All posts by EntertainmentStrategyGuy

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

The Myths of CBS…Debunked!

A few years back, I was at a party—more like a family get together—and the subject turned to TV. Everyone at the party started raving about the latest The Big Bang Theory. Then raving about other CBS shows. As an effete, Millennial, west coast liberal, with New York values, I joked about it with my brother. We don’t watch any CBS shows!

Well, that’s not quite true. I currently watch Life in Pieces. I used to watch The Good Wife. My brother watches The Amazing Race.

Hmm.

I was stereotyping. I took some data points and anecdotes about CBS from my experience—both personal and professional—and drew broad, generalized conclusions. Like most people in my social circle, I don’t watch The Big Bang Theory. So I stereotype the people who do, along with the people who watch NCIS (formerly CSI). In fact, very few coastal liberals brag about watching CBS. TV has become a cultural identifier, especially peak TV. We judge other people by the TV shows they watch.

Critics do this too. Well, especially critics.

If it ended there, in judgy cultural wars, fine. But I work in entertainment and media in a business capacity. These stereotypes inevitably infect my thinking. They infect all of our thinking. I’m saying “our” in the “we work in the entertainment industry” sense.

In business, you make decisions. You do that based on data, both good and bad. Stereotypes are bad data, and they’re a lot more common than uncommon. If you use stereotypes to make decisions, you’re likely making bad—sorry, “sub-optimal”—decisions.

On Monday, recapping the end of the Moonves era, I laid out a series of stereotypes about CBS. Broadly, Moonves made shows for “middle America”—meaning rural, white and not coastal—that were popular, but not “good” in a critical sense. That’s the general consensus. Today, I’m going to look at the data today because I wanted an excuse to reexamine these stereotypes I’ve carried for so many years.

Caution 1: I’m going to primarily use Nielsen data for today’s post. I used Nielsen data in past research projects at my former company, but I don’t currently have a Nielsen subscription. This means I’m relying on websites that do, that also publish their results. This makes it tougher to interrogate the data. Further, I wasn’t a Nielsen ratings expert by any means. (I was focused on streaming data, you know?)

Caution 2: This is also going to be a lot of selective data pulling. I’m not setting out provide a definitive answer. Instead, I want to pull just enough data to make you question your own assumptions and stereotypes.

Myth 1: CBS was popular with middle America, meaning not the coasts or not the cities.

If you think middle America, you think the middle of the country, not New York and Los Angeles. Fortunately, New York and Los Angeles are large enough markets that Nielsen could tell us how well shows performed in those specific geographies. Unfortunately, as I mentioned above, I don’t have a Nielsen account.

Here’s what I did find. Joe Adalian of Vulture used Comcast Xfinity data to pull the most popular shows by city. Here are three cities as an example (but seriously read the whole article):

Chart 1 City View 3

Source: Xfinity viewing data via Indiewire

The first lesson is that different cities do have different tastes, and likely these differ even further from rural tastes. America isn’t some uniform blob. Obviously. That’s what makes this a great country.

But…and here’s the huge but…notice that The Big Bang Theory is just popular. It made every city list except one (out of 16). Blue Bloods made a bunch more. (The data is from 2016.) My guess is CBS would do pretty well in the top 25 and top 50 lists.

The lesson is that sure CBS “over-indexes” in the middle of the country. But CBS still has a lot of fans in cities. And in all the states. That’s what blockbusters do. Besides Game of Thrones, The Walking Dead, and, I presume, Stranger Things, CBS is the closest thing to blockbusters in TV.

Myth 2: Middle America means old people.

CBS is an aging dinosaur and no one who is under 50 watches the channel.

That’s the stereotype. While it is true a lot of cord cutters are young people, a lot of cord cutters are also older people. Another stereotype for another article. But just because CBS over-indexes on older viewers, which it does, doesn’t mean that no young people watch the network. That’s a fallacy. For this data point, I used Michael Schneider’s summary of TV network performance from 2017. Here’s the broadcast channels:table-2.jpg

Source: Nielsen via Indiewire

It isn’t that CBS under-indexes on younger viewers—it has roughly the same as ABC and Fox—but that it has such an over-index on older viewers. More young people watch CBS than watch any cable channel on average.

Again the lesson isn’t that CBS doesn’t favor older viewers or favor rural areas versus cities. But it’s much too simplistic to say CBS is only older viewers, which is the stereotype. We need to be careful moving from a “trend” to to “no one” or “never”. That’s when evaluating data turns into stereotypes. (And bad decisions.) A lot of young people still watch CBS, not zero.

Myth 3: Middle America means white people.

Don’t get me wrong: I’m not setting out to prove that CBS is the most popular TV network for viewers of diverse backgrounds such as African-Americans or Latinos. I don’t think I could prove that because it isn’t true. But is the converse true, that no African-Americans watch CBS, which is the stereotype?

No. Here’s from Nielsen directly, their top 10s of a given week, broken down by demographic:Table 3

Source: Nielsen

Do you see differences in viewing habits? Yep. Only four shows overlap between the two lists. That said, a CBS show makes the cut for African-Americans, and I bet if we saw the top 25 or top 50 we’d see some other CBS shows make the list. Yes, CBS skews older and whiter, but it isn’t a monolithic blob. It’s heterogeneous, like America.

Myth 4: CBS has underperformed financially.

Okay, this isn’t a widely repeated myth, but it is the analysis I read in two critiques of Moonves, one by Richard Rushfield in Vanity Fair (which I said you should read on Monday) and one by Joe Nocera in Bloomberg. Both articles cited CBS stock price lack of stock growth as evidence of Moonves’ failure as a CEO. Nocera used a pretty blunt headline for this, “Moonves was not a good CEO”. Here’s their evidence in two charts:

Pic 4

Source: Bloomberg.

I have two responses to this. First, yes, the stock price has been flat. That said, if you have faith that the stock market is a good predictor of future performance in particular (meaning for individual stocks) then you have a lot more faith in the market than I do. (Also, if you pick and choose dates on the stock market, you can rig the outcome.)

Moreover, when judging firms, I hate just using one metric. This comes from my unwavering belief in “the balanced scorecard” approach to most problems. If you just focus on stock price, you’ll get executives focused on inflating that. If I had to pick one metric above all else, though, I’d pick cash instead of stock price. Specifically free cash flow. So here’s a comparison of the CBS Corporation and, oh say, Netflix in the terms of free cash flow:table-4.jpg

Source: MarketWatch.com and Annual Reports

I’d love to include other broadcast channels such as Fox, NBC and ABC, but they’re so encumbered by their large conglomerates it would be too tough to untangle. (And I didn’t do this analysis, I relied on others, either the company’s own annual reports or MarketWatch.) Either way, to call CBS a financial disaster is disingenuous at best and flat wrong at worst. It generated at least $3 billion for shareholders in the last three years, whereas the main tech giant in tech lost at least $4 billion, and plans to potentially double that number this year.

But this myth isn’t really about the numbers, but the narrative. Let’s get to that.

Myth 5: CBS is old broadcast, not new tech.

This accusation was leveled by Rushfield, Nocera, and I’d add most importantly, by Rich Greenfield, the most quoted analyst in entertainment. Here’s the money paragraph from the Nocera article, citing Greenfield:

There were no larger ideas — no sense that Moonves had a plan for competing in a future where Netflix has size CBS can’t match (130 million subscribers), HBO has content it can’t match (“Game of Thrones”), and AT&T-Time Warner has revenue it can’t match ($158 billion vs. $14 billion). Nor was there any inkling that he might invest for the future if it meant taking a short-term hit to earnings, something Netflix does as a matter of course. Rich Greenfield, the BTIG analyst who has been a rare Wall Street voice critical of the CBS chief executive, says that Moonves has long preferred to “focus on short-term cheerleading actions versus real long-term strategy.” Greenfield is right.

