Tag: Carousel

GoT vs LoTR vs Narnia – Appendix: Subscription Video Economics… Explained! Part 2)

(This is an “Appendix” to a multi-part series answering the question: “Who will win the battle to make the next Game of Thrones?” Previous articles are here:

Part I: The Introduction and POCD Framework
Appendix: Licensed, Co-Productions and Wholly-Owned Television Shows…Explained!
Appendix: TV Series Business Models…Explained! Part 1
Appendix: TV Series Business Models…Explained Part 2
Appendix: Subscription Video Economics…Explained Part 1)

The best analogy for content libraries on streaming services, for me, is theme parks. When I tried to value the new Star Wars land Galaxy’s Edge at Disneyland and Disney World, I wrote about this future scenario:

Next year, I’ll walk into Disneyland in the off-season (probably September-ish). I’ll be wearing a Star Wars shirt. My brother will probably rock a Marvel shirt. That said, I’ll also have a four year old wearing, if current trends hold, either an Elsa (Frozen) or Belle (Beauty and the Beast) dress. Other family members will likely have Mickey shirts on.

So how much of that trip do you allocate to the opening of Galaxy’s Edge? My family already averages one trip to Disneyland every year, and my daughter knows that Mickey lives at Disneyland. So she’d go anyways. But what about me? I’ll definitely go to see the new park at some point. 

Something about theme parks—maybe the permanence of the attractions—helps crystallize in my head the challenge of valuing content libraries. A theme park is a content library of rides, shows, shopping and food. Some of those attractions at Disneyland have been there since the 1960s. Those are the “library content” of Disneyland. Others are only one or two decades old. Those are the “recent library” of rides. Then there are the brand new attractions: Star Wars land, Cars land and a Guardians of the Galaxy ride. Those are the “new TV” of Disneyland rides.

The trouble is trying to value each of those pieces and disentangle them. At the end of the day, this both matters—because you need to make the best decisions possible to maximize revenue—and doesn’t—because at the end of the day the goal is to have revenues exceed costs on a total basis. Do the latter and how you get there doesn’t really matter.

My approach to valuing theme parks—calculating the money spent by both existing and new customers—gives us a good idea for how to value content libraries on streaming platforms. So let’s explain that. In today’s article…

– The rules guiding my approach to valuing content
– The “dream method”, which is what we’ll try to emulate
– The steps to the optimal method
– The HBO and Game of Thrones example explained
– Some other variations, caveats and thoughts

The Rules

As I wrote these last two articles, I kept coming back to the “rules” that define good business models. A few stuck in my head for valuing streaming video. Thinking that way…

– First, no double counting. If a customer gets attributed once to a piece of content, they don’t get to count twice. (A good rule of thumb, you can’t attribute more than 100% of your customers!)
– Second, CLV trumps monthly revenue and other calculations. If you attract a new customer, CLV is the best way to capture their true value to your business.
– Third, be humble in attributing success. No single show or movie accounts for 100% of its viewers in a library model.
– Fourth, use real data as much as possible.

The Dream Method – The Probability of Resubscribing

The dream method for HBO would be, basically, to be God Almighty. Looking down omnipotently, reading the mind of every customer subscribed to HBO and knowing why they subscribed, and what percentage of that should be credited to Game of Thrones. Add all the percentages together and you have it. (Maybe our Google/Amazon/Apple AI overlords will be there soon…)

In the meantime, we have data. Especially streaming data if you’re Netflix, Amazon or (partially) CBS or HBO. 

This data means you can track every customer. When their account starts. When it renews. When it lapses. And, crucially, what they watch the entire time. From the people who only watch movies to the people who complete every episode of Game of Thrones. In a big data sense, then you can compare their behavior to the customer who never watched Game of Thrones. 

Say the results looked like this…

…GoT Viewers resubscribe after a year period at a 92% rate.

…non-GoT Viewers resubscribe after a year period at a 80% rate.

That means, of customers who started the year subscribed to HBO, by watching GoT, they were 12% more likely to stay subscribed to HBO. That’s the best number if you can find that, because it basically means that GoT increases the probability of staying subscribed by a huge, statistically significant margin. Now that GoT is cancelled, if those GoT watchers suddenly flee HBO, well we can also reverse engineer that to know that GoT had been keeping them subscribed.

This could also be applied to new customers. If you take all the new subscribers for a given time period, you can look at the ones who watch GoT versus the ones who don’t and model their behavior. You can also tell which are the customers signing up to watch GoT right away, and which ones don’t. Add those up and you can attribute all the best approximation for value we have. (With heaping doses of regression analysis and machine learning.)

