Tag: Carousel

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.

Disney-Lucasfilm Deal Part VI – Television Revenue

(This is Part VI 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! Performance, Implications, and Cautions)

So film is dead. TV reigns supreme. We know this.

Except, it’s not?

I mean, we just calculated that Disney made back 63% of their initial investment on Star Wars with four movies, and has many more in the future. At its peak, nothing can challenge the feature film.

Though some gigantic TV shows have come close. Game of Thrones is a juggernaut in ratings, home entertainment purchases and merchandise sales. The top TV shows can command sales figures in the billions of dollars years after their initial broadcast. I’m thinking of Friends or Seinfeld. And not just in America, but overseas, like The Simpsons, which travels well because it is animated.

Of course, now seems like the time to mention that the future of TV isn’t in international sales, but streaming. (That’s semi-sarcastic.) Streaming will play a key role in Disney’s future and, as Disney CEO Bob Iger has put out, new Star Wars series will be a centerpiece of that.

So let’s value two more pillars of Disney’s empire today: adult and children’s television.

TV – Adult

Being frank, this is much more complicated than calculating projected revenue for films. With movies, we know how well they did at the box office and roughly in home entertainment, so we can assume a lot of the other windows. We can also use box office data sets to gauge ranges of outcomes.

We don’t have that luxury with TV anymore. Subscription services like Netflix, Amazon, HBO and Hulu can hide online ratings. They don’t release the costs of the shows. Other windows are more complex than film: Netflix won’t release in DVD if it doesn’t have to, Amazon hasn’t decided, and HBO will release its shows on DVD, merchandise and even sell to other networks. Even Nielsen data is available, but expensive. (I don’t have it since I’m not in a corporate setting.)

I don’t know what Disney will decide, which makes calculating the value for television series so difficult. Given the variability in the rest of the model, I’ve had to make some simplifying assumptions.

To start, how many series will Disney make? I’m going to assume that since Disney has said they are working on a “few TV series” this will mean three series released per year from 2019 (when the service launches) to 2021. And we’ll give each a three year run. Likely, some will do better, some will do worse. (Better meaning 5+ seasons, worse meaning one season. In between is 3 seasons.) Since they’ll keep making TV series, my model has two new series premiering after that through 2028.

Read More

The Economics Behind Not Making More “A Star Wars Story” Films

I love Han Solo.

To put out my Star Wars bonafides—as if proving I’m a hardcore nerd makes me cooler—I’m the type of Star Wars fan who read the both the Han Solo trilogies, one from the 1980s and the other from the 1990s. Don’t believe me? Here’s my collection of just Han Solo books dating from again 1980 to 2015:

IMG_3549

So it isn’t a coincidence that I chose the Disney-Lucasfilm acquisition for my first “analysis article”. And it’s partly why I can’t stop writing about the disappointment of Solo: A Star Wars Story at the box office. As a hardcore Han Solo fan who loves the business of entertainment, sort of combines two loves into a just overall intriguing topic.

Today’s article is a response—in as near real time as I get—to the Star Wars issue of the day: the future of Lucasfilm’ business slate. First, Collider revealed that Lucasfilm was putting spinoff films on hold. Then The Hollywood Reporter clarified that Lucasfilm was just being careful with future development. Then other outlets jumped in to comment or repeat the news.

I’m not a traditional journalist so I don’t have those inside sources. But I do have expertise in the business ramifications of these decisions. And having spent the last four months analyzing all of Lucasfilm’s finances, I have a complicated model on how their movies perform. This model will allow me to answer some questions about why Disney is slowing the pace of production for Star Wars.

Questions sound like a good way to go. So I’m going to set this up as a fictional Q&A:

Question: Can you explain in one chart why Disney/Lucasfilm are slowing development?

Sure. Here’s the “hockey stick” shape of box office performance from my article on the economics of blockbustersslide-17-e1529621369496.jpgThe take away is pretty simple: the spin-off movies are doing about half as well as the “episode” films. If you look at this chart, you’re tempted to say, “Hey, we should just make Episode films only, and not make spin-offs.”

Question: Do you buy this explanation?

Not really. We’re in the realm of small sample sizes. Five movies, to be exact. So just assuming that calling something an “Episode” film will make it perform better doesn’t make sense. We need a deeper explanation of the underlying forces.

Question: What are the underlying forces?