First, saying CBS didn’t have a strategy is my pet peeve. Clearly they had a strategy to generate about a billion in cash each year. You may not like it; you may not be able to define it, but they had a strategy. If you want to criticize someone’s strategy, define it first, then criticize it. Otherwise you’re building a straw-man.

Second, wait, it doesn’t have the content? That’s Nocera’s second point, but honestly, CBS makes more popular series than HBO, so that’s just not factually accurate. Both NCIS and The Big Bang Theory have viewership comparable to Game of Thrones. It also took a huge swing with Star Trek: Discovery.

Third, size isn’t a strategy. Ask GE. Conglomeration goes in waves, as I predict this wave of consolidation will do. (Also, I hate industry consolidation. Bad for consumers, good for stock prices. More in future articles.)

Fourth, it’s all moot because of the broadcast channels, CBS was the most forward looking. Alone among the broadcast channels, CBS had an independent streaming platform.

Disney still doesn’t have a plan for ABC with streaming, NBC has been trying to figure out a digital strategy since Comcast acquired them—and they have so many stakeholders they still haven’t figured it out, though they are hinting in recent interviews they have—and who knows what Fox’ plan is now that Disney is buying almost all of 21st Century Fox except for the broadcast.

So it can’t be about the tech. What really bugs Nocera/Greenfield about CBS?

That CBS won’t burn cash to grab market share.

Really, that’s what separates CBS from Netflix. They could have taken the $1 billion in free cash flow and made say 40 additional shows and put them on their streaming service, and poof cash gone. (Or ten shows at Netflix/Amazon Prime/Video/Studios prices.) Amortize over long enough it may not even hit the net profit line.

But Wall Street would have crushed them with that approach. Only Netflix gets away with that in today’s stock market. If you’re criticizing CBS for having a flat stock price, what would you have done if the stock price had tanked?

To sum up, was CBS the best streaming platform? No. Was it the most dinosaur-ish of the broadcast channels? No. It was somewhere in the middle, in that it was actually small enough to be able to launch CBS All-Access, even if it was late to the streaming party compared to Netflix, Hulu and Amazon.

Myth 6: CBS makes bad TV shows

Listen, I’d love to find an absolute ton of links with critics saying this, but I think this sentiment is, if anything, more popular in quiet discussions at entertainment shindigs than it is something said out loud. In the entertainment press you don’t want to burn too many bridges or future places of employment. The best summations were Todd Van der Werff’s three articles on the subject from 20152017, recapping each year’s upfront.

The problem is “bad” is just so darn subjective. So we need to find a way to prove this. I have two definitions that get semi-objective: awards and critical acclaim (which is usually the forerunner to awards). For the last time, and fifteenth time this article, I’m not setting out to prove that CBS is the best at making award winning shows—it clearly is not—but that it hasn’t completely struck out. (This is probably the most “accurate” myth.)

Awards

Reviewing the Emmy nominees for drama and comedy (the Golden Globes aren’t a real award show) since Moonves took over in 1995, CBS popped up regularly. Not the most, but not the least. In comedies, Everybody Loves Raymond won twice, Two and a Half Men was nominated, along with How I met Your Mother and The Big Bang Theory. The Good Wife was one of the few broadcast dramas nominated for several years.

In smaller categories, David Letterman won for talk show until Jon Stewart took a stranglehold. (Colbert and James Corben have both been nominated recently.) The Amazing Race, though, had a similar stranglehold on the reality-competition award for years.

Critical Acclaim

Okay, I’m not going to fight this battle. Most critics hated everything on CBS. This stereotype is accurate that critics just hate on CBS.

Most Important Story of the Week and Other Good Reads – 14 September 2018

Hypothetical question: in any given week, do more people in America watch CBS or Netflix?

Think about it for a moment, but  you know why I’m asking: the firing of Les Moonves is the most important story in entertainment. Absolutely for last week, definitely for the month and in competition for the year. I almost put some other articles that were overwhelmed by the news cycle in today, but the Moonves/CBS thoughts went long. Tune in Friday for those other ideas.

The Most Important Story of the Week – Les Moonves is fired/removed as CEO of CBS Corporation

Take that question I asked at the start. My guess is that many folks who live on the “coasts” would say Netflix. Many twenty-somethings and thirty-somethings would say Netflix. (I won’t use that term to describe them.) Heck, many people in entertainment & media would say Netflix, especially if they themselves cut the cord. I wish I knew the answer, but I don’t.

Here’s a bad data approach to comparing CBS and Netflix. One I expect anyone answering Netflix would use. Grab CBS’ highest rated show–The Big Bang Theory–and note that it had 18.6 million viewers. Grab Netflix has about 55 million plus subscribers in the US. Since 60 is greater than 18, Netflix wins!

If only it were so simple. That comparison isn’t “apples-to-apples” (my explanation of that term here). Netflix only releases subscribers. CBS only has TV ratings. The comparison above is subscribers to highest rated show, and logically the highest rated show is only a subset of all subscribers.

We don’t know Netflix’ highest rated show. So we can’t continue our comparison that way. But we do know CBS subscribers, since it is a broadcast channel featured in nearly every cable package, if we know the universe of TV viewing homes–via cable, broadband, satellite or over the air–we know it’s subscribers. That’s a number of something like 95-100 million households. (Note: I’m not counting Showtime or CBS All-Access subscribers either, since I don’t know the crossover.) Thus, the question hinges on the number weekly viewers as a percentage of total subscribers. If 100% of Netflix subscribers watch every week, then CBS needs only 55% of its potential audience to tune in. In other words, CBS has a huge head start in this hypothetical.

If I had to bet, I’d bet on CBS. And that quiet difference between the perception of Netflix and the performance of CBS says a lot about the entertainment industry as we head into the 21st century. Netflix may be the future, but CBS made a lot of money the last few decades as the number one broadcast channel.

Before I go on Les Moonves’ tenure as head of CBS, let’s provide a couple of caveats.

Caveat 1: This is my “gut” thinking.

I would really like to dig in deeper to the numbers behind Moonves’ tenure at CBS. But even something like CBS financial performance isn’t something I’ve studied in-depth. So as a reminder, this is my “gut” thinking as opposed to an analysis. (See the explanation for the difference here.) I’ll pull some data and links, but not a full-blown analysis.

Caveat 2: This is from a business/strategy perspective.

Following the financial crisis, the big question for business schools was ethics. Should/does business have any? Being a card-carrying liberal, in that tradition, I think it should. Conservatives in the religious sense should tend to agree. Only soulless free-marketeers would disagree. That said, today I’m writing about Moonves’ impact on the entertainment industry, and that means evaluating his performance by largely ignoring the ethical and social implications of the #Metoo movement. More importantly, others have written about it better, and my goal isn’t to echo good ideas, but to create new ones.

Regarding Moonves in particular, if a CEO commits unethical and/or illegal behavior, he needs to be fired without pay. A strong, independent board of directors should facilitate that based on a fair reading of the evidence. It’s pretty clear where the evidence led in this situation so despite his success, he shouldn’t be at CBS anymore.

So my conclusions/thoughts/predictions:

1. CBS was hugely popular in unpopular ways.

If you created a word cloud to describe CBS in the popular perception of Hollywood, you’d get something along the lines of…

    …middle America

    …rural

    …white

    …middle to lower class

    …older people

    …and lowbrow.