Yet, we don’t have the big data to do this. I mean me, as a commentator on the strategy of entertainment. If I were managing content strategy at a streaming company, I would set a team of data scientists working on. But I don’t have that team or that data here. As an outside observer, well, we need to make some assumptions, but we can try to replicate that method.

My Method – Attributing New and Remaining Customers by CLV

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Read My Latest at Decider – How HBO Made Billions on Game of Thrones

I’ve been in a bunker these last couple of weeks and that bunker was an Excel bunker with internet access where I had one quest: to estimate how much money HBO made off Game of Thrones.

As I was writing my big series, “The game of thrones for the Next Game of Thrones”, I realized I needed a starting point. And figuring how much money Game of Thrones made was that starting point. It helped me understand exactly how the GoT Prequel could make money, but also tested my model. And I learned a ton figuring it all out. I’m up to 20 pages of research for this series and growing by the day.

(And I’m not close to being finished…this model inspired at least two more spinoff articles and maybe more guest articles.)

It was so good, I pitched Decider on it, and they accepted all 2,000 words of it (with tables).  Go check it out and share it on Twitter, Linked-In, Facebook and everywhere.

Seriously, I don’t ask for a lot of favors from my small, but growing, audience and this is one of those moments. If you’re a journalist, consider picking up the story, and I can answer any questions you have. (Email on the contact page or DM.) If you’re just a fan, still consider or emailing it to your entire office. Any little bit helps. Thanks in advance!

Again the story of how HBO made over $2 billion on Game of Thrones here.

GoT vs LoTR vs Narnia – TV Series Business Models (Scripted)…Explained! Part 1

(This is an “Appendix” to a multi-part series answering the question: “Who will win the battle to make the next Game of Thrones?” Previous articles are here:

Part I: The Introduction and POCD Framework
Appendix: Licensed, Co-Productions and Wholly-Owned Television Shows…Explained!
Appendix: TV Series Business Models…Explained! Part 1
Appendix: TV Series Business Models…Explained Part 2)

I’ve spent a lot of time trying to make the “ideal” TV Series business model over the last few weeks. Getting that right—and a bout of stomach flu/Avengers: Endgame that ruined/thrilled the end of last week—has been holding up this article.

But honestly, why bother?

As I was reflecting on my Game of Thrones series, I was thinking about my “gut” section from the introduction. Essentially, my gut thinking is what—if I were a traditional trade print columnist—I would have turned into my editors. It has a thesis, some data points and tells a nice little narrative about how well set up HBO is compared to Amazon. Add a little more certainty to the rhetoric and I’m done!

But it didn’t have any “proof” in it. To use my own terminology, it didn’t have any numbers. Since “strategy is numbers”, in my opinion “gut thinking” can’t prove the case. Today, we start on the path towards developing some numbers. I want to prove my case, which honestly I haven’t decided one way or the other yet.

My bar for “proof” in a business plan, though, isn’t the same bar as scientific proof. It’s not “scientific”  because you can’t use the scientific method on future events. Instead, you can be rigorous. Have a model that you trust, and let its predictions be your guide. If your model captures, say, 80% of the potential of a business, that’s pretty good. That lets you know if a strategy is sound or not. For my Lucasfilm series, I had to develop a film model to make my conclusion. Today, I have to do the same thing for TV series.

Consider this the “Appendix: TV Series Business Models (Scripted) Explained”. The good news is once we have this model, I can build bespoke models for the Game of Thrones prequel, Lord of the Rings prequel and Chronicles of Narnia adaption. In today’s article, first I’m going to compare the film and TV models, distinguish between the participants in a model, describe the costs of a TV series, and explain the key revenue drivers during the initial window. In Part II, I’ll show everything else.

Thoughts on “What These Models Are For”

The purpose of any model depends on its uses. I’m trying to use these models for “strategic” purposes. The strength of any content company is it’s underlying IP, both the floor and ceiling of performance. And specifically how much cash they will generate. That means the numbers need to be close to reality, but not close enough to audit. These aren’t accounting statements, but strategic models to help us understand the underlying performance/economics. My goal is to build a model that will be flexible enough that I can use it for multiple projects. (I have an idea for how to use my film model for another project for example.) 

Also, these “show my work”. If you opine on the business of entertainment, and don’t have any models (even rules of thumb) guiding your work, you’ll end up just reinforcing your priors. Even a simple model forces you to understand the drivers of a business. These models and my explanations will allow you to critique my conclusions and/or build your own if you disagree.