Well, as tried to model, the “economics of franchise blockbusters”. I created a comparable films data set of 75 films, but then trimmed it down to answer specific questions. I modified it in four different ways: Star Wars films since the beginning, Star Wars films since 1999, all blockbuster franchises since 2008 and finally any franchises that showed “fatigue” since 2008. Again, I used adjusted US gross to even these out. Here are those four categories:Slide 28The takeaways of this are that Star Wars does really well. In just the films since 1999, 43% have been “super-hits” (over $700 million in US adjusted box office). But franchises that start to show fatigue, didn’t have any super-hits. So we can see that keeping box office performance strong can have a tremendous upside.

Question: Can we quantify what the upside is?

Yeah, I can. Here’s the financial models I made for my “comparables” each level:Slide 23As you can see, a super-hit isn’t worth just a bit more, it’s worth over four times as much as just a “median” blockbuster hit. (And flops cost you $40 million or so dollars.) In other words, you can attempt nearly 20 franchise blockbusters to try to get a hit.

(One note, the comparable numbers are lifetime numbers. In my model I condensed revenue to a four year period, but the point is Disney, with the blockbuster that is The Force Awakens will make money off of it for decades with resales, DVD sales, or putting it on its own SVOD platform. My numbers seem high, but for a film with a huge box office you make money off of it for years.)

Question: So can we combine the performance and comparables models?

Absolutely, and we get the “expected value” per film. In other words, if you make a franchise blockbuster, what is the expected value? Here is the a combination of the two charts, with the expected value.Slide 38Calculating “expected value” is easy, just multiply the probability of the various performance by the net profit. Once you do that, you see that if Star Wars can sustain at it’s historic level, it’s worth $731 million per film, whereas if it decays into franchise fatigue, that’s only $284 million per film.

(Side Question: Does this expected value apply to all blockbusters?

No! My franchise blockbusters are films in an already established franchise. Attempts at new franchises (which is what most blockbusters are nowadays) can differ in two ways. First, when they lose money, it isn’t just $40 million dollars. Even Solo had a gross of $375 million. And it is lumped in for sales with other Star Wars films. Flops like John Carter from Mars or The Lone Ranger can be in more of the $280 million dollar range. I was going to put The Mummy from last year in this, but it still did $400 million in total box office globally, with only $80 million in the U.S.

The other factor is the new blockbusters have a naturally lower hit rate then established franchises. These two factors make blockbusters riskier than established franchises, but less risky then lower budget films. This table is why, though, I said that Legendary could be doing “moneyball” with movies. They have seen this math, so their key was getting enough capital (billions) to back large movies like this to see the return. Back to the questions.)

Question: So Lucasfilm can’t lose money on these movies? Is that what you’re saying?

In the aggregate, yes. If you make enough films, and your movies perform according to the historical pattern, yes you won’t lose money. Of course, once “franchise fatigue” sets in, a film studio will chose not to keep making as many movies. And if it were on a true, sustained downward trajectory, then the value could drop further. Moreover, all these expected values are just one input into a model with a lot of uncertainty. If you deliberately made a lot of bad movies, yeah Lucasfilm could figure out how to lose money.

Question: So knowing this, how many Star Wars films should Disney make?

To answer that, I really need to go back to my model. (I explained how I got these numbers here, here or here.) To figure out how much money Disney will make on Lucasfilm, I needed to model the expected value over the next ten years. This needed to account for different production issues and different box office performance. Put it all together and I got 8 scenarios. This chart is new, though, and only calculates what Disney could make on Lucasfilm movies over the next ten years, discounted back to 2018 dollars.

Pic 7.jpegWhy discounting to 2018? Well, we’re looking forward so unlike my analysis that is partially backwards looking, we’re evaluating the value of Lucasfilm into the future. Since 2018 is the year Disney/Lucasfilm has to make the decisions about new films, that’s when we need to time our dollars.

(Question: Does the above chart include Episode 9 or Indiana Jones 5?

No included them in my overall analysis of the deal, but not here since I am assuming Disney is already committed to them. They’re sunk costs so don’t effect the planning for the rest of the future Star Wars films.)

Question: Okay, that’s a lot of numbers, any takeaways?

Yeah, first Disney doesn’t lose money in any scenario going forward. In the one scenario where they could—making a ton of films as fatigue set in—they would pull the plug on that course of action, as they are contemplating right now.

