you ask why. The sterling example of this is Billy Beane hated how his scouts referred to players as “looking good in a uniform”. As soon as he started looking skeptically at his decision-making process, he understood there is no way to explain why looking good in a uniform is correlated with “wins”. And it wasn’t.

Getting rid of uncorrelated data

Really testing conventional wisdom is a subset of this. Basically, conventional wisdom is the largest area of uncorrelated data. But it deserves it’s own section. Once you start using data to make decisions towards a goal within a defined budget, then you throw out all that data that isn’t correlated with success. In The Undoing Project, Daryl Morey, the general manager of the Houston Rockets—read the opening chapter here on Slate—describes how they’ve tested “player interviews” during the draft process, and it isn’t correlated with success. Basically, it usually adds bad data (personal opinion/bias) into the process. And bad data leads to worse decisions.

Exploiting Inefficiencies

I wasn’t going to include, but felt I should. After “using data” or mistaking tactics for strategy, this is the most common summary of “moneyball”. Basically, Billy Beane exploited market inefficiencies and Lewis, in his introduction to The Undoing Project, mentions exploiting inefficiencies multiple times. So I included it. But the reason I put it so low is that inefficiencies aren’t the driver of “moneyball” but the natural result. If you value things correlated with success, and the market doesn’t value them properly, then, yeah you’ll exploit it. Really it’s a matter of what comes first. I’d say that once you’re focused on decision-making with data, you’ll find inefficiencies. But you don’t start trying to find inefficiencies.

Track your predictions

This point and the next one really comes from Nate Silver, but I lump them in together because you see it in the moneyball sports examples like Daryl Morey and Billy Beane. One of the keys in making-decisions is to be super honest with the decisions you make. And the only way to do that is to track your decisions (and predictions) to see how often you were right or wrong. Then you can analyze where your process was wrong or analyze…

Think Probabilistically

…where the probabilities just went against you. This is another huge point from Silver and he just reemphasized it after the 2016 Presidential election. He gave Trump a 30% chance of winning, and Trump won. Silver’s point is that 30% is a big number. 3 out of 10 happens a lot. So even with the best process, you can be wrong a lot of the time in big bets. Silver comes from a poker background where even the best hand well-played can sometimes only have a 70% chance to win. That’s probabilistic decision-making.

So is Hollywood “moneyballed”?

I don’t know.

Well, not most of it. Netflix definitely uses data. And Amazon has said they do too. And Legendary was the subject of an HBR article called “Moneyball for Movie Studios?”. But as I said above, just because you have data doesn’t mean you’re following the example of the Oakland As, Boston Red Sox and Houston Rockets in following “moneyball”.

So let’s try to answer Derek Thompson’s Atlantic question: did Amazon turn away from a “moneyball” strategy?

I don’t know.

Thompson made the classic “moneyball” mistake which is to take a tactic employed by Billy Beane and call that “moneyball”. He equated Billy Beane buying low-cost, high-upside players instead of paying out huge contracts to superstars with Amazon’s approach of going after lower budget award winning shows. But the Boston Red Sox had a “moneyball” approach to baseball (paying huge salaries to stars) and won a World Series. The difference between them and Oakland was they had a lot more money (budget) to make their decisions.

So is Amazon Studios using a moneyball approach? Frankly, I’d have to interview the key decision-makers and review their slide decks. Only by asking the specific questions could see if data is driving the decisions or justifying them post-hoc. Sometimes really in-depth articles provide a lot of clues (take this New Yorker article on STX for example) or you’ll be the subject of an HBR case study (which has tons of interviews to write it), but short of that it’s nearly impossible to tell.

In entertainment, like baseball, making smaller movies or TV shows can be moneyball. Think Blumhouse. Or making big blockbusters could be moneyball. Think Disney or Legendary. I know the numbers behind both those tactics and I could justify them (again towards the success metric within their budgets).