Comparing Film Financing to TV Financing

To refresh your memory, here’s my business model for a feature films:

Feature Film Biz Model

(An aside: If you want another model of film profit, Deadline runs an annual “top profit” tournament for feature films. Their numbers for the Star Wars films are a bit lower than mine, and if I have time, by the end of the year, I’ll dig into the drivers why.)

Let’s start with the biggest difference between the TV and film models. Feature films are much easier. Essentially, once you have one piece of revenue—the theatrical box office—well everything else flows from that. So much so that often you can use percentages to get a pretty good guess of what the total revenues will look like. The studios have people who have this down to a science based on opening weekend, current deals and other categorical variables like genre, rating and such.

TV doesn’t have any similar starting point. Ratings can fluctuate season to season—as I showed in my most recent article—and even then the four major routes of TV—broadcast, cable, premium and streaming—each have different business models. Moreover, in success, the path a TV show can go is as varied as the initial platforms, while 90% of studio movies follow the same path. Meanwhile, the number of films made each year dwarfed the number of scripted TV shows historically, especially if you count “series” versus “seasons” as unique data points. To top it all off, the business model for TV has changed significantly in the last 20 years, from one about deficit financing in the hopes of syndication to adding in home entertainment (DVDs then EST), to adding in streaming to streamers having their own plans. So lessons from even 20 years ago no longer apply.

What does this mean for my TV series models? I’m not going to have a neat waterfall tied to percentages of box office like I did with film. I tried to do that, but I didn’t like the results. So I’m going to build a shell where all the potential revenue streams go in, and then build three bespoke models for each TV show. The first step to that shell, though, is determining which participants go in, which is another change from out film model.

Participants

If something does apply from my Star Wars film model, it is the inevitability of Hollywood accounting. (Which was since revealed in gory detail in the Bones Arbitration.)

Basically, a studio will always try to pretend it never made money on a TV show or movie to avoid paying talent. As a result, like my film model, our TV model needs two versions: one for the studio, one for the talent.

Let’s not stop there. My film model has two main participants: the studio, who paid for the film, and the talent, who acted/directed it. I could have added a third participant, which in a lot of cases is the “producer” if a film is independently produced. That happens so rarely for blockbuster films, and doesn’t happen for Star Wars films at all, that I didn’t include it. In terms of the value chain, the producer sits between talent and distributors.

TV Value Chain

For TV, though, producers and distributors are different more often. For my TV model, I need to add/account for this third participant. Sometimes they will be the same person—GoT on HBO—but sometimes they won’t be—New Line/Tolkien estate for LoTR on Amazon Prime/Video/Studios. And talent will still require a place in both. So my goal—and I’ll see if I can pull this off visually—is to make a model that can show all three pieces simultaneously in a way that doesn’t make the reader’s eyes bleed. 

Model with Participatns

So that’s the shell. The plan is to start with revenue for the “TV producer” (in two parts) and then next week, fingers crossed, I’m going to talk about how the network or streamer makers money off the TV show. (That, um, is complicated.)

The Four Main Pieces for a TV Producer

Fortunately, the the four main pieces of the film model—revenue, costs, fees and talent participation—are the same. I’m going to talk about them in roughly the same order I did in my film model, which is in chronological sequence. First the monies going out, then coming in, then going back out again. Like last time, I’ll “build” the model as I explain each section.

Costs

In film and TV financing—well like most industries—the costs come first and the revenues may not come for years. Or ever. When it comes to the TV producer, the two main costs are development and production costs. In other words, how much it costs to produce a half-hour or hour of programming. Development comes before it all, as you’re getting all the pieces lined up (writing the script) and then actually making the episodes. 

Then comes making the show. This is a key to understanding why TV producers are always so cash strapped. The TV production house pays all its costs up front. It pays the actors to show up, it rents studio space. It hires all the below-the-line workers. All paid in cash up front.

A simple multiplication problem then defines how much a TV series as a whole will, in general cost you: number of episodes times cost per episode. There are a few key drivers here, which I call my “inputs” in the model. First, the number of episodes in a season and the number of seasons a series goes for. Essentially, a 6 episode season is half as much (roughly) as a 12 episode season. (Some costs are amortized but in general this applies.)

The drivers of episode costs are related to the length of the episode and the quality of that episode. Very simply, it costs more to make longer shows. (You shoot a thirty minute sitcom in 3 to 4 days and a 60 minute drama for 5 to 8 days.) This even applies to the length of an episode; a 22 minute sitcom for broadcast versus 60 minutes for premium cable. On top of that length, “quality” can drive up costs. Or the production values. Shooting on a soundstage for a multicam sitcom gives one look, that saves costs, while shooting outdoors in Iceland for a prestige drama is another thing altogether.