But the biggest takeaway is that sustaining the “Star Wars is Star Wars” scenario is more valuable than any production decisions. Accelerating to “MCU style” definitely has higher up side, but if you can’t hold onto the audience, then you lose 40-66% of the value. Even a troubled production slate (“Issues”) achieves a higher return than going to MCU-style if you can keep “Star Wars is Star Wars” at the box office.

(So if you’re a Star Wars fan who’s disappointed in Disney for making too many Star Wars films, here’s you counter-argument to show to Disney execs. Say, “See, Disney if you make fewer films you can make more money.”)

Question: Do you buy this?

It all hinges on is correlation. Is the number of films causing the franchise fatigue, or is it unrelated? The model I have here shouldn’t be interpreted to say that franchise fatigue is caused by increased film output. I consider these two things independent in the scenarios.

That said, as I wrote in the Solo: A Star Wars Story post, yeah, I do think they are related. There is a reason why only one franchise has been able to release multiple films per year and not decay, and that’s Marvel. (And that reason is Kevin Feige.) Otherwise multiple films in a year or even more than every three years tends to yield a decline at the box office eventually. In short, hardly any film franchises can sustain sky high box office if they release a film every year.

Question: You just said that franchise fatigue is both related and not related to the number of films. So which is it?

The better way to say it is franchise fatigue is related to film quality. To go back to the Marvel example, the films keep being excellent. Dr. Strange is the worst reviewed movie in 18 months, and was still well-received. Before that you’d have to go back to Ant-Man. So when Marvel says we can do more movies and keep them great, then franchise fatigue doesn’t set it.

The last two Star Wars films both had mixed customer or critical reactions. That’s what causes low box office more than anything. Increasing the number of films increases the odds of more bad films, which causes fatigue. Very, very few franchises can keep the quality sustained at a high rate for that long, besides Marvel (and again Kevin Feige).

Question: Any other concerns with the model?

Well, the “Star Wars is Star Wars” version is a very small data set. Only 10 films so far. And it is a franchise that is unique in that there were two large gaps between initial films and the prequels (15 years) and the new films (10 years). This built up massive enthusiasm. This is why I don’t model another hit the size of The Force Awakens. I think Disney knows meteoric hits are rare, and as dependent on quality as they ever were.

Question: So after all that, why is Disney making this decision from a business perspective?

Disney is slowing to one film per year, roughly, to keep the quality high. It will also avoid risking bad films and hence franchise fatigue. At the same time, decreasing below that rate leaves money on the table, as our expected value chart and scenarios show. So expect Disney to keep one film per year.

Question: Do you have any other concerns?

Well, the worries about fatigue will only amplify is Lucasfilm releases one or multiple TV series aimed at adults on Disney’s new streaming platform. Even dialing back the movie releases won’t mean Star Wars isn’t in the culture. If the TV series aren’t good or the marketing machines over hype the new series, cutting back on the number of films may not matter.

Also, the TV shows bring up another issue I haven’t really addressed yet, which is the other lines of business. Keeping Star Wars relevant impacts the toy sales, the streaming service, the kids TV and the theme parks. That’s another reason to focus on quality movies.

Question: What is the biggest assumption in this analysis?

The big assumption, and I assume Disney believes this to be true, is that development executives at Lucasfilm can indeed improve quality by slowing down production. Of course, that assumes that development execs are skilled at development and can indeed make better movies, they just might have been spread to thin.

I actually don’t believe this. Assuming that you can simply make better films with the executives you have by trying harder…isn’t really a data-based position. I have a ton of thoughts on how to improve development—most of which Hollywood doesn’t do—but not enough time in this article. Keep reading this site and I’ll get to it.

Question: Do you have any creative concerns?

I do, but this is me putting on my “development executive” hat.

As I was researching Solo I read that Rian Johnson’s new trilogy due in the 2000s will close out Rey and Finn’s arcs. While critics praised The Last Jedi, they tended to gloss over the larger plot: the Resistance lost. I mean, the Resistance is basically 20 people on a ship and the First Order took over the galaxy. With such a deep hole, and knowing that Rian Johnson is making three more films to close out the story, the key question for Episode 9 is this:

Will the good guys win?

