Category: Ideas

My Questions for Netflix’s 2019 Q1 Earnings Call

I did something fun for the first time last week: I emailed questions for a corporate earnings call. Obviously, it was Netflix.

I’ll let you know why.. (And I’m under no illusions that I’ll actually have one of these questions asked.) Normally, if you asked me if earnings calls matter, I’d say no. Sure, the letter to shareholders will have some data, and the quarterly reports matter to investors, but the presentation is the most self-interested presentation imaginable. It would be like listening to just the closing statement of the prosecution in a trial. You’d get a lot more guilty verdicts, don’t you think?

But I have a much larger project I hope to unveil sometime this year where I make a “power ranking” of streaming/bundling services. From ad-supported to sports, anything digital video I will rank in one definitive list. Like sports power rankings, if you go to ESPN or any sports website nowadays.

To build that ranking requires good information, like all good decisions. And right now the company that has the most black holes in data, for me, is Netflix. Since I’ve written about their data and even coined a phrase about how selectively they pull it (read here for “datecdotes”), I naturally had the most questions for Netflix, and they convinced me to finally write an email.

To be fair—meaning unbiased across all digital video companies—I hope to roll out this type of feature semi-regularly with other digital video companies. Google, Apple and Disney are the most relevant, though Disney gets a brief reprieve with all the information they dropped on us last week. Youtube deserves a ton of questions and so does Apple with their paucity of information.

With that preamble, onto the questions. I have three big areas: Viewership (to see how valuable their content is), activity (to gauge how subscribers interact with the site) and subscribers (to probe their business model a bit). After each question, I’ll explain my reasoning in parentheses. These explanations I didn’t send!

Viewership

– In the last earnings call, Netflix reported that Bird Box was viewed by 80 million customers over the first four weeks. During that time, was it the most viewed movie on your platform? Over 2019 as a whole, was it the most viewed movie on your platform? Have any Star Wars, Marvel or Disney Animated films had more viewers than Bird Box since their respective launches?

(As we look to the battlefield of 2020, churn is the name of the game. Is the most popular content on Netflix leaving? I believe it is with either Friends (or other long running TV shows like it) or all the Disney content. This question helps get at that for the movies side, especially the Disney content.)

– In the Q3 earnings call of last year, you said that 80 million unique customer accounts had watched one or more “Summer of Love” romantic comedies on your site, was that using the same standard as Bird Box, where you counted “watched” as 70% completion of a film?

(If Netflix answers this, I’d be shocked. My guess is they moved to the 70% threshold after minor pushback on their Q3 report. They knew they had to explain the calculation, but waited for a film that did well enough, like Bird Box, to justify it. Still, if they say, “No”, then that “Summer of Love” number can be severely discounted. Likely they won’t ever answer either way.)

– How many people watched The Christmas Chronicles? Or The Ballad of Buster Scruggs or Private Life? How many hours have customers viewed for any of this content? (You reported in the last earnings call that you do track hours viewed on site.)

(Again, this is to help flesh out the context of whatever numbers they do release. And the scale of losses. This is the best example of how one-sided an earnings report is. If there were a “defendant” making the bear case, these are the numbers their defense lawyers would seize on to make their case, to continue the prosecutor announcement from earlier.)

Activity

– In 2018, what was your monthly active users? What has been your monthly active users in 2019?

(Monthly active users is the metrics that “feels” right for me when it comes to truly understanding the people who love your service. I don’t have data, but my gut that it explains usage best. Monthly users are the people who devoured some piece of your content in their entertainment diet. Subscribers is not that. If I were “entertainment czar” all streamers would have to release this.)

– You reported the service “averages” 100 million hours a day of viewing in the US in a month. How much does that average vary by month? What does the time on site distribution look like by customer decile? What was the annual daily average?

(We all hate averages, don’t we? Well I do. They don’t tell use anything. And since someone quoted the “2 hours per day” number to me for Netflix usage recently, it made me want to know a lot more about it. Also, related to this is the variance overtime. December happens to be a huge month for Netflix, so touting numbers from December is deliberately overselling the annual performance.)

Subscribers

– In your Q4 report, you mentioned a net add of 29 million customer accounts. What was the number of gross adds versus net? How does this breakdown internationally versus US? You used to report gross adds in 2011, why did you move away from this metric?

(I didn’t know Netflix used to report this, and this is the type of number they should report, if you follow the standard, “Does the CEO get this information?” Because Reed Hastings definitely does. [I love that standard, by the way.] Again, churn is the name of the game, and the great thing about Netflix’s 60 million or subscribers is that it grows steadily every year. Which gives an illusion of stability the gross number would help understand. International is even more curious for me.)