To keep these straight, in my head, I added an information bucket above my model to capture the key production details. I’m calling these the inputs. So here’s the model now:

Model with Inputs

The last driver is talent costs. Especially as a series progresses into future seasons. Getting top flight talent attached to a series and to keep working on a series requires lots of money, usually paid per episode.

Finally, if we’re talking all costs, marketing costs come into play. In TV, the network/streamer pays the upfront costs for the season, and it’s up to them to market the show. I’ll only take these costs into account for the distributor/network/streamers, which is how this three part model could get confusing. To make it even more confusing, the fee the network pays, while revenue for the studio, is a cost for the network. Here’s the shell of the model, with the “inputs” on top and the costs that I’m going to account for.

Model with costs

Revenues

Let’s get to the fun part: making money. My goal here is to list as many major sources of funding that I can, in order of perceived size. Or better phrased, their expected value. (Syndication is the largest bucket historically, but has a low probability of being achieved.)

First Run, Initial or Imputed License Fee

This is where you start as a TV producer. When you sell a show to a network, you negotiate fiercely for the network to pay you as much as possible up front. This is calculated as a percentage of the production budget. Historically, like 1980s historically, this was pretty low, I believe around 50%. According to Harold Vogel, this creeped up to 70% by the dawn of the 2000s, and in my experience and reported, now as streamers want more rights, this is well over 100% and sometimes up to 130% of the budget.

This fee is needed by the TV producer, because otherwise they are deficit financing, which is risky. The broadcast networks paid only a fraction of the budge and the TV producer had to make the rest on syndication. Since streamers offer so little potential future windows (Netflix gobbles all the windows up), the fees have increased since the producers have no chance at future revenue.

For that last case, what that means is that in exchange for all the future windows I’m talking about, the producer is paid essentially an upfront profit. So the producer makes the show for $5 million, get paid $6.5 million and call it a day. In the old days, the producer made the show for $5 million, and got paid $3.5 million, and needed the rest of these windows to make up the shortfall.

That’s why I’m calling this line three different things, that kind of mean the same thing and also don’t. If a TV producer sells just the first run rights to a network—with no co-production terms—that’s a “first run license fee”. However, for someone like a streamer, this license may extend beyond that first run, so you could use “initial” to just cover the length of the deal. Really, those two terms are semi-interchangeable.

However, when the network also has a piece of the show, calling it a “license” fee is a little disingenuous. Especially if it is wholly-owned by the network. Do you trust the network to tell you what a show is valued at? Isn’t that exactly how the Who Wants to Be a Millionaire? and Bones controversies started? As a result, this fee is really an agreed upon price, which I call an “imputed fee”, that’s also based on the production costs. It acts the same way, but since the money isn’t actually trading hands it is imputed versus real. (And as I just clarified, it shows up as a cost for networks in this model.)

That’s enough for one session. I’ll be back early next week with the rest of the accounting.

Porter’s Five Forces…Explained

The trouble with value chains—which I unveiled Monday—is that they don’t stay the same forever. They are constantly changing. Disruption, right?

Take filmed entertainment. In 1980, it was just movies and TV, with movies in theaters and TV on broadcast. But home entertainment, cable, digital and the internet have all disrupted those two models. Plus toys and merchandise are sold for all of it now at unconsidered levels back then.

While the value chain shows how things currently flow, it is pretty silent on how the things relate. Who has the power in the relationship? Who creates the most value? And for people at the same part of the value chain, what is it like? So we need another tool.

And thus enters perhaps the most famous tool in strategy.

Porter’s Five Forces…Explained!

The second tool is similar to the first one, but focused on a single part of the value chain. The competitors at one layer. And to complicate it, after Porter unveiled it, he added a sixth force, so it’s the five forces, plus one. Here’s the shell of that model:

1280px-Elements_of_Industry_Structure.svg

That’s from Wikipedia, so I hope they don’t mind me borrowing. (And yeah pro sports tip: Wikipedia is pretty damn good at explaining a lot of economic, statistical, business and other scientific concepts. It doesn’t replace reading the underlying books, but is great for refreshers.) My change is to tilt the model from the Wikipedia version:

Screen Shot 2019-04-10 at 3.11.17 PMValue chain analysis and five forces analysis serve two different purposes. The value chain is really about analyzing who creates and captures value at each stage, with the specific costs, gross margins and profits at each potential stage. (Twitter follower Simon pointed me to the Exponent podcast from 2017 that digs in to this usage a bit deeper.)