If they don’t, and it ends on another “cliffhanger” a la Empire Strikes Back, I think you could see another negative fan reaction. Or at least, it will have to be a pyrrhic victory, where the good guys win, but the First Order is still in charge.The fans want closure to this arc; if they don’t get that, it could impact future box office. This is one of those creative decisions that could impact the business, but is super hard to model.

 

Question: Fine, just for fun, what would you do if you were Lucasfilm?

Make a Tales of Mos Eisley TV show. It’s still in the core world, but exploring possibly the best short story collection in the larger Star Wars universe. Which is to say, spinoffs shouldn’t be dead quite yet.

Disney-Lucasfilm Deal Part V: Movie Revenue – The Analysis! Performance, Implications and Cautions

(This is Part V 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! Performance, Implications, and Cautions)

Have you ever been on a long hike? Your friends tell you, “Let’s go on this beautiful four mile hike to the top of a mountain with a scenic overview.” So you say yes. You go. It’s long, but you keep trudging.

Midway, you start to count your steps, anything to avoid thinking about the burning in your legs. You imagine the joys of finishing. It just keeps going.

Finally, mercifully, you finish and you’re at the top of the hill. Man, the view is worth it.

Gorgeous.

Then it hits you: you have another four miles to get back down.

It was four miles each way.

That’s a test of fortitude. I bring this up because this article just keeps going. Every time I think I’m finished, well I find another fascinating principle to explain. The “movies” portion alone has kept me busy for four parts and a bonus appendix. (And maybe a bonus article next week on this topic.)

Last time, I explained how I built 8 scenarios forecasting the future of Lucasfilm’s film slate. Previously, I explained how feature film economics work, explained my financial estimates for the first four Star Wars, and explained the economics of blockbusters, which developed a set of “comps” for franchise blockbusters. I brought them together in my 8 scenarios. Today, we’re bringing it home to analyze the results of my model. (See the links at the top.)

Really, this is the fun part. The hard work was building the model and getting some good data to make it accurate. That was the hike up the hill. Let’s gaze out at that gorgeous view, to drive the above analogy firmly into the ground.

Overall Performance

The best way to summarize our 8 scenarios is to calculate the net profits for each outcome. (In my model so far, gross profits are revenues minus costs, and net profits then subtract talent participation.) I’ve discounted them all back to 2012 acquisition dollars since that is when Disney first acquired Lucasfilm:Slide 33Those are a lot of big numbers, so another way to look at it is in percentage terms of the acquisition price ($4.05 billion with a “b”). Here’s that:Slide 34Wow. So in 5 scenarios, Disney makes all its money back with just this line of business alone. Further, even if they only achieve “Blockbuster Average” at the box office, they can even have production issues and still get 94% of the initial price. At worst, with production issues and a franchise fatigue, they still make back 82% of the initial deal price ($3.3 billion in adjusted dollars).

Still, I can hear you, “Entertainment Strategy Guy, just give us a single number! Tell us the average!”

In scenario modeling, the “expected value” is the closest thing you get to an overall “average”. To give that to you, though, I need some probabilities. These scenarios aren’t equally likely. It’s more likely that Lucasfilm has production issues then it is they ramp up to 14 films per year. It’s also more unlikely that “Star Wars is Star Wars” at the box office, as opposed to franchise fatigue or just general underperformance.

So here is how I would rate the probabilities of each scenario. And to simplify things, I gave each scenario it’s own name to make it easier to refer to. And it’s more fun. So how likely are each scenario?Slide 35Here’s the thing: I don’t have great rationale for the production side of the equation. I had started with MCU-style at 20%, but given the vague post-Solo rumors, Lucasfilm put the spin-off movies on hold. That makes me think they will strive for the one film per year rate. For the performance, I think the odds that franchise fatigue truly sets in is about as likely the high case where Star Wars defies box office gravity forever, especially if Lucasfilm determinedly releases 1 film per year. (Remember, before this, Lucasfilm had a 15 and then 10 year gap in films.)

I used my judgment for these probabilities, but I could tweak them if I found better data to make the forecasts. So with the probabilities of our two inputs, we could calculate the probability of each scenario.

Slide 36

The odds of “Gangbusters”, the scenario where “Star Wars is Star Wars” at the box office while moving to two films per year is only 2%. That makes sense; each is unlikely. On the other hand, the idea that franchise fatigue sets in and they have production issues (“Worst Case”) is 5%, which feels right.