Here’s the thing: that’s the perception, but is it the reality? Was CBS successful in middle America? Sure, but his shows were still among the most popular shows in LA and New York. Was CBS only successful among lower class viewers? Maybe he over-indexed there, but you’d be surprised how many wealthy people watch CSI or NCIS or Blue Bloods. Were their shows popular and hence lowbrow? I think this is fair in that critics couldn’t wait to pan most of CBS shows, but The Big Bang Theory is an awards juggernaut. (Which is a conundrum. You can’t call the Emmy voters out of touch when they vote The Big Bang Theory or Modern Family, then praise them for awarding Transparent or Veep.)

Les Moonves success as a TV executive mostly went unremarked by TV critics. Or at least it wasn’t a buzzy topic. Being popular tends to make you “unpopular” with critics, so CBS was generally not buzzed about. Or at least less buzzed about compared to the coverage of Netflix, Hulu or even Amazon Studios/Prime/Video during awards seasons.

CBS appealed to the masses and by doing it so it managed to be popular with just about every group. Not everyone, but every group. Since The Big Bang Theory and the NCIS/CSI families were super popular, they were popular across almost every demographic, geographic and social category you could find.

Why don’t we know this as a business community? Well that will take some time to explain, but to summarize, poor segmentation driven by “over-indexing” means that the entertainment business tends to stereotypes certain networks/companies.

(This little section inspired two future articles for me: 1. “Indexing…Explained!” and 2. An analysis of CBS to find the data or lack of data supporting the CBS stereotypes.)

2. Les Moonves really was a hit maker.

The watchers of the media world weren’t focusing on CBS, so it kept accumulating viewers even if it wasn’t accumulating reams of Emmy and Golden Globe awards. This is a very “gut” statement, and I hope to do the analysis on it, but it seems like every season CBS trotted out successful new shows to replace the ones leaving, across both drama, comedy and reality. If we charted out all the successful broadcast/cable shows in the 2000s and 2010s, we’d see that Moonves/CBS shows would take an out sized portion of the top 20% of shows. Given the logarithmic distribution of returns (so excited to use that already!), that means he had an outsized impact in creating hugely popular shows.

That’s why I call him a hit maker. Moreover, I didn’t realize until this week he was at Warner Bros. television during the dawn of Friends and ER. He really did seem to have the talent to make hit TV shows. Or at least identify those people who could make hit TV shows.

(This section is just the tip of an iceberg for a third article I’m writing, this one on “development executives” and how many hit makers there truly are. It’ll be fun and super controversial.)

The only caution to all this is the idea of “network effects”, which isn’t quite the right word, but close enough. Network effects are when a business that has a network gains additional benefits as the network grows in size. Facebook or Amazon Marketplace are the best examples; if everyone is on Facebook, you benefit more from joining the social network; if everyone is selling on Amazon, customers go there more often to buy things.

In TV, though, “owned-and-operated” media is the one place where size can beget size. So if you’re launching a new TV show, would you rather have Comedy Central advertising it to it’s hundreds of thousands of viewers, or CBS advertising it to the millions of The Big Bang Theory viewers? You want to be on the latter, which means it can be easier to launch new TV shows. CBS definitely benefited from this effect, but it can’t explain all of CBS sheer dominance.

3. This makes the CBS/Viacom merger more likely.

Pretty simply, the CBS board would have backed Moonves against the Redstones in the takeover. Now I can’t see that happening.

4. And I would agree with this merger from CBS’ perspective.

Clearly, I hate industry consolidation. But I hate it because it is a prisoner’s dilemma: if everyone else is consolidating (instead of growing by adding value), then everyone has to consolidate. If you don’t, as your competitors grow, they can use size as a weapon to negotiate with buyers, suppliers and customers. That’s bad for customers, and for the remaining small firms.

An independent CBS would have been fine with Moonves. Probably. Then, CBS could have bulked itself up to prepare for the impending “streaming wars”. Without Moonves, CBS really risks becoming an also-ran. Moonves was a hit maker and I have no guarantees his successors (whether in the CEO role or as head of development) will have the same ability. If his successors are just average, overtime the network effects will wane and CBS will go away. Instead, I’d recommend that CBS join Viacom and let size help them negotiate.

5. Who will step into the CBS void?

I don’t know.

It doesn’t have to be another broadcast channel. It could be, but no network has shown they have a reliable hit maker. It could be a cable channel, but again, no obvious examples jumps to mind. (If this were the mid-2000s, I’d have bet on USA, but they’ve under performed compared to their 2000s performance.) It could be a streaming service or premium cable, but only Netflix has flirted with popular programming for popular sake. The downside with Netflix is that their hit rate could be the lowest in the industry, which is the opposite of “hit making”.

Or no one. There isn’t a law that one channel/platform has to rise up and achieve dominance. But if a streaming/cable/broadcast platform wanted to seize TV market share, now is the time. If you have a hit maker, you could take CBS place.

The challenge is the development execs. In Hollywood, it’s sexy to make award winning, buzzy, prestige shows for peak TV. TNT, FX, Netflix, Starz, HBO, Amazon, Hulu, NBC and USA/Syfy have all dabbled or tried to pursue this strategy. It isn’t sexy to make cop shows. It isn’t sexy to make multi-cam sitcoms. They can make lots of money, though.

If you run a content company, do you have development executives willing to risk industry scorn for making popular shows that don’t appeal to critics? A lot of money and market share can be won by making popular things.

Long Reads of the Week – Other Good Reads on Les Moonves’ Exit

I enjoyed a few reads this week.

To start, listen to the emergency banter with Kim Masters and Matt Belloni of The Hollywood Reporter and KCRW on The Business. Alway worth a check in, and in their banter from this week on reporting on CBS board.

Next, The Ankler’s Richard Rushfield published in Vanity Fair about Hollywood protecting powerful men in Hollywood, with the flair he usually writes with.

I said others wrote better about the larger #MeToo explanations, and I’d point to Todd Vanderwerff at Vox (my go to general news site right now) as the best example.

Finally, I’d point out Joe Adalian’s piece for the “CBS will be fine” narrative that I sort of challenge above. That said, I’m simplifying Adalian’s poin. He has faith that CBS is the work of a few people, many of whom could have absorbed Moonves’ style.

The Most Important Shape in Entertainment Part II: Logarithmically Distributed Returns

My dad didn’t like the ending of Empire Strikes Back. His felt that it didn’t finish the story, it left off with a, “See you next movie!” conclusion. That irritated him. He hasn’t seen Avengers: Infinity War yet, so you know he won’t like that.

My article yesterday probably did sort of the same thing to the audience. I come up with this big conclusion—the logarithmic distribution—but then barely touch on it.

Well, since we’re already talking about the movies, we might as use that as the ur-example of my magic trick, “Logarithmically distributed returns”. I first learned this law by analyzing movie performance, and it’s my best tool for teaching it to others. But I’m not just going to show you this phenomena, I’m going to show you it multiple ways, in multiple categories. Then I’ll explain the biggest statistical mistake I’ve seen when forecasting box office performance.

Logarithmically Distributed Returns…What is it?

Let’s start with the last word. What I’m describing today is the “output” of most entertainment or media processes. So my examples are about the “result” or the “y-value” or the “dependent variable”, to describe it in three different statistical terms.

In other words, performance. This means how well something does. Box office for movies. Ratings for TV. Sales for music. Attendance for theme parks. No matter what the format, the success (or very frequent failure) is logarithmically distributed.

What does logarithmically distributed mean? Essentially, orders of magnitude. The returns don’t grow on a geometric scale, they grow on an exponential scale. This means that the highest example can be in the billions while the smallest can be in the dollars. That’s a difference in magnitude of 9 zeroes.

The most common summation of this is the “Pareto principle”, who coined the term about “power law” distribution. Roughly speaking, Pareto is summarized by the 80-20 rule, or 20 percent of the inputs deliver 80% of the returns. And like any mathematics/statistics topic, there are obviously a ton of variations on this law and specifics that I’m not going to get into.