Five forces analysis is about the power of each of the inputs or outputs of an industry on the potential to make a profit. But usually limited to one stage. It helps explain why profit margins are high or low given the biggest inputs on those margins. Here’s the picture from my text book. It combines how my model (listing the players) and the Wikipedia model (showing the forces). (Again, I love my Strategic Management text book, and they have it oriented my way):

img_4436.jpg

As I clarified, I could build a “Porter’s Five Forces” model/run a five forces analysis on potato chip manufacturers or stores or distributors or even potato farmers. Since we’ve been doing manufacturers this whole time, let’s keep with that:

Screen Shot 2019-04-10 at 3.11.38 PM

The key insight of a five forces analysis is explaining how strong or weak each of the interactions is. This is usually described as as high or low power. The power of each part is relative to your strength/situation. If someone has a lot of power, that means they can demand lower prices to buy, or higher prices to sell. The power of this model is it visualizes the strength of the various relationships. 

Still, let’s walk through the steps, to explain the value in the various components. And that pesky “plus one” that doesn’t fit neatly into the chart.

You start with suppliers to the west. Say your main supply is a commodity, like oil or corn or even energy. Well, the suppliers don’t have a lot of power to charge you higher prices since commodities tend to be priced at what the market can bear. If, on the other hand, you have a monopoly on the supplies—say a patent on a new drug—you can charge exceptionally high prices. 

For potato chips, you’ll notice there are many more supplies required to make the final product than just potatoes. However, for the most part those other supplies are still commodities, so my gut is the suppliers have low pricing power for the potato chip manufacturers.

On the east side of the model, you have buyers. Buyers are not “customers” necessarily and absolutely not necessarily “consumers”. If buyers have lots of power, they can demand lower prices. Take gas stations: I can always drive to the next one. If their power is low, it means they have to price low to get your business. Another currently relevant versions is tax returns. Only a few company offer the services, and they’re really hard to do, so consumers have less bargaining power. (The fewer options, the less bargaining power, in general.)

Let’s head to the middle of the model. The value chain is usually silent on this part: what is it like for you at your part of the value chain? If there are lots of players, then competition can be very fierce. Again, think commodities and all the potato farmers showing up to the farmer’s market at the same time. Or the ice cream truck wars of Portland. On the other hand, in heavily consolidated industries like cell phones or cable companies, isn’t it funny how they all sort of charge the same price, for usually bad products? That’s a lack of rivalry among firms. 

For potato chips, the main thing is that while it seems like there are a ton of potato chip options, uh, there aren’t. Frito-Lay owns them all. Doritos, Cheetos, Tostitos, Ruffles. All Frito-Lay. While there are smaller brands that have entered and expanded business in the last twenty years—Kettle Chips mainly—I’d say that rivalry is low among firms, because Frito-Lay buys most competitors, or uses its power to keep its shelf space with buyers. 

In general, the CPG companies have about 5-6 huge companies that own all the brands. I’m not sure if I’d call this rivalry fierce or not. I could argue either way. On the pro side, there is a lot of battles over price and the companies run small margins (see Kraft and Buffett right now). On the con side, with only 10 major players, there is a lot of unspoken agreements on behavior and sharing shelf space.

57ebc2d7077dcc0f208b7830-750-500

The other thing that can really drive down margins is the ability for new firms to enter. (And this will probably be the key to discussing entertainment next week.) Look to the north of the model for this. If it is really easy to enter a market, then the barriers to entry are low. This can also force firms to keep prices low to ward off entrants. If the barriers to entry are high, then you can keep charging high prices or capturing value. Think cable companies for forty years in this way. Since it costs a fortune to lay fiber optic cable, the barriers to entry are very high. (Notably, Google’s adventures in fiber optic cable have been bad, but every tech company wants to launch a video service.)

The barriers to entry are high in consumer packaged goods, even though they shouldn’t be. This come from the massive consolidation at the center of the industry, which means the current players can try to box out upstarts. This is complicated to explain—and I don’t have enough numbers to prove my point right now—but it is easy to start a hot sauce, barbecue, or even chip company. It can be impossible to get national distribution. (And I don’t credit some sort of clever business strategy for this, but industry consolidation.)

The only new entrant I can think of is Kettle Chips, at least nationally, in America. Twenty-five years ago, they weren’t a thing and now they are. (Why? To summarize, a family company sold their company to a British PE firm, who later sold it to one company, who was bought by another and that company was bought by…Campbell’s Soup. Who didn’t even make the above chart.)

(Oh, all of these business examples are from America, for my foreign readers. That’s the market I know best.)