In the downside, in “Burn Out” or “Sub-Optimal” Disney doesn’t make their money back. On the other hand, Lucasfilm makes money in the “Base Case”, “Make Money”, “Make More Money” and really cashes in with “Gangbusters”. They also make money in “Missed Opportunity”, though it contains a good lesson that production issues can really leave money on the table.

With our probabilities and the returns, we can now calculate the “expected value” of Star Wars movie revenue going forward. To take this all the way back to the introduction from yesterday, I can’t tell I “know” how well the films will perform from here on out. (Again, that quote.) But I can tell you this is what I expect just the movie portion of the deal to be worth, in financial terms: $4.3 billion in 2012 dollars from 2015-2028. In other words, I expect the movie net profits, after costs and talent participation, just the movie revenue, without toy merchandise sales either, to account for 106.8% of the value of this deal through 2028. In just one line of business, Disney made its money back.

Slide 37

It does strike me that my “base case” scenario is fairly close to the expected value (only off by about $40 million, or less than 1%. So was it a waste to build all the scenarios? No. Those numbers represent two different things, though it does give me confidence in using the base case in an “average total model” when I finish all the lines of business.

Implications

Overall performance isn’t the only thing we can learn from this model. The great thing about a scenario model like this is we can learn some things from it (assuming our math is right, the data is accurate and representative and our assumptions are reliable):

“Franchise blockbusters” have a low downside.

This was a conclusion from “Part III”, but bears repeating. Blockbusters based off preexisting movie series can usually put up something of an opening weekend and make some money. This doesn’t apply to brand new blockbusters, who can lose a whole lot more when they completely flop (I’m looking at you, John Carter of Mars and The Lone Ranger. I wonder what studio made those?)

For franchises—movies in a series based off preexisting IP, for lack of a better definition—the downside really is limited. For Lucasfilm, you can see why they’ll keep making Star Wars films: it’s a low downside risk. (If you’re a Star Wars fan upset at Disney making so many films, well this is your explanation.)

We can quantify this per film with our “expected value” chart as well. Here is the net profits from the green light model per comparable level. Now we can multiple these by expected probabilities:Slide 38For a franchise blockbuster, like Star Wars or Marvel or Harry Potter, the studio can expect $392 million in expected revenue if it performs like past films. Now they will hardly ever get exactly that, but in a portfolio of films this is what they can expect to earn. (For Star Wars fans who think Disney is driving the franchise into the ground, this is your financial explanation of why.)

2020 is a big year for the model

I put most of Star Wars: Episodes 9’s revenue in this year because it will come out in December of 2019, so Disney won’t collect the cash until 2020. Combined with an Indiana Jones 5—if it stays on track—then the total revenue in 2020 is $2.7 billion. That’s a big year. Combined with a 2019 or 2020 release of a new TV series and Lucasfilm has a lot going on.

The time value of money starts to have a strong effect.

The time value of money has a real effect. Take the $2.7 billion in 2020 dollars. Well, discounted back to 2012 dollars, it’s only worth $1.6 billion.

Assuming Rian Johnson’s first new Star Wars movie comes out in 2021 (the year after Indiana Jones or the Christmas of 2020), if you booked all the profit and loss the next year (2022), well you would only earn $0.50 on every dollar in 2012 dollar terms. Again, not that Disney wouldn’t collect each dollar, but if you’re evaluating the deal in 2012 terms, ten years down the line a dollar is only worth half in 2022 what it was worth in 2012 when discounted at 8%. The easiest way to understand the time value of money (for me) is to multiply the money earned by the discounted rate, as shown here:

Slide 39That’s why literally more than half the value of this deal in terms of movie revenue was baked in when the first four films came out, again when evaluating in 2012 terms. Essentially, huge box office returns from the first three films locked in 63% of the adjusted price. A huge Episode 9 and big Indiana Jones 5 (which I assume in half of the scenarios) yields another 23% of the initial price. By 2028, if the series performs on blockbuster average, the last seven movies only make 20% of the initial price of the deal. Here is how that looks by scenario:

Slide 40In terms of our initial question, it basically says that Disney would have to do a lot wrong with the franchise to NOT make money at this point. It’s possible, just much less likely.

Cautions & Criticisms

I’m not perfect, so naturally I can look at my model and give some critiques. Let’s keep these in mind so we don’t take this model as 100% gospel truth.