(For those who are curious, inputs have their own distributions, but aren’t as reliably distributed as outputs. A topic for the future.)

Logarithmically Distributed Returns Visualized: Feature Films in 2017

Enough talk about what it is, let’s use an example. I went to Box Office Mojo and pulled all the films from 2017 that grossed greater than $0 in theaters. I didn’t adjust for year and pulled everything, no matter how small. The result was 740 movies released. Oh, and I only pulled domestic gross.

I’m going to show you the data two ways to help you visualize it. First, is the less accurate way, but I love it because it shows scale. This is all 740 movies plotted from lowest to highest, with the y-value as the domestic gross in dollars.

slide021.jpg

Source: Box Office Mojo.

I love how smooth the curve looks. But the true measure of the data is the “histogram”, where you count the number of examples per category. I set up the categories myself at $25 million dollar in intervals, starting from zero.

slide031-e1536791810207.jpg

Source: Box Office Mojo.

Most people don’t realize how many films are written, produced and even released every year. Like I said, last year was over 700. So let’s add a threshold of $1 million dollars at the box office to our list. If I had production budget estimates, I’d sort by that, but the result gets you to the same place. (The reason for using production budget is that when you scan that “almost grossed $1 million threshold”, you see some legitimate films such as Patti Cake$ and Last Flag Flying, from Fox Searchlight and Lionsgate/Amazon Studios respectively. Those films cost a lot more than $1 million to make.)

slide041.jpg

Source: Box Office Mojo.

All the charts show the same story in different ways: there are hundreds of films that made less than $1 million at the box office, around 150 that did less than $25 million (many of which probably lost money), a range of movies in the middle and then a few monsters (Star Wars: The Last Jedi, Wonder Woman, Jumanji and Beauty and the Beast).

I think I can hear some of you insisting that I give you the “counting statistics”. You still want to know the average, right? Well here they are, for all 740 films. I mainly did this because I’m going to use them in the next section.

slide051.jpg

How Logarithmic Distributions Differ from Other Distributions

Perhaps the best way to describe the logarithmic distribution is to show how it isn’t other distributions. In other words, to show how inadequately the normal distribution and uniform distribution capture the performance of feature films.

Let’s start with the uniform distribution. The idea that, “Hey, a movie can gross anywhere between $600 million dollars (Star Wars) and $0, and every where in between.” What if we had an equally likely chance of that? In decision-making, the human brain often defaults to uniform distributions when assessing possibilities, so this isn’t completely academic. Here’s how that would look:slide061.jpg

If only this were how to finance movies! The industry would green light a lot more movies. But it isn’t, only a few films hit that rarefied air of $200 million plus dollars.

What about the normal distribution? I tried to chart this, using our mean of $15 million and standard deviation of $50 million. Unfortunately, that gives us a lot of “sub-zero” grosses, which I just cut off at zero. The problem with the normal distribution is it makes misses as rare as hits. That just isn’t the case. Also, the odds of a giant hit become astronomical in a normal distribution. In this case, a hit like Star Wars: The Last Jedi would be 10+ standard deviations form the mean, meaning it has a 1 in a million chance. Obviously, hits like that happen every year, so more like 1 in 200.slide071-e1536792385695.jpg

Let’s put them all on the same chart, to really show how logarithmic distribution of returns just looks different.slide081.jpg

Source: Box Office Mojo

This chart shows how quickly the results drop off in reality compared to other hypothetical distributions. If someone tells you Hollywood isn’t normal, show them this chart and say, “You’re sure right!”

Variations on the Initial Theme

I might still have skeptics in the crowd.

Maybe, they’d say, I just got lucky. That distributed returns happen to be power-law-based for the year 2017, but this lesson doesn’t really apply to other parts of film. Well, that would be wrong.

Spoiler alert: no matter how you slice the inputs, you get the same result.

First, I could expand the number of years I’m using. I happen to have box office gross from a project I did that covers 2012-2014. Here’s that chart.Slide09

Source: SNL Kagan

Here’s the next fun trick: the distribution of returns still applies for sub-categories. Take horror, which I looked at a couple of months back. Here are all the horror movies going back to the Exorcist, according to Box Office MoJo. Specifically, “Horror-R-rated”, which is 504 films:

Slide10

Source: Box Office Mojo

The rule still holds! In this case, there has been one monster horror film—It—then some other smaller ones. Of course, I could hold all the box office and adjust them for into 2018 grosses. Does that change the picture? No, if anything it amplifies it. In this case, The Exorcist did $1 billion in adjusted US gross, and The Amityville Horror did $319 million. But for those increases, a lot of other smaller films drop down even more, especially recent films.

slide111.jpgI’ve done this for a ton of different genres. Superhero movies. Foreign films. And it always holds. The only caution is that sometimes the “ceiling” of the range gets compacted.

What about sorting by something else? Say, rating? Do R-movies have more hits versus PG-13 or PG? Fortunately, my 2012-2014 data set has ratings. First, know that G, NC-17 and Not Rater just don’t have a lot of examples (only 45) so I deleted them from this analysis. Here are the other three, in line chart form:slide121.jpg

Source: SNL Kagan

As we can see, for R, it holds. For PG-13, it holds. For PG, it looks like it holds, but honestly since we only have 39 examples, it doesn’t show as clearly. Increase sample size and we’re going to see this.

You could do this analysis setting for production budget and studio and even types of studios. As long as the input is independent, it holds.

Two Examples Where This Works Less Well

Listen, I believe in being up front with my data analysis. Even though this is a magic trick, I’m not trying to hide or obscure data that doesn’t make my case as well. That’s why I left PG rated movies in above, even though it’s the least logarithmic looking line in my analysis.

So in my experience, have I come across sub-sets of movies where my rule/law/observation doesn’t hold? Absolutely, so I’ll share those with you next. To clarify, it’s not that my magic trick fails, it is that the floor disappears. So look at this chart, from my series on Lucasfilm:

slide131.jpg

Source: Box Office Mojo

These is my data set of “franchises” that included Star Wars, Marvel, DC, X-Men, Harry Potter, Lord of the Rings, Indiana Jones and Transformers. As you can see, those films just don’t have flops. The “floor” is about 200 million in domestic box office, with only 14% of all films dropping below that. So it isn’t logarithmic on one end. I actually think my timeline of films by box office, with their names, shows this floor pretty clearly over time:

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Source: Box Office Mojo

My rule doesn’t hold—this is important—when I sort by another output, not by an input. In other words, I’m sorting by the result.

A franchise is a series of films made off a successful first film. In other words, it is sorting by “success” of the first franchise film. Many aspiring franchises therefore didn’t make my data set. Four examples off the top of my head that I did not include, from three different genres: The Golden Compass, Battleship, The Lone Ranger and John Carter from Mars. If I included all aspiring franchises, the list would have looked more exponential Also, this data set is small, only 50 movies.

What about that huge data set I just pulled to look at Oscar grosses? Well, I haven’t even histogrammed that yet, so I don’t know what it looks like. So we’ll see. Again, though, this is in a way a “success” metric in that these are all “good” films. Obviously, a lot of films at the bottom of our list—meaning getting sub $1, $10 and $25 million grosses—were just bad, so no one saw them. With the Academy Awards, we’ve deliberately sorted that out.

slide161.jpg

Source: Box Office Mojo

The rule holds! Mostly. Now, with adjusted gross we do see a bit of a floor. Historically, a best picture film tended to get more than $50 million in domestic box office. But with both Oscars and Franchise Films, we can see that “super-hits” are still rare, but present.

Final Lesson: This is Why Linear Regression Doesn’t Work in Entertainment.