The true lesson of Moneyball and The Undoing Project and The Process in Philadelphia and The Signal and the Noise, is that moneyball is unpleasant. People hate it. People still disparage Billy Beane, question Daryl Morey, make fun of Nate Silver and Sam Hinkie was straight-up fired. When you test their conventional wisdom and it’s wrong, the conventional thinkers get upset. Like really mad. That’s the true moral of the story in all the above scenarios.

Hollywood is a place that avoids unpleasantness. It’s a people-driven industry (it’s who you know after all) and moneyball would hurt too many feelings. Uncorrelated data (like winning awards) or conventional wisdom (hiring established showrunners) or binary analysis (in pilot season most shows are described as “great” or “bombs”) thrives in Hollywood. It’s a creative, people-driven field, which makes it as un-moneyball as you get. Sure the distribution method (streaming video!) is changing, but the decision-making behind it remains mostly the same.

Of course, that leaves the field ripe for true “moneyball” disruption. We’ll see who does it.

Most Important Story of the Week and Other Reads – 15 June 2018

Today’s update could be called the “don’t hold your breath” edition. Once we knew that Judge Richard Leon would deliver his verdict on the Time Warner-AT&T proposed merger on Tuesday of this week, well we knew we had our biggest impact news event of the week.

And I’ll get to it. But first I want to provide my recommendation on how to read the news.

Listen, I’m just some guy writing on the internet. You don’t have to listen to me. Even if you don’t take my advice, maybe it will cause you to pause for just a moment to question your (unasked) assumptions. Maybe you’ve thought a lot about your daily schedule and your news diet; if you haven’t, maybe my unasked for advice will help you reconsider it. To make you better.

To illustrate my advice, take my schedule. On Monday, as I was planning my week, I considered clearing my Tuesday schedule to wait for the AT&T decision. I thought, “Maybe I should schedule some time to react to that in real time.” Instead, I went the other way: I deliberately avoided all news on Tuesday and spent the time writing my article on the Disney acquisition of Lucasfilm.

Why?

Because I didn’t need to know the results of the decision right away. In fact, by waiting, I could savor the really quality analysis of the decision instead of the simply immediate news. So that’s what I did. Unless you work at Time-Warner, AT&T, Comcast or Disney, you could wait until the news was digested and analyzed.

You should follow my example. The fastest breaking news is often wrong. The initial story often times doesn’t hold up to scrutiny. (The Masterpiece Cakeshop decision last week was the most glaring example of this. The initial articles failed to capture that even though the ruling was in favor of the baker, it didn’t set a precedent.) That’s reason one to take your time. The second is that breaking news hardly ever is really relevant to your current decision-making. In other words, if you don’t need the news to make an immediate decision, you can schedule it to later. Like I did with this post.

So, avoid the news during the work day, or until scheduled times. And have a deliberate news reading strategy that avoids the urgent for the important.

Just my advice. On to the regularly scheduled programming.

The Most Important Story of the Week

The AT&T-Time Warner merger is the biggest story this week, probably the month and will likely make the top five in our year-end roundup. So no other stories this week, just this one. Because it is such big news, I won’t just provide my usual one thought but three:

The reason? Netflix

Well, Netflix and all other “innovative” video streamers. I read that Judge Leon approved the merger because of Netflix in a few different articles, and I wondered if Judge Leon really did emphasize this as much as the coverage suggested. Well, he did. Judge Leon put it right on page 2. He says, paraphrasing, customers are cord cutting because of successful business models like Netflix, Hulu and Amazon (in that order, too, which says something) and that means, if you buy it, Time-Warner and AT&T needed this merger to stay relevant.

But what if Judge Leon misunderstands why the streaming companies have gained market share? The answer to that question is crucial to both good governance (via regulation) and good business (via competitor decisions). If Netflix and Amazon are truly creating value for customers, then we shouldn’t allow this merger to hinder that; we should force Time-Warner and AT&T to separately discover how to create value for customers. If Netflix, Hulu and Amazon are, on the other hand, simply capturing value by delivering products at below cost, then we shouldn’t allow this merger because those companies have unsustainable business models. They’ll flame out as soon as the markets correct and we’ll have this colossus remaining.