– What is the total unique subscriber base you have had in the US since you launched streaming?

(My final way to get at the churn questions. Say Netflix had had 140 million unique subscribers in the US since launching in 2008. Some of those are duplicate accounts—people who signed up, then switched—surely. But some aren’t. That gets to the idea that it isn’t like Netflix is convincing people to try Netflix for the first time, but to come back. Which is fascinating, to me, and a different business challenge.)

Value Chains…Explained!

2019 is off to a great start for “maps of the entertainment universe”. When I was writing on media consolidation, I wanted to make one of these to help explain this crazy industry in better detail. But I doubt it would have looked as good visualization as this Wall Street Journal visualization I saw on Twitter last week:

WSJ Map of Universe.png

Source: Wall Street Journal

What I love about that image is that it conveys multiple pieces of information in a 2D fashion. That’s really hard to do. That’s the gold standard of charts/tables/maps. This image conveys the names of companies, types of entertainment they offer, who competes in multiple areas and who doesn’t. Good job.

But while I love that lay out for what it does, it still has limitations. Mainly, you don’t know the directions of the various branches. Are they all separate types of services or are they interrelated? Are some companies distributing the other types of entertainment? How do they relate? How distinct are live TV, ad-supported and sports content anyways?

So while that visualization is good, it is still incomplete. (To be clear, I liked the image. A lot.) I want to build on that chart and others, but to do so, I need more tools. My goal is to explain the business of entertainment, and to do that requires first explaining some of the tools I plan to use. These tools explain not just what the companies are competing in, but how they compete and relate to each other.  Today, I’ll explain the “value chain; tomorrow, I’ll explain a different tool. Then we can build our own map of the entertainment universe. 

Note before I start: These two tools are both super complicated. My explainers do not do them justice. I mean chapters and chapters in strategy text books have been written on this. And I felt I needed to reread those chapters in my still saved text books as refreshers before writing today. So at some point I’ll make a reading list if you’re interested.

Value Chains

Let’s start a series of shapes. 

A shapeOkay, that didn’t help. Let’s add three words to make the simplest value chain imaginable:

Image 1 Simplest

The “chain” here is the journey of a product to a customer. Essentially, someone makes a good. They sell it to a store who sells it to a customer. If each step “adds value”, that’s your “value chain” in action. (What if someone doesn’t add value? Well, they’re still here in the value chain. That’s what we call it.) Here’s a pic from my strategy text book, just vertically rotated:

IMG_4437

Same really simple principle.

In my experience, good strategy starts with this core tool. Even if you think you know your industry top to bottom, back to front, you should still use this tool. First, it’s a good refresher to challenge how your industry has changed over time. Second, as new industries emerge, you can use this tool to understand their emerging value chains. In my previous role, when I dug into a new business opportunity, I would sketch out an initial value chain and use that to figure out how to research the new industry. It never failed to generate some insights.

(An aside, yes, I’m explaining a “strategy 101” concept here. The basics of industry analysis. Some super smart executives definitely don’t need me to teach this to them. If you’re one, skip ahead. That said, at three different companies, I never saw this tool used. Even as business models changed rapidly. It’s a basic yet powerful tool, like value creation. And I doubt business affairs, creative development or production executives have ever seen it.)

The value chain is a little tricky for digital goods. It started its life as a tool for manufactured goods. You know, the hey day of American might and exceptionalism in the 1960s. So I’m going to explain the tool with a manufactured good. Trust me, we’ll get to digital video.

Let’s use a delicious example, potato chips. This will pair nicely with my example for value creation, craft beer. Throw these two articles together and you got a party. The first step? The potatoes:

Potatoes

How do farmers add value? Well they grow a crop that wouldn’t exist if they didn’t. Harvesting the raw supplies. But now we need someone to come in and turn potatoes into something:

Potatoes and ManufacturersThe manufacturers add value in two ways. First, by turning potatoes into chips, they make a tasty treat. Then, they also pay for the marketing to make you want to eat that tasty treat. But potato chip companies like Frito-Lay don’t own all the stores. And often don’t have the distribution to sell everywhere. So they use:

Full Value ChainThe distributor has the logistic excellence the factory doesn’t to get the chips out there and the store has customer service and variety of products. (Again, in an ideal world.)

You can see that you start with potato farmers, who sell to potato chip manufacturers. They sell to distributors or wholesalers who sell to stores. At each level, they take their margin, so that a potato sold for say $0.25 becomes a $3.99 bag of chips at the store. In an ideal world, each level is “creating value” for the end consumer. The potato farmer creates a potato that wouldn’t exist otherwise, the potato chip manufacture transforms it to make it delicious using special techniques. We need one last piece though.