I have one final lesson for the data heads in the crowd.

Let’s say you’re an aspiring business school student who hopes to go into entertainment. Or you’re a junior financial analyst. Or a statistician diving into entertainment. (Three real world examples I’ve encountered.) You’re given a mess of data on the performance of feature films at the box office. And you want to draw some conclusions.

Well now that we know how our data is distributed—logarithmically—we should come to one clear conclusion: linear regression WILL NOT WORK!

It’s really just right there in the name. Linear regression works on things that have linear growth, and our things have exponential growth, which throws off all conclusions. The work around is that you can convert our data points to logarithms, and then have a “log-normal” distribution, which gets you closer to accuracy. (Though, as I wrote here, you still have a sample size problem.) In general, as well, since you have so few examples of success—the long tail at the right—you just can’t draw statistically meaningful conclusions.

Conclusion – What’s Next?

Well, I didn’t say this was a law of media and entertainment because it applies to feature films. I said it applies to everything. And it does.

But that’s for our next installment and another dozen or so tables and charts!

The Most Important Shape in Entertainment Part I: Distributions Explained!

You want to know a secret? The underlying secret to all media and entertainment? The peak behind the curtain that explains all you see in film, TV, music and more?

Here it is.

“Logarithmically distributed returns.”

Once you learn it you can’t forget it. Like how to do a magic trick, which is what I call it, my magic trick for the business of entertainment. I didn’t discover logarithmic distributions. I first read it in Vogel’s Entertainment Industry Economics, the wonk bible of entertainment financial analysis. (Figure 4.8 in chapter 4 if you’re really curious.) I also assume it’s the theoretical underpinning of Anita Elberse’s Blockbusters, which I haven’t read. (Her book is one of those books that has been on my “to read list” for years.

Unfortunately, I can’t just show you that logarithmic distribution under girds all of entertainment. As important as the “logarithm” part of the statement is, the “distribution” part is even more crucial. I don’t want to gloss over that. The value comes in not just seeing one chart, but seeing the value of distributions as a tool.

Today, I’m going to teach you about distributions. What they are and why you need them. This is a mini-statistics lesson to pair with my other mini-statistics lesson on why you can’t use data to pick TV series. I won’t use any equations, because they’re boring, but I’ll show you what the distributions look like. Then, tomorrow I’ll show you the ubiquity of logarithmic distribution.

(As I recommended before, go pick up The Cartoon Guide to Statistics for the best reader on statistics. Learn them in a weekend. It’s way better than this very useful, but very technical Wikipedia page.)

Before we get to the “what” of distributions, let’s get to the “why”.

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Most Important Story of the Week and Other Good Reads – 7 September 2018

I try to write these updates to post by late afternoon on a Friday. Often–most weeks actually–I miss that optimistic target and finish them over the weekend/first thing Monday morning, then back date them to the week they cover.

Obviously the biggest story of entertainment was Les Moonves being fired, but that happened on September 9th, two days after this “update”. So I’ll cover another story and this week, but rest assured I’ll chime in on the Les Moonves controversy at the end of this week. (Or early next Monday morning.)

The Most Important Story of the Week – Broadcast TV Ratings Continue a Slide

A few weeks back, I checked in on the box office results for the summer so far. In my ideal world, all senior executives–heck, all managers period?–would “react” to data by not reacting. That’s right, in my opinion, the real-world-ification of data hasn’t made us better at making decisions. If anything, it causes us to react to bad data or uncorrelated data. (This includes “real-time dashboards” and “email alerts” for data. Even weekly updates can be misleading if the trends are sustained.)

Let’s apply this philosophy to TV ratings. Do executives “need” to know how a show did in over-night ratings, especially since they focus on C+3? For instance, the Thursday night football game from last night had a three year low in viewership. Does this portend down ratings all season? Maybe, but we won’t know. What if Sunday has a high in viewership and some combination of the teams involved, the rain delay and the fact that a lot of people (like me and Bill Simmons) hate the idea of Thursday night football games?

So we can step back and look at the ratings from the season as a whole, which the Hollywood Reporter did for us, including emphasizing that broadcast generally, and  scripted shows particularly, were down. So that trend continues. I also love learning that a show I’d never heard of–Yellowstone–was a beast in the ratings on a network most people haven’t heard of. (The new Paramount TV, converted from Spike.) Also, for all the buzz I heard about Succession, Sharp Objects actually delivered higher ratings, which I feel like happens a lot for HBO series (the more popular series have less buzz and vice versa).

Other Candidates for Most Important Story – Amazon Had Technical Problems in US Open Coverage in UK

This is one of the stories I have a feeling most people missed. In short, Amazon Prime Video is distributing live sports in various territories, like how it did in the NFL Thursday night games last year. The big debut in the UK was it’s coverage of the US Open in tennis, but it had a lot of technical issues such as a limited number of games and lagging.

This isn’t THE most important story because surely Amazon can throw engineers at the problem. But it’s a good lesson. As a community, the mantra goes that “content is king”. Don’t forget, though, that “UX is the bishop”. Or hand of the king? So the metaphor isn’t great, but know that a crappy

Big Bad Data of the Week – The Hollywood Reporter on International Film Sales of African-American movies

Honestly, I hesitate to even write this little blurb for fear of offending people. So let’s be clear: I want more “variety” in my movies (wait until my listen of the week to explain that term). I love diverse movies on a variety of topics. I celebrate those. And celebrate diverse voices in directing, acting and writing. I also think I have a better grasp on the problem than most execs (panels and reports don’t solve problems; economics do), but they will never solve it because of self-interest. (Basically, nepotism, self-dealing and bias towards class prevent true diversity/variety.)

To solve our problem–a serious lack of diversity–we need to be precise in diagnosing the problem. We have to let the data guide our decisions. The old axiom, “multiple anecdotes don’t make data” applies here. Unfortunately, too often the latter happens when discussing diversity.

I see this a lot in coverage about the success of films featuring diverse casts, including African-American, Latino and, recently, Asian-American casts. Instead of drawing an entire data set of all movies, articles such as this prominent one by The Hollywood Reporter rely on a self-selected dataset featuring a biased sample of successful movies.

To start, this is an example of the availability heuristic at work. The availability heuristic is when your brain calls out easily “available” examples. Often, these are misleading examples and not a representative samples. In films, it’s easy to think of popular/successful movies–especially if you have an emotional connection to them. It’s much harder to think of flops.

Take the sample set from the above article. These movies are hardly representative of all movies. They feature films nominated for Oscars. Oh, and a Marvel movie, either the first or second most successful franchise in film history. The alternative is to capture all movies in a given time period, give them all diversity categorizations, then measure performance. That takes time, and a lot of journalists and companies don’t take the time to do that analysis.

This is really important for the decision makers. I’ve first hand seen the availability heuristic and, more importantly, a biased sample get a 9 figure business plan launched. It later lost the company lots of money. (The key to the success of the plan? HiPPO. See here or me writing on it here.)

We have a diversity/representation/variety/inequality problem throughout our industry. We need to solve it, and bad data doesn’t do that.

Listen of the Week – “Variety” episode on Martini Shot by KCRW/Rob Long

I loved two things about this episode:

  1. The word play between “variety” and “diversity”. You can just tell by listening that Rob Long is a writer; he’s a wordsmith. Sometimes changing one word can have profound effects on how you look at an issue. This wordplay did that for me. As he points out, the examples of “diverse” films don’t feature diverse casts, they feature in some cases uniform casts, just different than traditional films. So the better word to describe that is “variety”. Rob Long says it better.
  2. He gets at why variety is so valuable. Sometimes we focus on diversity for diversity’s sake. Which may be okay. But from a business standpoint, a well-executed film featuring a unique subject matter can offer audiences something they don’t usually see. That leads to higher box office returns in general, and this applies to all sorts of films.