But while it can be hard to break in, that doesn’t mean there aren’t alternatives for customers. This brings us to the “south” and a perfect example of how a Five Forces analysis can provide insights the value chain can’t on its own. Substitutes are things that can fill in for the core product. You can either think of these super broadly or narrowly. An Oscar worthy example of this was when Reed Hastings said Fortnite is a bigger competitor than traditional TV for Netflix, he really meant it is a substitute for leisure time.

Potato chips have many substitutes. Say consumers want something healthier, they could eat pop chips or Hippeas. Those are healthy items that fill the same need of something to snack on. Or nuts for protein. Or pretzels. Or Cheetos. Even tortilla chips substitute for potato chips. If you go natural, apple slices! Those are examples of other substitutes for snacking and you could even go into candy. (Though we risk starting up the “snack vs treat” debate.) Moreover, you could decide to just NOT eat chips, which is healthier anyways. So here’s our chart with my “back of the envelope” perception of power:

Screen Shot 2019-04-10 at 3.11.46 PM

So my take on potato chips that the biggest piece keeping prices down is that you can always choose not to eat them. You don’t need chips to survive, and actually will live longer if you eat less of them. But that’s my gut take without pulling specific pricing numbers. (And if someone knows CPG better than me, which is a lot of people, shoot me a note if I got something wrong.)

Oh, did you notice that new piece, in the upper right? After rolling out these forces, Porter later added the “plus one”, complements. These are things that add value to the core product. For potato chips, think ranch or onion dip. Dips take a plain potato chip and kick it up a notch. Or salsa on tortilla chips. They aren’t in your value chain (presumably), but they definitely impact your bottom line.

Connecting the Value Chain to Five Forces

You may have noticed my sellers, incumbents and buyers had the same shapes I used in my value chain. I used the same shapes, because frankly, you can see that the value chain is really just the middle line of the Five Forces model. A connection that literally no one (that I know of) made in business school. They were basically two pictures of smaller parts of a much larger image, which is roughly the interlocking pieces of any industry. So I combined them, and since I felt like I was creating something new, I gave it my own name:

The Value Web

I call this my “insight” because again I really haven’t seen it in other places. And most of the maps of entertainment ignore the value chain component, instead using company names. the value pieces, as opposed to just company names. The name is simple: Value, because I love it above all else, and web, because it explains not just one layer but all the interlocking pieces. 

(I haven’t seen this term in other places, but Clay Christensen uses the term “value network” in The Innovator’s Dilemma, so it may not be that clever of an idea. And Deloitte uses it here, but focused on the supply chain.)

So let’s look at a value web for potato chips. 

Screen Shot 2019-04-10 at 3.12.03 PM

Some insights. Well, the traditional buyers are under threat from web sales. Not everyone (liquor stores are fine), but eventually, lots of things will be sold online, so every brand needs a web presence. Notably, some data shows that this means smaller CPG companies can expand their presence to new markets, so maybe the incumbents have less power. So that’s why understanding substitutes and multiple parts of the chain can be useful.

This is why I like the larger view. A value web will help a company at one level understand the substitutes and new entrants across the range of the value chain. And how that may impact their business. So Popchips are a substitute for potato chips, but online shopping is a substitute for grocery stores. A potato chip company probably needs to understand both those potential substitutes. 

Digital video has new digital entrants across a range of the value chain from producers (shooting video on cell phones) to streamers and eventually even bundlers. Youtube is taking eyeballs in a different way than Netflix, which is both at the end of the value chain. Online shopping is changing toy sales. Going back in time, even reality television—with its cheaper production costs—was a substitute for scripted television at the production level. 

The challenge with building a strategy web is one of simplicity and scale. Honestly, I’ve just about maxed out what I can build on Powerpoint (a tool which works for 95% of my image creation on this website) and what 95% of business folks use. I’ll need to look up other drawing tools to build a true strategy web. And even then, it will probably get update too frequently to last long. 

But we do have the two tools needed to analyze this industry in slightly deeper depth. Which I’ll start to do with my next big series and next week as I try to define digital video.

Did the Pac 12 Need a Strategic Partner – Director’s Commentary

(This article is Part 3 of my series on the Pac 12, including whether they should have brought on a strategic partner in 2012:

Did the Pac 12 Need a Strategic Partner in 2012? Part I at Athletic Director U
Did the Pac 12 Need a Strategic Partner in 2012? Part II at Athletic Director U)

When Athletic Director U reached out a couple months back, I knew I wanted to collaborate with them on an article. Live sports will be a key component of future digital video bundles, and they currently prop up MVPD bundles. And I love UCLA and college basketball/football. So that’s a no brainer on my end.