True Full Value ChainI like adding customers even though it is redundant in someways because it clarifies if the end of a value chain ends in customers or a business. And I always believe in reminders of the value of the customer. 

I’ll make one last point. I mentioned that the problem with the Wall Street Journal article was that it didn’t explain the relationship between the layers, and the value chain doesn’t either. What it does is explain what pieces feed into what to make a final product. So we need to start adding some numbers to get this thing rolling. And since I mentioned value, ideally we’d fill out a chart like this:

Value Creation Chart

Again, this is a vertical form of the horizontal value chain from above. (I’ll explain tomorrow why I left it horizontal.) But now if you can estimate the “willingness to pay” for customers, and you know the values of potato, then you can figure out all the prices and see who is capturing what value at what level. If I knew the actual numbers, I’d fill this out, but don’t want to lest I get something wrong. But you can see where you would put the numbers in. 

The next key insight, though, comes from using those numbers to see who captures the biggest share of the pie. (Technically, PIE, but do not have time to explain that in one article. That’s some math.) If one level captures an inordinate amount of the value, well, they are pretty powerful. 

Well, how do we explain that? With another tool for tomorrow.

Other Ideas/Notes

First, if you search “value chain”, or go to Wikipedia, you usually get this phrase tied to Michael Porter’s “value chain”, which is about a company internal value chain. That is specifically not how I am using it and again my professors teaching me were using value chains to discuss larger industry analysis. Which is how I use it. (And we’ll get to Porter tomorrow!)

That said, I do love always thinking about creating value and the similarities between a company and an industry. And this type of value chain analysis can be insightful. For entertainment, a development exec adds value by finding a great project, business affairs gets the talent signed for good prices, a production exec adds value by producing it on time and on budget, finance gets the money on time to pay for everything, and then marketing gets customers to pay for it. 

Second, value chains don’t always have one straight line. You can sell to multiple distributors, with their own value chains and outputs—say liquor stores or super markets or online—or have multiple suppliers—how many things go into a car, for example? But still, understanding them at a high level is usually useful. 

Why I’m Unveiling These Tools Now

I don’t like referring to strategic concepts that are even slightly advanced if I haven’t explained them. And on Thursday, I’m going to need to use the value chain to explain my next HUGE analysis article. (Analysis articles are where I use numbers to draw definitive conclusions. Like my series on Lucasfilm-Disney Acquisition, M&A in entertainment or the Pac-12. Just look to the column on the right for ideas.) I’m not telling what it is, but it involves dragons, orcs, talking lions, white walkers, rings and multiple media companies.

Also, the definition of digital video”, it seems to me, needs a better explanation. Understanding the value chain helps get there. The roll out of Apples Plus/TV product two weeks ago seems to necessitate better explanations of digital video’s value chain.

Oh, and last week, I mentioned value chains with agents! So if I see value chains all over the place, and they need explaining, well, I’ll help out.

Prediction Time: Forecasting the Effect of Netflix’s Price Increase on US Subscribers

Netflix moves the PR needle. Even I jumped into the Twitter maelstrom to generate clicks based on their two announcements last week, especially the decision to increase prices on US customers.

The problem, for me, is that Twitter, as a medium, is really bad at digging into numbers. It isn’t Twitter’s fault; spreadsheets just don’t really fit. (See my last big analysis article for another debate taken off-Twitter.)

As a result, a lot of the “debate” on Twitter devolves into “this is good” or “this is bad”, with some anecdotes thrown in and the occasional Twitter rant. The fun thing in the #StreamingWars2019 is we’ve all clearly taken a side and this war will only end with all our heads on pikes. (I’m rereading Game of Thrones/ASOIAF in preparation for April 14th and George R.R. Martin ends lots of events with that outcome.)

We can do better than Twitter debates. Today, I want to make the subtext of all the discussion on Netflix text. I want to change the terms of the debate around Netflix by moving into concrete specifics. Strategy is numbers, right? 

That means putting our predictions into quantitative terms. I described my process for this regarding M&A back in July and my series on Lucasfilm. So here’s the question:

How will Netflix’s price increase in 2019 impact US subscribers in 2019?

The results will come in when Netflix announces their annual/quarterly earning in January 2020. For the record, Netflix currently has 58.5 paid memberships at the end of Q4 2018, among three tiers of pricing. Over Q1 and Q2 of this year, they’ll increase prices $1 to $2, raises of 13-18%. 