Disney-Lucasfilm Deal Part VII – Licensing (Merchandise, Like Toys, Books, Comics, Video Games and Stuff)

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

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

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

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

Slide57

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

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

Slide58

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

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

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

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A Quick Addendum to Theme 2: Making it More Complicated

When I wrote “Theme 2: It’s Not Value Capture, It’s Value Creation” last week, I made things seem really simple. Probably too simple.Value Creation ChartThat said, I hold to my core point: most businesses could benefit by pulling out that chart and answering three simple questions,

“What price do we charge customers?”
“What is their willingness to pay (WTP)?”
“What are our costs?”

Then they could ask the forward looking questions: “How can we raise the willingness to pay for customers?” or “How can we lower our costs?” In short, how do we create a competitive advantage derived by creating value, not capturing it?

Real life, unfortunately, is never that simple. That simple chart gets complicated. Really quickly. Here are some ways.

The entire value chain

I kept the chart and examples from the last post relatively simple. I only used one buyer and one seller. But this transaction is repeated down the chain. I pay the store for the beer, the store pays the beer distributor, the beer distributor pays the beer producer and the beer producer pays it’s suppliers of water, hops and aluminum. Each stage has it’s own version of this chart.

Value Chain

This applies to film: the production company pays the talent (who pays a piece to their agent), the studio pays the production company, the distributors pay the studio (theaters, tv networks, streaming platforms) and the distributors collect the money from the customers.

One time transactions versus relationships

Of course, I don’t just go into the story to buy beer once, I go in regularly. (Not that often. Well, maybe.) For customers, regular trips like this can develop habits or a sensitivity to the changes in the price. So I could choose to measure the WTP/Price/Costs as one time events, or over the course of a month, or over a year or even longer. That’s a great way to make something simple complicated.

For example, say you lower the price of a good, which causes a customer to buy it more frequently or larger quantities. In other words, this chart looks like a single transaction, where profits went down, but they would go up with increased iterations. Of course, a customer could just stock up on items and store them, which means you did lose value, but the customer gained in consumer surplus. This is an age old challenge in “consumer packaged goods” that can offer regular discounts.  Like I said, it gets complicated quickly.

This biggest ramification for this for entertainment is evaluating subscription services. Analyzing MoviePass last week, I focused on the per month value chain. Arguably, MoviePass could consider their relationships annually, so they look at it on that basis. Maybe any given month is a bad deal, but over a year it saves you money. Or take HBO, I subscribe for a year, usually, but the biggest TV show by far that I devour is Game of Thrones. Is a year subscription worth that one show? Maybe, so being too lazy to aggressively cancel isn’t that bad of a deal, overall.

Distributions of people

I hate averages. Telling me the average almost never tells me anything useful about a data set. Take height: most men are five foot eight inches tall. Is everyone clustered around that point, or are there outliers? (Maybe an excellent explainer on this next week.)

Same with movie box office grosses. Chart it next to height and they look completely different. One is logarithmic and one is normally distributed.

So the value creation chart is basically the averages, especially for WTP. To extend the beer analogy, some people would pay a lot for a very bitter IPA, other people would pay a little more, many wouldn’t pay anything. And even among the people who would pay for it they have different values attached to the IPA. You can’t really summarize this as one number, though that’s exactly what I did.

When in doubt, use distributions, even with value creation. Understand who gains the most and try to emphasize that, but don’t stop with the averages.

You can’t measure parts of the chain

Especially “willingness to pay”, which is an imaginary value. How do you measure imaginary? Well you have to guess, and there are complicated and often unreliable ways to do that. (The worst way? Ask someone what they would pay for something. That never works.) The most reliable way is a conjoint analysis, but even that can get unwieldy with too small a sample size.

Streaming services are bedeviled by this problem, especially when they have to figure out what consumers actually love on their platform. Is it Stranger Things? Or GLOW? Or both, in some combination? Or is it actually the Disney movies, but the other shows are filler? That’s an epically tough problem to sort out.

Costs can be tricky

The “costs of goods sold” can be difficult to allocate. Especially for support functions that don’t directly tie to a good. Allocating the value correctly can be the difference—in a big conglomeration—between profitability or loss. Right now, content costs and how companies allocate those costs versus the prices customers pay is the biggest accounting/economics/finance question in the industry. Getting that answer right could determine he future of entertainment, for good or ill.

Most Important Story of the Week and Other Good Reads – 31 August 2018

With this update, we’re officially out of the slowest, dumbest month of news, August. Here’s my round-up of the “Most Important Story of the Week”, a few days late because of that blasted long weekend. (I’ll save my rants on how much better America would be with more 3 and 4 day weekends for a future article.)

The Most Important Story of the Week – The Fall of Global Road

So I held this story for a week. Coincidentally, I’d been mining some box office data for another project, and had looked into Open Road’s film history. I’ll admit when I first saw Global Road when bankrupt, I thought, “Wait, what is Global Road? Oh, it was Open Road.” Then I thought, “What happened?”

The story has been well covered. Since I spend so much time “reacting” to negative news stories, it’s worth praising when the trades really dig in well. (Hat tip to the Hollywood Reporter and Variety.) That said, I have a theory that the trades usually know the dirt on companies, they just wait to dig in until after an adverse incident (bankruptcy, firing, scandal). I, on the other hand, have no problems calling out what I perceive to be bad strategy.

If I had one single take away from the demise of Global Road, it’s this: “content is hard”. Especially when someone is keeping track. Looking at their slate, Global Road, and Open Road before it, didn’t have a huge blockbuster in the US they could hang their hat on. Without that huge hit–and not owning any IP outright–they couldn’t sustain operations.

Who should we watch out for as possibly being next? Well, a candidate off the top of my head–and note this down for a great future project for the Entertainment Strategy Guy, predicting who could go bankrupt next–is STX Entertainment. I devoured the New Yorker profile of that company, and frankly couldn’t understand their competitive advantage beyond “China money”. Let’s compare Open/Global Road’s US domestic box office performance and STX’s same numbers for the last three and a half years:

In chart form, with each film’s gross as the Y value:Slide1 In table form, counting the number of films at various box office levels:Slide2

(Source: Box Office Mojo. Open Road. Global Road. STX.)

(I used unadjusted box office gross from Box Office Mojo, going back to 2015 and deleting any films less than $1 million in total box office, which was three films for Open Road.)

Why did I think of STX? Well, Global Road just released the underwhelming AXL and STX released the underwhelming Mile 22 and Happytime Murders. Both are backed by Chinese money and new mini-majors headed by execs with long careers in Hollywood. But looking at the data, we can see for the near term, STX overall has just a higher trajectory. In addition, STX has had a “hit” that spawned a sequel, Bad Moms and A Bad Mom’s Christmas. Now the “hit” wasn’t tremendous ($113 million US) but that’s enough with their supposedly huge line of credit.

Of course, STX may have higher aspirations and may lose more money when you factor in production budgets and P&A spend. Arguably, they shot the highest on with Valerian and the City of a Thousand Planets, which only did $41 million in the US with a big marketing spend. It had franchise potential, but didn’t love up to the billing. (It did do $184 million in foreign box office; I don’t know how much STX kept of that.)

You know what is really cool, though? The ability to “keep track” of how well movie studios are doing. You know who I can’t do that for? Television shows that premiere on digital platforms like Netflix, CBS All-Access or Amazon Prime/Video/Studios. Instead, everything is a winner based off buzz. With movies, you still need a good box office performance to justify your existence. Enough flops and you go out of business. (First, Relativity, now Global Road.)