I don’t know if Athletic Director U knew what they were in for. When I finally sent in my finished piece, it had exploded to 3,000 words. It’s like a New Yorker article, destined to sit in an unread pile for being too long. (So we split it into two.)

I went deep into the Pac 12’s finances so, of course, I had extra ideas. Those ideas would have interrupted the flow I had and since I was long already, many of those thoughts ended up on the cutting room floor. So here they are, including additional homework assignments for myself (and hopefully follow-ups to Athletic Director’s U). Today is thoughts about the model and what I learned; tomorrow is an opinion article with my advice for the Pac 12 CEO Board, from a business perspective.

The Missing Piece: Bottom’s Up Analysis

My initial “concept of the operation” to value the Pac 12 was to roll out a back of the envelope, top down and bottom’s up look. Like most plans, it didn’t survive first contact with the enemy, which in this case was a lack of data. Here’s my half-built bottom’s up model:

Table 1 - Bottoms Up Model

It was still useful to build, even without the data, because I got a great sense of the drivers of the Pac 12 Network, and what I did and didn’t know. Still, a model with that many guesses instead of estimates would have misled more than it educated. How much does it cost to run the Pac 12? In what areas? How much do they make on advertising? I just don’t know.

Though, if I can ever get my hands on Pac 12 financials…

New Scenarios

One of the great things about building a model is, if you do it right, it can be very easy to update the model with a few inputs or tweaks and you can get a new output. And a few jump out that I absolutely want to build:

The $750 Million Equity Sale for 10%

I conveniently used $750 million valuation as the middle case in 2025, because that’s the number currently being trial ballooned by the Pac 12. (And it’s about one-third higher than the amount initially expected.) The key difference is this $750 comes a few years earlier than the 2024 “all distribution deals expire” scenario. Being 5 to 6 years earlier means the Pac 12 gets to keep more of the cash in a “time value of money” sense. So an equity sale could change the model. 

But not quite so fast. As hinted in today’s column by Jon Wilner, an equity sale isn’t a long term solution. If you get money up front now, presumably your equity partners gets paid later. (Otherwise, how do the bankers make their money back?) This would mean estimating how much distribution the partner gets starting in 2025. It’s really a trade off of cash flows. It isn’t about generating more revenue or cutting costs, but timeshiftimg your flow of cash. (More on this tomorrow.)

The ESPN Extension

This is an intriguing deal too. As reported by the Sports Business Journal and confirmed by Jon Wilner, instead of getting $750 million in equity sales, the Pac 12 could have extended their deal with ESPN to 2030, and ESPN would have taken over distribution (with a split of revenue, presumably) of the Pac 12 Networks. The Pac 12 passed on this deal and my gut is that makes sense, but I could still run the numbers on it to prove it.

A Higher Cost of Capital

Sensitivity analysis is the name of the game here. Basically, you test your model on various inputs to see how much it changes. I sort of already did that with the low, medium and high revenue loss scenarios. But the other big input is the “cost of capital” which is how much the Pac 12 would lose or gain depending on how much return it expects on its capital. As you’ll recall, the current WACC is 9.4% for entertainment, but I used a lower 8%. That was generous to the Pac 12.

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Read My Latest at Decider! Why Did Hulu Lower Their Price to $5.99?

Last week, I was thrilled to announce that I had a guest article at TVRev, and I’ve followed it up with an article over at Decider. The folks over at Decider asked me about the Hulu price decrease a few weeks back—which as I mention, was really a promotional price continuation—and at first I didn’t have an “angle” on it. But as I thought about it—and really as offers of free Hulu kept coming (by my count Spotify, WaPo, Sprint, with probably more)—I realized I had my view: This is just their “hook” to bring in customers.

So check it out and hopefully I’ll be appearing over at Decider from time to time.

Other Lessons from MoviePass’s Demise

Often, when I write a long article, I have extra thoughts. MoviePass—and its demise—may be my “story of the decade”, when judging off the “hype-to-cash flow” metric. (Remember when I used it to explain subscriptions? Or logarithmic distribution of returns?) Recently, I wrote about the lessons of MoviePass’s demise at TVRev (here for Part I or here for Part II).

Today’s article is is basically the “director’s commentary” of that guest article. Whenever I write a long article, invariably I have a ton of extra ideas. For example, in my first draft, I tried to find historical examples of companies that made my same mistakes. (What is old is new again, or just digital now.) I found some, but couldn’t find others—and I was already long—so I cut that idea from the initial article.

So what to do with all those extra pieces? Well, put them on my website! Enjoy more thoughts on MoviePass. Today is all about “additional lessons” to be learned from the fall of the mighty ticketing giant. These lessons weren’t as great as the initial four in my TVRev piece, but I still wanted to make them. Especially the first problem, which I see happening a lot. 