I’m going to walk through my process to make a prediction. First, I’ll explain why I’m predicting customers in 2019, not other financial factors. Second, I’ll evaluate what we know and some good and bad ways to look at the problem. Third, I’ll talk a bit about the data and finally make my prediction. Feel free to leave yours as a comment on this article or in my Twitter feed.

Stating the Problem: If the number of subscribers who leave is lower than 18%, it’s a win.

This is the simplest of simple microeconomics that Netflix is practicing here. If you raise prices, but the units sold (in this case, customers) decreases less in percentage terms than the price increases, you make money. (Assuming no increases in costs.) Since this is digital and each additional “unit” sold has a marginal cost of zero, that math works. (Note: this is still an “assumption”. If you continue to need a larger and larger content library to woo subscribers, well then our magic “marginal costs is zero” isn’t actually true.)

economics model

Source: EconomicsHelp.org

Like the “value creation” model, the above chart is the simplest explanation of price and supply and how they interact, but it is woefully incomplete. Many, many other variables ultimately impact the number of units sold or customers who subscribe.

Yet, as rule of thumb, it works. The number, therefore, to watch out for is the subscriber growth or decrease. If Netflix decreases its subscribers to 55.6 million paid subscribers, that’s a 5% decrease. Since that is still lower than the 18% price increase, the move made financial sense. Thus, the terms of the debate change to, “will Netflix customers grow, slow or halt?” Here’s the past 7 years of subscriber numbers (paid, US):

subs from earnings reports

Predicting the Effects: How Many Subscribers will Drop from Netflix?

There are a couple of ways to try to triangulate this number, but let’s start with how not to do it.

The Bad Prediction Method: Using yourself as a data point.

Many people when discussing TV or film use themselves as the ur-example of a customer. I saw multiple people say on Twitter something along the lines of, “I use Netflix all the time. I don’t care about a $2 increase. Ipso facto, this doesn’t matter.”

Now, if you are a representative sample size of America, then congratulations. This analogy works. (Also, I have a ton of other questions to ask you. Like who will win the 2020 election? You should know.) If instead, you are a single data point, then we need something else.

The Trust Method: Believe in Netflix’s army of economists.

Read More

Most Important Story of the Week and Other Good Reads – 18 January 2019: NBCU Streaming & Netflix Has Very Ordinary Economics

If you judged importance by following my Twitter feed, the most important story of the week is Netflix and Netflix and Netflix. For business leaders plotting the future of entertainment, though, remember to always look for the “signal” through the noise. A lot of Netflix news is Netflix noise. “Buzziness” may justify Netflix’s original programming goals, but it doesn’t tell us what stories really matter. (But yeah, I’ll have a Netflix take later.)

Most Important Story of the Week – Comcast NBC Universal Announces Free Streaming for Comcast/Sky Customers (and ads)

Sometimes, disagreements about the strategy of a company boil down to disagreements over who a company should be targeting with their newest products. For instance, at first, I was really skeptical about Quibi, the short-form, subscription video service. (This was a hold-over from my skepticism for Vessel.) My main criticism is I don’t think it will work on TV sets in living rooms. But that’s not Quibi’s plan: they’re focusing on mobile to reach even-younger-than-Millenials. In that sense, my critique of their distribution strategy doesn’t make sense.

That’s why I thought some of the criticism of Comcast NBC-Universal didn’t make a ton of sense either. (Beyond the criticisms that are just, “If you aren’t Netflix, you have already lost.” I can’t really debate that.) Instead, I think a lot of the criticism compared NBCU’s new plan to Netflix, when first you need to ask, who are they really going after here? Are they they same segment?

To evaluate a strategy fairly–and many times in business we don’t do it fairly–starts with understanding who they are targeting, then judging the tactics based on that plan. Or you explain why they shouldn’t target a given segment. The disingenuous way to do this is to assume a company should target a different segment, then evaluate their tactics in that vein.

With that mini-preamble, who is Comcast NBC-Universal (NBCU from here on) targeting with their latest offering?

This is where it gets tricky, as NBCU has both B2C (business-to-customers) and B2B (business-to-business) masters it is trying to serve. Starting with the customer side, the generous interpretation is that NBCU is trying to focus on customers who haven’t cut the cord yet. Essentially, get them used to streaming by offering it to them for free. (This could also be a different segment entirely, focusing on people who want a free streaming service.) In other words, making a streaming service for older-than-Millennials who already have cable.