Which also brings us to the “successful” part of both Global Road: their TV business. (Paramount is having the same story right now; STX has moved into TV too.) How is it that a company can’t make enough successful movies to stay in business but they can for TV? Well, because SVOD platforms buy TV shows by the boatload, and pay profit up front, instead of in success. Since every show is renewed–no one fails in streaming–everyone in the TV production business is finding buyers for shows.

Which doesn’t mean people aren’t losing money in TV streaming, it’s just that they can afford to lose money and Global Road couldn’t afford it in movies.

Long Read of the Week – How Hollywood is Racing to Catch Up with Netflix in Variety

I’m going to stop writing on the above topic before it turns into a “Long Read” of the week. Instead, you should head to Variety for this good summary by of the state of “direct-to-consumer” offerings in the marketplace. The most useful part is the summary of each DTC service, it’s pricing and some basic information about the services then the summary of the streaming video players. The most glaring omission is something author Cynthia Littleton doesn’t have: the daily, monthly and annual active users and subscribers by platform. (It is also a little too praising of Netflix for losing billions every year, but isn’t everyone?)

I’ll also say there remains a glaring disconnect between the huge volume Netflix offers and it’s low, low cost compared to all these new DTC options. How is it possible? Well, Netflix loses money and Disney needs to earn a profit, as Littleton points out. This disconnect for me tarnishes the entire Netflix narrative, or at least challenges how disruptive it truly is, but that’s for later articles.

Listen of the Week – Malcolm Gladwell’s Revisionist History on “12 Rules for Life” and Pulling the Goalie

All the development executives of the world should listen to this episode. It argues you should “think the unthinkable” and ignore the responses of fellow humans. For me, the episode illuminated the key challenge of our industry: “relationships”. If you listen to Kim Masters regularly (and you should!), then you can hear her skeptically address outsiders coming into entertainment who don’t understand this is a relationship-driven business.

This is why the hockey coaches in the podcast–not to spoil Malcolm Gladwell too much–won’t pull the goalies. Their relationship with the fans would suffer.

But it’s also why the people who recommend this strategy–pull your goalie! Be unconventional!–work in one of the few fields where you don’t need relationships (or as many), which is hedge funds. They can do their trades automatically via computers…so they don’t really need to worry about pissing off people. In the parts of finance where relationships matter, like investment banking or wealth management, this strategy wouldn’t work. But certain hedge funds can get away with it.

Why Customers Love (Some) Subscriptions with Charts and MoviePass

To put it simply—why not just answer the question in the title early for once?—customers love (some) subscriptions because the consumer surplus is tremendous!

Yesterday’s post really captured why customer hate some companies, so let’s explain the few times when customers love subscriptions. Let’s be clear: in the digital age, when a company decides to lose money, it can be great for customers. Phenomenal even. In business terms, the consumer surplus is huge. That’s right, “consumer surplus” which I introduced on Monday in my article “Theme 2: It’s Not Value Capture, but Value Creation” is a customer’s willingness to pay minus their price. The larger the gap, the better the value.

Let’s use MoviePass as the example of the day to explain the benefits and pitfalls for a company that tries the subscription model.

Why MoviePass?

Mainly, because it simplifies the value creation model to its essence.

First, “willingness to pay” (WTP) is basically a made-up number. Customers don’t really know how much they “would” pay for a good, as that’s not usually how it’s asked. You walk into a store, see a price, and pay it. You don’t usually have a negotiation. Behavioral economics has shown that a lot of pricing is about setting expectations versus a rational cost-benefit analysis. Fortunately, we don’t have that problem with MoviePass. We know the price for a movie ticket, because anyone can just go out and buy one. For this case, we can substitute those prices for WTP. Easy peezy, lemon squeezy.

Second, MoviePass has real costs per transaction, which is that movie ticket from above. One of the big drivers of what I called “digital all-you-can-eat” subscriptions is the low or zero marginal cost of digital products. A DVD needs to be produced in a factory; each additional sale on iTunes has a marginal cost of almost nothing. This can make costs tricky to calculate, amortize or account for. MoviePass doesn’t have that since it’s costs are very clear and very real.

Movie Pass: What is the consumer surplus?

Well, it depends on who you are. In my simplified, single product, value creation model from Monday, the WTP could change per customer, but for the most part everyone is buying one six pack for roughly the same price. With MoviePass, the value per customer depends entirely on usage. Which changes the “consumer surplus” or WTP minus price.

And that single fact explains why subscriptions are either loved or hated.

To show that, we need to make some quick assumptions to illustrate our point. MoviePass—in its epic journey of the last year—changed business plans like ten times. So I’m going to pick what I think was the most popular plan for the longest period of time: seeing an unlimited number of films, once per day, for $10 a month. From Box Office Mojo, I see that an average ticket cost $9 (technically $8.97) in 2017. So we’ll use $9 as the price per movie ticket. (Round numbers, right?)

Knowing this, we can recreate the “value creation” chart from Monday. Let’s imagine four customers, one who forgot to use the service, one who saw one movie, one who saw two movies and one who went hog wild and saw 10 movies.Slide08That can be a bit hard to read, so let’s put it into chart form like Monday.

Slide09

There is one glaring takeaway from the chart—the fundamental flaw in the MoviePass business model—which is that MoviePass at it’s core is asking a stark proposition: do you use the service or not? If you don’t, like customer one, then you don’t get a value from the product. Even someone who only sees one movie a month would be much better off just buying movie tickets at the theater. On the other hand, if someone goes even twice, they’re clearly getting a better deal than buying tickets from the theaters directly.

What if you’re a “super user”? Going multiple multiple times per month? Well, you’re taking money from MoviePass’ pocket.

The last line on the table shows this trade-off explicitly: MoviePass never created value, it merely exchanged consumer surplus for producer surplus. That’s why you never have the “blue section” (consumer surplus) at the same time as the “green section” (profit) in the chart. MoviePass deliberately couldn’t make money unless a consumer ran a “consumer deficit” versus a surplus. They actively needed customer to sign up for subscriptions and not use them.

That single, obvious fact eluded most coverage of MoviePass.

Taking Our Model and Applying MoviePass’ Real World Numbers

Far from being a bug of MoviePass’ business model, signing up and not using the service was a feature. For this, unfortunately, I have to go to the person running the company, CEO Mitch Lowe, who told the podcast The Indicator that their data showed that the average customer only saw 1.7 movies in a month.

Well, look at where “1.7” puts us on the consumer value chain above. That means he’s acknowledging that at least half of his customer—I’ll be generous and assume the mean is close to median here, though I doubt that—are losing money on the MoviePass subscription. They’d be better off just buying tickets when they go, but instead they’re locked into his long-term contract. (Again, echoes of Columbia House here.)

Months after that above interview, MoviePass had to “pivot” business models. The company was losing lots of money with the unlimited plan, so they changed to a max of three movies per month. Here’s what that looks like in table form:

Slide11Essentially, MoviePass limited their upside risk. They made the “super users” who were using it for a lot of essentially free movie tickets capped to only two free movie tickets. This, though, made the value proposition a lot worse. Thus, when NRG researched this for the Hollywood Reporter, after making the change, MoviePass went form 83% satisfaction down to 48%. In other words, if you take away a lot of free stuff, people like you less.

The “Other Business Models” Arguments for MoviePass

Of course, you could make one of two arguments against my clear value creation chart: what if MoviePass had other ways to generate value for either itself or customers?

The first option is the “What if MoviePass negotiated better deals with the theater chains?”

Well, that wouldn’t really help customers, but would help MoviePass. Assume MoviePass used its size (at one point it was estimated it helped sell 5% of tickets in the US) to negotiate a rate of $8 per ticket. The consumers would get the same benefit, depending on how much they used the service. MoviePass would also lower it’s deficit in most cases. Here’s a chart of that:

Slide10At first, it looks like we might have created some value.