Lesson 5: Beware of upper 10% companies pitching themselves to the masses.

This is one of the underrated stories in business right now. You know who the business press doesn’t talk to a lot? Poor people. Sure, the political press ventures to Middle America to find Trump voters, and can’t help but write stories about the Dollar Store, but overall, most technology writers talk to software engineers or product managers or venture capitalists or lawyers or biz dev folks who are really, really well off. They don’t do a lot of interviews with the contract workers who are cleaning offices or serving meals or working in retail. (Unless it is an expose. But they don’t usually ask about their thoughts on the newest VC round.) 

(Politicians don’t know much more either, since most Congresspersons are millionaires. Same with the interns, whose parents are millionaires.)

There is a gap in lifestyles between the top 10% and the bottom 80%, especially the bottom 50% and the top 5%. MoviePass started as a top 10% company. If you’re an intern at a TV publication and your parents pay your rent, then yeah adding a $10 MoviePass subscription is no big deal. That doesn’t apply to a family eking by to pay rent every month. So MoviePass felt like an upper 10% product to me.

Who else does this apply to?

Not to pick on scooters again—as I did in my TVRev article—but they are a classic top 10% company. A lot of the initial hype around scooters billed them as a way to radically transform urban transformation. And suburban too! This never quite made sense to me, from a mass transit standpoint. If you can barely afford a car to get to work, can you afford a scooter ride?

Let’s do that math. Say you add a scooter to your daily commute. And it is $3 round trip per day (Which may be low, depending on the commute. This also means you live very close to work or to a bus stop, which I didn’t add to the costs.). Well, at the end of two years—assuming you found a scooter every day and never crashed—you’d have paid $1,440 to Bird or Lime or Uber.

Of course, you could have bought a scooter online for…$300.

Scooter rides aren’t replacing commuting (and the math on Uber is the same). Instead, my theory is that ridesharing and scooters are additional expenses to people’s lives. The majority of users—in Los Angeles at least—ride in Ubers or Lyft for convenience: Uber replaces one person in a group staying sober enough to drive when going to a concert or dinner. Any of the stories of people who gave up commuting for Ubers are invariably about wealthy business folks (definitely in the upper 10%).

Why do we get this so wrong? Because the early adopters are rich. At least upper 10%, but in some cases upper one-percenters. Given that they have the extra cash to pay for the convenience, they do it. And they assume this applies to everyone, even those scraping along at the bottom for pennies. This seems to be a feature of 2010 tech companies: they pitch themselves as cost saving, but are usually about adding convenience.

Uber/Lyft – Pay to avoid having to drive home from bars.

GrubHub/UberEats – Pay to avoid driving to fast food.

Amazon Prime – Pay to avoid having to drive to store.

Scooters – Pay to avoid walking.

The Grub Hub rise is the most fascinating one to me too. I mean, delivery from Thai or Chinese or pizza restaurants used to be free! Now we’re paying 10% on top? (In fairness, this convenience, can be value creating, it if boosts willingness to pay.)

The new wave of “bring it to you” from massages to house cleaning to car washes are just variations on the above principle: you used to drive to get it, now it comes to you, for a fee. Which doesn’t mean the companies above are doomed, but if the growth rate for a company—and hence its valuation—is built on 100% market penetration, ask if that is even financially feasible for lower income Americans.

Lesson 6: Beware who you sell your company to.

If MoviePass had been acquired by Amazon, wouldn’t it still be in business? Amazon would hide its revenue losses in some anonymous sub-category of its earnings—or add it as a benefit to Prime—and we’d have no idea what is happening. ($240 million a quarter? Piece of cake for Amazon.) Of course, I don’t mean to imply that Amazon has lost lots of money on other businesses it has acquired or built. But we don’t know, do we?

You can’t explain the demise of MoviePass without acknowledging that it got bought by the wrong company. It was acquired by Helios Matheson, a data company that couldn’t handle exorbitant losses month and after month. It could lose some millions as long as it stayed buzzy and that floated the stock price. But a not-Fortune 500 data company can’t handle losing tens of millions every month like an Amazon or a Netflix. 

Lesson 7: Beware wildly fluctuating prices and/or UX.

Some companies just feel shady to me. Some hallmarks for me are… 

Hastily designed websites. Honestly, do you trust a company who looks like they are working on HTML 2.0?

Dozens of subscription options. Why so many? Where is the catch? 

Or promotional pricing. Is it really 40% off today only? What is in the fine print?

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