In a lot of ways, this reminds me of the “TV Everywhere” push of the mid-2010s, just more centralized. TV Everywhere failed because it had too many offerings (an app for every channel and cable company), confusing offerings (5 rolling episodes), no guiding force (every channel was on their own) and lack of in-house technology and data analysis. This deficit extended from NBC Universal to Fox to Disney. That said, the purpose of TV Everywhere made sense. Even if this is just “TV Everywhere on steroids” or “alt-Hulu”, the focus on adding value to the traditional TV bundle could work.

Of course, the second set of masters for Comcast will appreciate this too. That’s all the MVPDs that Comcast risks offending by offering this new streaming service, including it’s own cable/satellite services. The problem plaguing the traditional studios is how to respond to Netflix while not trading streaming revenue (that is actually negative cash flow) while forgoing valuable subscriber fees (that is a huge free cash flow positive). The potential answer from Comcast seems to be a giant punt on the issue, which could be brilliant. If it works–a big “if”–then they’ve essentially cracked the most difficult nut of the whole “traditional studio with network transition to digital” piece.

Further, if “subscribers” are the only metric of performance that matters then with a stroke NBC-Universal can take a lead in the streaming wars. Of course, the skeptic could and will say, “Sure they claim 50 million subscribers, how many use the service?” But neither Netflix, Amazon or Hulu has released Monthly, Weekly or Quarterly users yet. Why should Comcast be the first? In the meantime, we’ll have to triangulate with device installs, Nielsen/Comscore measurements and new subscribers to triangulate. But we won’t know for sure.

(Final note: Using the (3C–STP-4P Marketing Framework for the new conglomerates streaming platforms is a tremendously useful way to look at this problem. That will be fun, and take me weeks to make. Expect it in March or later.)

Data of the Week – The Extremely Ordinary Content Economics of Netflix

Where are my thoughts on Netflix raising prices? Well, my rule of thumb is if I write 2,000 words on something, it becomes its own article. So tomorrow I’ll release my thoughts on the Netflix’s price increase. That would have been a candidate for “Most Important” event in many weeks, but the NBC-Universal announcement bumped it. Earnings reports usually don’t make it in, unless they have ground-breaking news.

Read More

Suspiciously Recurring Numbers and More Implications of The Netflix versus Crazy Rich Asians Debate

(This is the third part of a multi-part series exploring one specific Netflix number. To read the other pieces:

Introducting “Datecdotes”, when Streaming Companies use Data to Win the PR Wars

Did More People Watch Crazy Rich Asians or a Netflix Rom Com Last Summer?

Netflix versus Crazy Rich Asians: What Else Does Netflix “80 Million Customer Accounts Tell Us?)

Okay, enough skepticism. If we take the latest datecdote from Netflix, at face value, what can we learn from it?

Well, to start, let’s take a look at the history of “80 million” in Netflix releases…

What other evidence of “suspicious” numbers do you have?

In a future article, I’ll write about another “theme” of this website called, “Theme X: Be Skeptical”. Especially with competitors. Don’t give them the benefit of the doubt! 

One of the corollaries to that theme relates to data. The corollary is, “Be wary of large, rounded numbers.” Data isn’t often rounded so evenly. This applies to scientific studies, political causes and other social phenomena. Oh, and entertainment success stories.

I’d add, if the same company keeps repeating the same big number that would be weird, right?

Netflix has this problem with their original movies, and I don’t think anyone has pointed this out yet. Researching the “80 million customer accounts” I naturally googled to try to find every news source uncritically repeating this datecdote. Imagine my surprise when the first occurrence of 80 million accounts wasn’t October in the Q3 shareholder report, but actually in June!

See, their “Summer of Love” romantic comedies weren’t the first time they had “80 million customers” watch something. For example…

– In June, Reuters was given data from Netflix that 80 million customers had watched a Netflix Original movie in 2018.

– In June, Dana Feldman on Forbes also reported that 80 million customers had watched a “romance film” on the service. Rereading it, this looks like it includes both originals and licensed films. This came from a Netflix tweet.

Then in their Q3 letter to shareholders, Netflix repeated the 80 million customer accounts number.

Clearly there was some rounding going on. And for press releases or on information provided on background, Netflix is under no obligation to be precise. But let’s assume the numbers are close enough. If the baseline assumption is all Netflix movies combined get 80 million customers accounts involved, the “Summer of Love” films didn’t really boost viewership that much did they? Either for romance films or original films. Eighty million customers is just what big groups of movies promoted by Netflix tend to get.

But if I wanted to be skeptical, I mean, what are the odds that exactly 80 million customers watched an original in from January to June, which is the same number that over the previous year watched a “romance movie” and then, after two more months of Netflix rom-coms being released in the “Summer of Love”, they had all in 80 million customers watch an original romantic comedy? Is that crazy overlap, or three part coincidence?