But not so fast. Where did MoviePass get that discount from? From the theaters. To do a true accounting, you’d have to factor in the new deficit to theaters, which would directly equal MoviePass’ negotiation. In other words, all MoviePass did was enter a value chain and demand payment. This is called “rent seeking” and is specifically not value creation.

(MoviePass would defend itself saying it is increasing attendance which drives concessions sales for theaters. Again, theaters could make that trade off—cheaper tickets for higher concessions—themselves without a middle man taking a cut.)

The second explanation is that MoviePass told us it planned to sell our data.

In general, and I hope to get an article published in another outlet on this soon, I’m skeptical of this idea. I’m skeptical of any “secret business plan” that isn’t core to a product. The more obscure the business plan, the less likely it actually exists. Given that data is already plentiful on movie viewing behavior, and the fact that MoviePass didn’t actually sell a lot of data, and given how much money they lost, well this idea wasn’t really real.


The Lessons from MoviePass for Subscriptions

Lesson 1: Customers clearly saw what was a good deal versus a great deal.

MoviePass made it very stark, as people knew the price of tickets in their local theater versus the price of MoviePass. As MoviePass changed/altered/finessed/destroyed their model, customers could immediately do the math to determine if this made sense. As a result, after prices went up and the total number of films available went down, customers saw this subscription wasn’t a good deal.

With many digital “all-you-can-eat” subscriptions, this analysis is a lot harder. Did you watch Netflix last month? Did you listen to Pandora or Spotify or Apple Music? Was it worth listening or watching without ads? Since those questions are a lot more obscure, it makes the decision to cancel that much harder.

Lesson 2: Companies need to price subscriptions very carefully, especially with marginal costs.

This is why most physical goods don’t bother with subscriptions. Just imagine a McDonald’s or fast food chain offering an all you could eat subscription per month. You’d either eat more or less than the cost of the food; if more, you cost the company money; if less, you wasted your own money. It’s very stark with physical goods.

With digital goods or services, the key—especially for non-digital subscriptions—is to price something at the rate that customers perceive they’re getting a good deal, even when you need a majority of them don’t benefit from it.

This is my worry with Lyft or Uber. At my last company, I had an hour plus commute every day. So if Uber offered me an “all you can eat” subscription for lower than my car payment, gasoline and insurance, I would have snatched it up in a hot minute. But for all the Lyft/Uber boosters out there—especially those predicting subscriptions—that number is impossible, unless Lyft and Uber deficit finance it.

My daily commute would cost $50 (at least) for each trip using a ride share. If not more. The true substitution cost assuming 20 commute days a month is $2,000 per month. If it was truly unlimited, I’d use it to go to the store and other places. I know all the people predicting that ride shares will replace car ownership, but they have to explain how long distance commuters won’t devastate the prices of subscriptions. (The answer is the hypothetical “self-driving cars”.)

More likely, the ride share companies won’t offer customers a good deal. They’ll arrange a program that sounds like a good deal, but comes with a lot of strings attached. Once you get caught in the strings, you’ll find that it isn’t that great of a deal.
 Again, Lyft and Uber could keep to per unit pricing with loyalty programs. Once they offer subscriptions, one side of the transaction will likely lose.

Lesson 3: Perception matters more than reality.

Arguably, this is the lesson from Netflix. They price their plan at $11 right now. So every other traditional studio trying to launch a new streaming service immediately runs into a problem: if we mimic Netflix’ price, there is NO WAY we can offer as much content as they do. How do they get away with it? (Hold on a moment.)

Netflix set the perception that tons of content should be available for nearly nothing. Or nothing if you’re using your parent’s account. Unfortunately, if they raised prices to cover costs, that perception may evaporate. So when you launch a subscription, the goal is to show how good a value you offer customers. As shown by MoviePass, this usually means a tremendous consumer surplus. Usually, this means losing money for the company in the near term.

At some point, though, if you can’t cover the costs…

The Main Lesson? Subscriptions that are truly good deals lose money (and Wall Street/VCs pay for it)

I mean, all those lessons above are fine, but the best numbers for MoviePass are the losses it sustained:

$40 million lost in the month of May.

$149 million lost to date from January to April.

It was hemorrhaging money in a way no alternate business plan could hope to rescue it from. But it isn’t the only internet company losing gobs of money in an effort to “secure market share”

$2 billion – Netflix free cash flow losses in 2017 (up from $1.5 billion in losses in 2016)

$900 million – Hulu losses in 2017

$418 million – Spotify operating losses in 2017

I’d also add that Amazon says they have positive free cash flow, but this article by New Constructs says even that might not be true. We don’t know how much money Amazon is making on either Twitch or Amazon Prime Video, though my rule of thumb is if they were making money, they would tell us.

In other words, it isn’t a coincidence that subscription services losing money happen to be the ones customers love. Instead, the more likely explanation is that it is directly tied to offering great consumer surpluses at the price of great producer deficits. Basically Wall Street (and venture capitalists for smaller tech companies) fuel huge producer deficits to enable subscriptions that customers love. At some point, they have to identify a way to actually make money, but that’s a problem for the future, not the near term.

Answering the Question at the Start: Who loves subscriptions?

1. Customers love subscriptions where they have huge consumer surpluses. The only examples of these, though, are where the companies run huge cash losses.

2. Customers hate subscriptions where they have low WTP. The main examples of these are monopolists or near monopolists like cable companies, wireless companies, health insurance or alarm companies. Or they are examples of subscriptions most customers regret after a few months.

3. Wall Street loves both types of subscriptions, for different reasons. They love subscriptions customers hate because again they are near monopolies. They love internet subscriptions because of huge gains in the stock market.

Subscription Business Models Explained: Why Investors Love Them and Customers Hate Them

Here’s a list of companies. Think about how you feel about them:

Netflix. Spotify. Dollar Shave Club.

Here’s another list of companies. Think about how you feel about them:

Comcast. Spectrum (formerly Time Warner Cable). Verizon. AT&T. Sprint.

My guess is you love the first set of companies; you hate the second. What links them? They’re both subscription services.

Subscriptions are hot right now. Hot! The Ringer has their article on subscriptions taking over the world. Here’s an Economist article on subscriptions (they also had a Money Talks episode on thist). Here’s a Forbes interview with the author of Subscribed!. Here’s another book on subscriptions. And since this is a website on entertainment, here’s Variety opining that subscriptions are here to stay.

Subscriptions!!!

My quick splashing of cold water on this hot take is this: um, subscriptions have been around in entertainment since the 1980s. At least. If you count “media”, well magazines pioneered subscriptions decades ago, if not centuries, depending on your definitions. (Yep, I just checked and a German magazine was selling subscriptions in 1663.) What was old is new again.

So subscriptions aren’t new, but they may becoming more prominent. I’ve seen two explanations for this: 1. Investors/Wall Street/shareholders loves subscriptions and 2. Customers prefer subscriptions. Are either or both those claims true?

Let’s dig to find out.

Why Investors Love Subscriptions: Customer Lifetime Value

There is a simple economic formula that explains why investors and shareholders love subscription services:

Screen Shot 2018-08-28 at 12.10.27 PM

That’s the formula for “customer lifetime value”, which is the economic way to model the value of a customer in a subscription business. In short, if you know the revenue of a future customer, and the margin you collect of that customer, and the number of people who stay with the program in a given time period, you can calculate the full value of a customer. And yes, I pulled that definition from Wikipedia. Here’s another way to look at it, also ripped from Wikipedia:

Customer lifetime value: The present value of the future cash flows attributed to the customer during his/her entire relationship with the company.

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