The predictions of future mergers? Too confident

As soon as the deal was announced, I read this take in The Hollywood Reporter and elsewhere. I’d call this the “generic hot take” analysis of the deal. I just want to raise a flag that says, “Be aware: this is a prediction, not a fact.”

Honestly, the entertainment industry has been consolidating for forty years. This may accelerate that trend, but not by that much. That’s a prediction too, but being more skeptical is usually more reliable than being overly optimistic. Also, while one judge could have stopped this deal the Trump administration seems largely supportive of mergers so the pace in entertainment likely would have continued.

The impact? Bad for consumers

I say this as a committed free-marketeer. Basically, I love Luigi Zingales’ description of himself from an old Planet Money episode:

    “I’m pro-markets, but not necessarily pro-business.”

There’s a difference. I can’t provide all my logic and explanations in this post, but I’ll say that more industry consolidation in any industry tends to be bad for consumers. Yes, consolidation can lead to lower prices, but it can also lead to less consumer choice (including lower prices on average, with a higher floor, if that makes sense). The problem is businesses love consolidation. That’s why “pro-business” isn’t the same as “pro-market”.

I tend to side with Kevin Drum, who has summed it up best, that there are benefits to competition in and of itself. So yes, I side with those who say this is a bad deal for consumers/customers/the public. Having massive monopolies or near monopolies or oligopolies doesn’t help customers. And the entry of other monopolies from a different industry (technology) isn’t an argument for more consolidation.

Good Reads

So those were my three thoughts. I have two more I’m letting marinate for a week as I think about them. I’ll add I had hoped to read better analysis on this topic then I saw, in general. But just because I didn’t read a lot, didn’t mean I didn’t find a few of good ones:

Michael Hiltzick in the LA Times

His opening is good on its own, because it just captures the inanity of the situation. At the same time that a judge can rule this deal protects consumers, most of the public/intelligentsia acknowledge that this merger will likely hurt consumers. That’s a win for the lawyers. But his point is definitely right that this deal isn’t unique at all and Comcast-NBCUniversal cleared the way. That deal at least had a lot of strings that scared off future mergers.

(And he didn’t point out that the judge who approved Comcast-NBCUniversal is the same one who approved this deal. That staggers me. More on that later.)

Tara LaChapelle in Bloomberg

This article is basically a variation on the “mergers are imminent!” theme I semi-questioned above. That said, I’m a sucker for visualizations that help to clarify a complex topic. This one succeeds. (And its predecessor.) It’s a good layout of the landscape. Though it doesn’t mean the pace of mergers will increase as predicted.

Nilay Patel in The Verge

If you don’t have time to read the whole thing, I enjoyed Patel’s thorough read-through of the opinion and summarizing. He also points out how often Judge Leon got the facts about entertainment wrong.  He also shows how the judge’s read of one expert decided basically the entire case. In other words, if you want to know the danger in having one expert approve deals that impact the future of entertainment, this is it.

Disney-Lucasfilm Deal – Part II: Star Wars Movie Revenue So Far

(This is Part II 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: The Television!
Part VII: Licensing (Merchandise, Like Toys, Books, Comics, Video Games and Stuff)
Part VIII: The Theme Parks Make The Rest of the Money)

Today, we continue our series evaluating the Disney purchase of Lucasfilm for $4 billion dollars. (Read the introduction here.) I can hear some of you saying, “Why do you even need to do that analysis? Why did you waste all those words on a question we already know the answer to!” And then you would point to this Hollywood Reporter headline:

Star Wars’ Franchise Crosses $4 Billion [Box Office], Eclipsing Disney’s Lucasfilm Price

Sigh.

I mean, if you want to know why I’m writing this series, that article—or more precisely that terrible headline—explains it. Even the article points out that Disney won’t keep all of the $4 billion in box office, and it doesn’t account for production costs or marketing. So the article itself acknowledges that the headline is as flashy as it is misleading. (And I would throw in that it doesn’t factor in the time value of money, the centerpiece of Part I’s analysis.)