What if we take all the recurring 80 million customers at face value? What can we learn from this number?

We can triangulate the floor for Netflix “Monthly Active Users”.

This is the biggest way that streaming video distributors, social platforms and subscriptions services in general try to game the narrative. A customer or user includes anyone who “samples” a subscription. So you order from Blue Apron, or start a Hulu free trial, or sign up for SnapChat. Those are all users or customers.

But Monthly Active Users (MAUs) is a much better approximation of who is actually using your service regularly. (Or weekly or daily active users, which are even smaller time periods.) That means actual people you can monetize through ads or monthly billing. With Netflix, we have no clue what their monthly or weekly active users are. Most social platforms include this in their SEC filings. Netflix does not—it isn’t a social platform—and instead focuses on “subscribers”.

Read More

Introducing “Datecdotes”, When Streaming Companies Use Data to Win the PR Wars

Here are some fun stats. What do they tell us?

– Netflix over the summer had 80 million customer accounts watch one of their Netflix Original Romantic Comedies.

– Netflix had 20 million streams for The Christmas Chronicles over the last weekend.

– Amazon Prime/Video/Studios had 14.7 million total customers watch an NFL Thursday Night football game.

– Snapchat had over 10 million viewers watch a Snapchat Original show this year.

At first blush, that’s a lot of data. And it’s big! You know, in terms of size, in that 80 million sure is a lot of people.

But let’s count the actual numbers released. One. Two. Three. Four.

Four numbers is not “big data”, in the data science sense. Data doesn’t get “big” until you reach the hundreds of thousands of data points. In fact, some data scientists would say data doesn’t really get big until you have millions of data points with many, many categorical variables.

Alas, as we ponder the bare handful of data points above, if we really pause to think on them, we understand how little we’re being told. Take the journalism “Five W’s”, who, what, when, where and why. Most data can’t tell us the why—it’s implied—but in streaming video it can tell us the other four.

When streaming video companies release single data points, they usually only give us two of the five W’s. First, they give us the “who”—customer accounts, customers or monthly active users. And they give us the “where” in the broadest sense possible in that they give us the “global numbers”. But crucially they always omit the “what”. How many minutes were viewed per person? The “when” is also usually implied, but not explicitly stated, usually so that the numbers are as large as possible. In the case of The Christmas Chronicles, they gave us the “what”, but left out the why.

As a result, usually we can learn very little as competitors, observers or investors from these nuggets. A contrarian might say, look here, Entertainment Strategy Guy, you said in this very early article that you LOVE data. At least these companies are providing us some data.

Well, I’ll dust off a great quote from statistics to counter that,

“The plural of anecdotes is not data.”

Netflix, Amazon and Snapchat—who are just the three companies I’m picking on today, Twitter, Facebook, Twitch, Hulu and Youtube do this too—aren’t providing data, they’re giving us anecdotes. Selectively curated data-based anecdotes in the hopes—that are almost always granted—that unsuspecting and unquestioning news outlets will repeat to boost their perception among customers, Wall Street and competitors.

And we always fall for it.

See, the companies above aren’t choosing between one or two data points. Or even a couple of dozen. These companies are literally choosing between millions of potential data points, which make these numbers some of the most selective anecdotes you could possibly come across.

The analogy (and yes it is in the title) is the old saw about the iceberg. 10% of the ice floats above the sea, with an even larger 90% below the water. This is how it feels when a streaming company drops their knowledge on us.

Slide1

With streaming video, the numbers are even more extreme. They have millions of customers watching tens of thousands of videos with at least a dozen or more categorical variables per interaction. We’re talking thousands of potential ways to meaningfully slice the data, and the companies pick one or two per quarter. Again, the plural of anecdotes isn’t data.

Slide2

The line is so close to the top of the iceberg, it may as well not even be touching it. That’s how much data we don’t have access to.

I have a new name for this. Even if you have a data point, that still isn’t “data”. It’s an anecdote. It’s a “datecdote”, an anecdote of data. Interesting, but not enough to base decisions off of.

Netflix, we’ve been told, isn’t an entertainment company, they’re a product company that leverages huge amounts of data to deliver us our entertainment. Maybe that’s true, for internal work. But when it comes to PR? Netflix isn’t a data company. They’re an anecdote company. They’re a datecdote company.

I’ve spent a lot of the last week polishing an article digging deep into the second most recent Netflix datecdote. My main conclusion is that at conferences or on investor calls or when choosing to publish press releases, as journalists we need to push back. We need at least the five W’s, and we need at least comparisons to put these datecdotes in context. Without those, and this is controversial, we just shouldn’t publish their number. I’m realistic enough to know this won’t happen, but we’d know a lot more if we did.