Be ready to update your priors…especially with small sample size.

If it seems like everyone is over-reacting to Solo’s disappointing box office, well I disagree. Essentially, I think Solo: A Star Wars Story caused a lot of people, myself included to “adjust their priors” to use Bayesian/Nate Silver-ish talk. We assumed Star Wars films didn’t have flop potential when they clearly did. Solo: A Star Wars Story changed my preconceived floor of box office performance for this franchise. I changed my whole model based off that event. That’s worth the conversation.

And it should provide a warning. Do I have other prior assumptions I haven’t captured in this model? I tried to call out as much as possible, but it’s always the concern. Off the top of my head, a big potential swing in value is the probabilities assigned to each scenario. If instead of my probabilities you just assigned an equal likelihood, then Disney would earn $4.6 billion, or 115% of the initial price.

(Of course, there is always the fact that my model might not match the actual performance. I did a little searching for research on other projections of Star Wars revenue/profits and I’ve found a range of numbers. So again, I went with my best judgement.)

This current expected value isn’t the expected value at the time this deal was signed.

Don’t mistake the current estimate of value ($4.3 billion in 2012 dollars, 106% of initial price) for the expected value at the time this deal was closed.

In 2012, at the time Disney signed this deal, a new trilogy could make over $5 billion in total box office, or it could have made “only” a billion dollars. Or somewhere in between. That’s immensely reasonable. Now, Lucasfilm and Kathleen Kennedy hired the right director for The Force Awakens and that didn’t happen.

Evaluating the deal midstream, we get to look back with hindsight and look forward with our forecasts. But that doesn’t mean the performance for the last five years was guaranteed by any means.

Is “franchise blockbusters” the right data set?

This is the toughest part of the process and I’ve seen really smart people make really big mistakes when it comes to finding the right data set of movies. So I could see quibbles with my data set of “franchise blockbusters”. The worry is I biased the data set positively for Star Wars.

As I wrote before, my notable omissions were Jurassic Park, Star Trek, Fast and Furious, and James Bond. The last two I have no qualms leaving out since they don’t have the kid appeal of Star Wars or Marvel. Same with Star Trek overall. The toughest was Jurassic Park, which would mean Jurassic World, so it’s one movie, but a “super-hit”. Again, I’m fine with this, but I’m monitoring those franchises for future data analysis. (If you’re curious, if we had added all the omitted franchises, it would have pulled down the average for hits and super-hits.)

There is no “terminal value”.

In other words, why did I stop modeling at 2028? Short answer: uncertainty. After ten years, models lose almost all their predictive value.

But won’t these films keep being made on into the future? Don’t I need to account for that? Yes. And I will, when I put it all together. I plan to do that for this deal, but not until the last step when I do it for all the business lines at one time.

Final Questions

Please, can I see the unadjusted gross profits?

Sure, since you asked nicely. Again, I don’t think we should lead with this because they are misleading. The raw numbers just look better because they look bigger. (I’m losing clicks I know by not leading with this in the headline. But fine, here they are: First, the total revenue from 2013-2028, unadjusted:Slide 41Do you learn as much? Again, I don’t think so because I think our brains translate that money into current price/value and not the true scaled price. But again it looks huge.

What is the current value of Star Wars movie revenue?

That’s a good question. First, I’m going to give you the unadjusted revenue for the next ten years. To show you how it can mislead you:

Slide 42

I put the expected value below it. So you could look at this and say, “Look, Lucasfilm is still worth $4.2 billion! In just movies!” But again, let’s discount for the time value of money. But instead of discounting to 2012, we need to discount it to the current value.

Slide 43

In this case, we see the revenue is “only” worth $2.5 billion for the next ten years. Still really not bad, but not as well as the initial deal did. Again, a lot of that is drive by the fact that most scenarios only have one or two “super-hits” whereas Disney came out and delivered three of those in a row.

Where do we go from here?

Back to the analogy. We just finished admiring a gorgeous view of financial prosperity. And we learned some things. But now we need to walk back down the hill.

The movies portion has long been the centerpiece of this deal from a financial standpoint. At least, that’s what I expected when I started on the model and remain convinced by after all this work.

But Disney is so much more than just movies. Toys. TV. Theme parks. Our walk down this hill to arrive at our final conclusion needs to analyze each of those lines of business. And I’ll do that next.

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.