Yet the Star Wars franchise has crushed the domestic and international box office. Doing $4.7 billion (and counting) in total box office since Disney acquired it ain’t nothing. Claiming three number one movies for three years in a row ain’t nothing. Having three films each do over a $1 billion in box office ain’t nothing.

Lucasfilm and Disney are doing well, but how well? More precisely, how much cash did they make on these movies? How well do we think they will do going forward? And could they start losing money?

How to Analyze Lucasfilm Movie Performance

I’ve had a lot of “fun” trying to figure out how to organize this part of my analysis. There’s a lot to go over and explain and justify. I’m really worried that, without realizing it, I’ll tip into text book territory and all my easily distracted Baby Boomer readers will go back to email. (What? Should I have put easily distracted Millennials? I’ll save that for another time.)

The difficulty stems in part from having to differentiate between what I know, what I can estimate, and what I have to, frankly, guess about Star Wars films. And that realization gave me the organization for the rest of the movie section. When I say “know”, I mean that we have the results of how well a movie did. We know (roughly) how movies that have been released did at the box office. “Estimate” means films we know are coming out (or have a high confidence that they will come out) but we have to estimate how well they will do. And all the other potential films? Those are guesses and we’ll need to build scenarios to cover the various options.

So let’s start with what we know: the first four films.

What we know: How well did the first four new Star Wars films do?

Let’s be clear on what “know” means here. It doesn’t mean I have a 100% accurate accounting statement of the films. But since we know the box office results, we have a very strong idea of total revenue and we can make really good assumptions on productions and costs. Take that all together and I can make a single estimate for how well each film did.

But we have some explaining about how I got those estimates.

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Hey Cultural Pundits, Don’t Get Cocky: Using Solo’s Demise to Discuss Bad Narratives

I think this one headline captures the surreality of Disney’s box office position:

Solo bombs with $150 million global debut

The headline above uses “bomb”, sarcastically, though most websites eagerly called it a flop unsarcastically. Yet, most films (and their studios) would love to open a movie over $100 million dollars in its opening weekend. Yet to my yet, expectations for Star Wars films aren’t like normal films; we expect them to win the year in total box office. Indeed, as one analyst pointed out, Solo will likely be the first Disney-produced Star Wars film to lose money. (Not Disney film to lose money. Sorry John Carter from Mars.)

(Though, overall, Disney will still make a lot of money on its Star Wars films. Tune in Wednesday for more!)

If our media loves anything more than success, it’s when the successful fail. And not even when you still make money, but just miss expectations. Given the hype of Star Wars, this under-performance begged to be explained (and entice all those Star Wars fans and haters to click). And critics, columnists and Hollywood watchers jumped into the fray.

Here are the reasons I saw for why Solo failed:

– Fans didn’t want a Solo movie in the first place.

– Star Wars fatigue

– Blockbuster fatigue

Solo was released in the crowded summer months

Solo had a troubled production

Solo wasn’t very good

– Solo had a weak marketing campaign

Star Wars fans were still angry about The Last Jedi

Star Wars fans boycotted Solo because of racism

Star Wars fans don’t want movies featuring white men

So which one was it Entertainment Strategy Guy? You’re so good at analyzing things, why don’t you tell us?

Well, it’s some of them. But I can’t prove any of them.

The biggest thing to remember when doing a “post mortem” (or WWW-TALA: what went well-take a look at; or what HBR calls “After Action Review”), is to acknowledge we only have a single data point. Solo came out and did poorly; if it had come out and done well, then we’d be looking for explanations to explain Star Wars’ continued dominance. In truth, one film shouldn’t adjust our priors that much.

Take the explanation above “releasing in the summer time” frame. I’ve seen that mentioned as a reason that Solo is different from The Last Jedi or The Force Awakens. But every single Star Wars film before The Force Awakens opened in the summer and annihilated the box office. Not to mention, two films were just released in “the summer” and had great box office, so blaming Solo’s demise on its summer release date seems like fitting data points into the narrative.