The Most Important Shape in Entertainment Part III: The Examples

(This is Part III for a multi-part series on “Logarithmic Distribution of Returns”. Read Part I HERE and Part II HERE.)

I come across the flaw of averages in reporting quite a bit. Take my article on MoviePass. The CEO said in an interview with The Indicator that the “average MoviePass customer sees 1.7 movies per month”.

If you read my articles from a few weeks back explaining distributions—and I know you read all 3,000 words—that average of “1.7” is virtually meaningless. He could have told us what the distribution looked like, but didn’t. And probably for good reason. (Impending bankruptcy.)

Since he won’t tell us, here are my guesses:

Chart 1 MoviePass

I would call this a “Log-ish” distribution. First, it’s not a continuous range. With MoviePass, they had discrete scenarios. You see one movie or two movies, but not 2.5. Also, my guess is more people use the service in a given month then let it sit idle, which keeps this from being a true log distribution. I also put an artificial cap at 10 films. That said, the behavior in general will have power-law results. (Some very small number of people will see an order of magnitude more movies over a year, literally 100 in some reported cases.)

(If these numbers were true—and I have no reason to expect them to be—then MoviePass would lose, on average, $5 per month per customer, on average. Given they had 3 million customers when I got my 1.7 number, this would put losses at 15 million per month. Since their CEO said that they were losing 21 million per month, my gut says that tickets were more expensive than my model, mainly because they were over-indexing on coastal users. Also, if the subscribers went up to 4 million, I’d be about perfect.)

Still, I found a Logarithmic Distribution in a random place. (Said in the voice of Rhianna to the tune of “found love in a hopeless place”.) When I started this three part series, I called the Logarithmic Distribution of Returns the “most important shape” in entertainment. I said it applied EVERYWHERE, not just to movies.

Well today, I’ll show you the everywhere. I’ll be blunt with you, I want to convince you of two things:

1. This is the reality of returns in every field of entertainment.
2. The average sucks (or is “sub-optimal”) at describing this reality.

Data Notes and Cautions

Some cautions on data, as always. Why do I always talk about the data itself? Like why provide this critique of my data? Because NO ONE else does on the internet. You should always be as informed, especially when coming with numbers, so when I use data I want you to know what I do and don’t have, what I can and can’t prove.

Caution 1: I’ve seen this in more places than I can share.

I worked at a streaming company, but that data is confidential so I can’t share it. In addition, I’ve done deep dives into other parts of entertainment, but sometimes I can’t find the charts I’d made, or they were on other computers. So that’s a bummer.

Caution 2: I’m limited by available data.

In many cases, I don’t have access to the database that has all the information. To really show a log-distribution, you need all the data, not just slivers. Instead, I have to rely on what I can find—the good graces of the internet—which is usually top ten, top 25 or top 50 lists, which isn’t good enough. We can still extrapolate using some logic, but if I had access to the database itself, it would all look more logarithmic.

Caution 3: I plan to update this over time.

This post has taken a lot of research, which takes time. At the same time, I promised this three weeks ago. So to manage both priorities, my goal is to post this today, then update it over time as I find more examples and/or think of more.

On to the examples.

Video

Or “filmed entertainment”. Any marriage of visual recording with audio usually performs like our logarithmic returns. But let’s start with our example from last time.

More Movies/Films

As a reminder of a perfect logarithmic distribution, here’s box office returns in 2017.Chart 2 Movies Again

In my second article, I showed how this distribution applied to multiple genres of films. Well, I recently looked at this for another genre of films. And guess what? We got the same distribution. In this case, I looked at war films.Chart 3 War Films

Source: Box Office MoJo

TV Ratings by Series

Of course, you could argue that maybe theatrical box office skews the performance of video. So let’s turn to the other primary form of video, TV. Let’s start with traditional broadcast TV. Deadline had a summary of the ratings for broadcast channels in 2017 with the top ratings by series. Unfortunately, it doesn’t look as great as I wanted:

Chart 4 Broadcast Ratings

Source: Nielsen, via Deadline

What went wrong? Well that’s “broadcast” TV. In fact, that’s broadcast “prime time” TV. People with cable (or broadcast) can watch a lot of other types of shows: daytime programming, syndicated shows and cable. Oh, all the cable.

In a future update, my goal is to expand this table. (Trust me I’ve google the internet for a while and this is the biggest hold up to me posting today.) If I had access to Nielsen, I could do make the table pretty quickly. Instead, they only provide “Top 10s” and I can only find prime time broadcast on publicly available sites. (I made this chart for work before with Nielsen data.)