Which is really what I see in most Solo post-mortems: a desire to craft a narrative that probably won’t really hold up to analytic scrutiny. It will read well and tell a good story (and again subtly inform studio execs as they make decisions) but it won’t necessarily be right and it will be delivered too confidently.

Especially when the goal is to distill a failure like Solo into a single cause and say, “This is why it did well.”

The idea that you can distill success or failure down to a single reason is, frankly, lazy writing. It’s also almost never the case. So what I’m going to do with Solo is figure out my best guess for why it tanked at the box office. I’ll do it in two parts: first, figure out what is true, from a data perspective, if I can. Then, I’ll weigh the strength of each explanation. It won’t be as fun narratively, but it will be more accurate/honest.

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Weekly News Round Up – 8 June 2018

Sorry for the delay in posting these. It has been a bit tough getting back up since my family emergency. The goal is to release one of these a week going forward, so fingers crossed. Since it’s been awhile let’s start with the most important story of the last two weeks:

The Biggest Impact of the Last Two Weeks: Solo and the box office “flop”

So I won’t go too far into the weeds on this this week, because I’m thinking of writing a whole article on it for next week. But I do want to explain why this story is so important.

Normally, a movie winning the box office gets a ton of headlines in the Hollywood Reporter or Variety, so you don’t need me to call it out. Same with movies flopping. That said, Disney has a lot riding on the Star Wars franchise. I mean, it was a four billion dollar deal and inspired me to write an entire series on it. And this flop has lowered the floor of future Star Wars films. If audiences really do get tired of these movies, it can limit the ability to make money by billions of dollars. No, really, billions of dollars. The difference between releasing Star Wars movies every 18 months or every six months (or four months) will add up over time.

The Biggest Impact of the Last Six Weeks: Everything “Viacom” related

Everything related to Shari Redstone trying to have Viacom merge with/acquire CBS fascinates me. And clearly is the biggest business story going on. (Hold on a sec for Comcast/Disney/Fox news.) If they successfully merge, but Leslie Mooves leaves, is that a better company? If some how CBS survives with Moonves, what moves do they try to make? Could Paramount get sold at anytime?

Clearly, how this story shakes out will have the biggest impact on the competitive landscape.

Lots of News with No News

Comcast will allegedly put in an all cash bid on 21st Century Fox. I’ll write about that when it happens as in when the bid is actually submitted.

Listen of the Week: Internet a la Carte

I listen to a ton of podcasts, and this one was one of my favorites from The Indicator over the last week. That said, like most great reads I love, it has some flaws. The upside of this story is that I do buy the premise that this is the order in which customers most value internet services. So the survey discussed at the heart of the story is directionally accurate.

That said I have two data complaints. First is one I’ll make all the time: it isn’t the averages that matter but the distribution. Go read The Flaw of Averages to understand what I mean by this.

Second, customers are notoriously bad at putting a price value on something. As in asking them if they would pay $X for something and accepting their answer. Self-reported answers hardly ever matter compared to behavioral data. So its fine to say that people would need to be paid $800 to lose online shopping, but until you actually offer them that money you don’t know if that amount is correct.

Data of the Week

A good example of a host of news stories that don’t really matter is the “who got cancelled; who got renewed” stories. On aggregate, as this link from Vox provides, it is news.

My contention, though, is that each individual story that made up a post on Deadline was not newsworthy. Or at least not enough that you needed to stop you meeting or work to read who got cancelled. So save yourself lots of time and turn of those alerts, and wait until the end of the season to see who got cancelled and who didn’t.

Long Read of the Week

This article from Slate released a few weeks back was one of the most interesting I have read on the media and entertainment landscape. I’d subtly noticed this trend that HBR was becoming mroe and more pervasive by leveraging itself. That said, many b-schools are mimicking this strategy.

Another point: I learned an absolutely ton from case studies while in business school and read almost all the entertainment ones at the time. One of my long term self-improvement projects here is to catch up on new case studies taht have come out since I graduated. My thesis is that the stories and/or data/financials are better than you would guess, providing unique insights to lots of businesses.

 

Disney-Lucasfilm Deal – Appendix: Feature Film Finances Explained!

(This is an “Appendix article” to 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: The Television!
Part VII: Licensing (Merchandise, Like Toys, Books, Comics, Video Games and Stuff)
Part VIII: The Theme Parks Make The Rest of the Money)

So after a planned family vacation and an unplanned family emergency, I’m back with my series estimating how much money Disney has made on the Lucasfilm acquisition. The next place to go is movies. How much will Disney make on the new Star Wars films?

Well…

Listen, I was all set to dive into the economics of Star Wars movies. Then I realized some readers may not know how movie accounting really works (or doesn’t work?). Before I can get into the specifics of these films, I feel like I should explain all feature film economics.

Can I explain it all? Given that some professionals spend their lives working on this and books have been written on it and courses taught on it, no. What I think I can do—what I will try to do—is provide enough of a summary right now that you’ll know how I calculated the movie returns, and you’ll have an idea for how this works.

I also decided that this isn’t really “Part II” of my series. If I were writing a report on this, I’d put this section in the Appendix. You don’t have to know it to get to the conclusion, but you may want to read it. And if you don’t know it, you’d want to read it before Part II. So here is is: my explanation for how film economics works and my confidence in various pieces.

A Brief Movie Windowing Model
A movies’ finances breaks down into four rough areas: costs, revenues, studio fees and back end. They appear (either going out or coming in) in roughly that order, which is also important. (As I wrote in Part I about the time value of money, you can skip ahead if you know this, but you may still enjoy it.)

A note before I start. I call this a “windowing” model, but I’ve heard it called all sorts of things. If you make it before the film is released, then you’d call it a “greenlight” model. It’s called that because you forecast all the numbers to give a movie the “greenlight” to release. It’s called a windowing model because each phase comes in successive windows. Otherwise it could be called an accounting statement for purposes of talent.

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No, Seriously, Why Don’t You Use Data to Make Movies?

If you want to know the “holy grail” for data scientists, I’ll tell you:

Predicting box office performance of movie scripts.

Here’s how it goes. An aspiring data scientist—ranging from bright undergraduate in computer science to a Ph.D. candidate in statistics to even tenured professors—looks for a new topic. They’re bored by analyzing mortgage applications and discover that no one is very good at predicting box office for movies. So they say to themselves, “I can do that.”

Sometimes they even create a model and/or publish papers. Then they go to the Hollywood studios and claim they can use an analysis of a script to predict box office success. Often this is touted alongside advanced analytics, machine learning and neural networks, or other similarly jargon.

We shouldn’t shame these data scientists for trying, though. I mean, the executives at streaming services like Netflix and Amazon Studios/Prime/Video claim they can also use complicated algorithms to predict how well they pick TV shows or movies. Both those streaming video platforms are constantly asked about—and they in turn release vague hints about—the data and algorithms they use to pick TV series.

I have also fielded those types of questions since I helped work on strategy at a streaming platform with tons of data, as I mentioned in my second post “Theme 1: It’s about decision-making, not data”. It typically went, “With all the customer viewing data, how did you use that to pick TV shows?” In my initial post, I specifically didn’t answer the question, but went off on a tangent.

But it is worth answering, because it will illuminate a common Entertainment Strategy Guy theme, “Be skeptical”. In this case, “Be skeptical” of the streaming services claiming they have esoteric data knowledge and the entertainment journalists who let them repeat this.

Of course, I don’t blame the executives per se for claiming they have complicated algorithms. I blame the journalists who repeat it without questioning it. These media members don’t probe that audacious statement. A quick push will reveal those statements to be a house of cards, if you will. (Wow, brutal pun.) In reality, Netflix/Amazon/Hulu/other streaming services and traditional studios don’t have enough data to actually use data to help them make decisions.

So let’s push back, just a bit.

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