So I’m not off to a great start (though trust me, if you add cable above it looks logarithmic), but I have two other TV options to show.

TV Channels Viewership

Of course, we could also look at “TV Channels” as their own distinct entities. Do we get the same type of performance? I hadn’t initially thought of this, but stumbled across ratings by network when I was looking for data in my “CBS Myths Debunked” article. Here you go:

Chart 5 TV Networks by Viewers

Source: IndieWire

TV Subscriber Fees

Thinking of channels got me to think of another way to measure the value of TV channels, by the amount cable companies have to pay in “subscriber fees”. I don’t have time to explain sub fees now, but just know they were the straw that stirred the drink for the last few decades in cable. I had some old data from 2012 listing cable sub fees and here you go:

Chart 6 - Cable Networks by SubYou could look for logarithmic distribution in “total subscribers” in cable, but you won’t find it. There is a cap on the number of households that can subscribe to a cable channel, which nears the total number of households at 100 million-ish. As a result, when cable channels hit that upper limit, they used fees to capture the extra value.

Streaming

So Netflix, Amazon, Hulu and the rest don’t share ratings data. So no charts here. But I’ve seen the data for one of the streamers, made the charts, and let me assure you this: this law absolutely applies. The most popular shows on a streaming platform are multiples bigger than the vast majority that come, go and are forgotten. If anything, given the larger sizes of the platforms, the effects of the log-distribution are more pronounced.

Speaking of size of libraries, let’s head to the largest library of video on the internet.

Youtube

Know this: if you search for information on the number of views by video, you find a lot of articles on “Gangnam Style”. Which I’m not saying to be negative, just pointing out.

Search hard enough, and I did, and I found the key insight here. This long, information article on a website called the The Art of Troubleshooting, where he used some scraping and R to pull the data on the video views. I took a screenshot of his “log-normal distribution” of video views. (In other words, he converted the logarithmic distribution into “logs” to show the normal distribution. It’s the same thing, it just looks different because the scale is in log.)

Here’s the picture and another link to his site.

Chart 7 - Youtube Log Normal

Source: Art of Troubleshooting

The insight with Youtube makes sense: “Despacito” and previously “Gangnam Style” have literally billions of views. Yet, since anyone can make a video, the vast, vast majority have 0-100 views. This effect continues with channels as well, as measured via subscribers, sort of like how I measured both by show and channel above. This article on Vox has some of the statistics showing how big the biggest stars are. For example, PewDiePie is way out front, but most people don’t have any subscribers to their channel.

Youtube is definitely winner-take-all and the distribution holds. Here’s a chart showing the top 250 channels by sub. Look at the trend:

Chart 9 - Top 250 Channels by Sub

Source: TwinWord

If we turned this into a histogram and expanded it out, we’d get our log distribution.

Social Media & The Internet

As the Youtube example shows, as the sample size grows, the effects of the power-law get amplified. Moreover, with the internet, the data is a bit easier to come across. And it makes the power-law distribution even starker.

Social Media

Let’s start with Twitter. Do the number of followers someone has follow a power law?Chart 10 Twitter Followers

Source: StatisticsBlog.com

According to this website, yep. And again this makes sense: Rinaldo has tens of millions of followers while most people are in the hundreds and bots have hardly any. This other article says that over 90% of people have less than 100 followers, which makes sense. Let’s head to Facebook. In this case, the number of friends someone has is NOT power-law, since it isn’t really consumer facing. But, the number of likes something has does follow this law:Chart 11 Facebook Followers

Source: A ScribD article via Quora

In the future, I could look at both measurements of fandom (subscribers, followers, etc) or popularity of individual posts (likes, shares, etc) on multiple other social platforms and you get the same effect each time. That’s what going viral is.

Internet

One last part of this which is how the internet started: old fashioned webpages. Do certain cites have multiples more viewers? Of course.

Chart 12 Top News Sites Statista

Source: Top News Sites via Statista

That comes from Statista, who only covered news websites. You can go to Alexa and see another list of top websites, all in the hundreds of millions of monthly visitors. Yet, according to this one website, there are 1.89 billion websites. That’s definitely power law distribution. This random paper online backs this up.

Next Time

So that’s five pages, 12 charts, and 7 or 8 different categories of entertainment (film, war films, TV shows, TV channels, Twitter, Facebook and the internet).

But I’m not done, just done for today. In my next update, I’ll try to tackle music—there are two more databases I don’t have access to—and other more unique/weird subsets like toys, comic books, sports and theme parks.