Tag: Hollywood

Who is a “creative” in Hollywood? My Creative-to-Business Spectrum

When I worked at a studio, I found it funny that we referred to our development execs as the creatives. Not that they were creating the shows, but compared to finance or strategy folks, development execs were way more creative. They read scripts all day, took tons of pitches, provided story notes and helped decide who to cast in the show. That’s pretty creative work, when you think about it. 

So I had to cut them some slack if they couldn’t quantify everything; they’re creatives!

I say funny, because along the way I heard some talent on one of our shows—a showrunner, so the top writer/producer—refer to our development executives as the “suits” at our studio. And, they weren’t wrong?

I’d never considered the development execs the suits, but if your only point of contact with our studio is a development exec, then they seem like the business side of the house, don’t they?

It all depends on your point of view for who is a creative, doesn’t it? The director probably seems like a suit to an actor—an authoritarian bossing them around—while that same director drives the producer crazy with their creative demands. Meanwhile, the production folks are just trying to get shows made, which makes them seem like creative types to the finance folks just trying to get everyone paid. 

As I was starting my website—writing the first articles and sketching out a business plan—I set about to define my target audience. I knew I wanted to target the business side of Hollywood, but thinking about “what is business versus creative?”, I realized there isn’t just two sides on the “creative vs business” battle, but it’s a spectrum. 

Here is that spectrum that I jotted down and eventually turned into a Powerpoint slide.

Creative vs Biz Spectrum

For the most part I think everyone on this line would call everyone to their right a “suit”. Which means business. So I like this spectrum.

Some quick insights

Definitions

A lot of this depends on what I define as “creative” versus “ business” in the first place. I used those terms since that seems to echo the jargon in the industry. I debated calling this left brain-right brain, though I’ve never liked that terminology since apparently the science behind it isn’t great. I also debated some other definitions (see below), but this worked best.

And the reason I think it works is it captures two inherent tensions, in my mind. First, who cares most about making the product? The closer you get to it, the more you are talent, actively crafting the final product. A creative. On the other side, who cares most about the bottom line? Well, the business folks. If you want a rule of thumb, ask this question, “Who would care the most about going over budget?” The more you care, the more “business” you are.

I debated calling this the “qualitative versus quantitative, but that doesn’t work either.

Or you could call it the “gut versus data” debate. But that doesn’t get at the difference between the business folks and the creatives, really. Some business folks eschew numbers, sort of like the development execs I mentioned above. That’s a pretty qualitative group of people—in my experience—though they are more business than screenwriters.

Creativity is the pretty clear driver on the left. And the opposite of creativity isn’t data. Data analytics and math actually require a lot of creativity. Not that business should be the death of creativity, but it’s what we all assume.

Not Included Jobs

These jobs aren’t left out because they don’t deserve a spot, but because I ran out of space. And for some, I didn’t know where to fit them in. As is, this was a pretty clean line of the people involved in getting a piece of content out there in the world. 

I did want to get in the below the line folks—like set design and make up and wardrobe—but again couldn’t get them to fit neatly. They would be on the more creative side, though to the right of some talent because they start and end with a budget. Precisely where, I’m not sure.

I had no idea where to put production assistants. Probably near the directors—which is where many want to end up—but they aren’t really creatives, just following orders. Programming folks balance both and are probably in the middle. Script readers are likely on the creative end, as they are usually aspiring screenwriters themselves.

Did the spectrum help with the website?

Definitely. I knew my goal was to skew towards the business end of the spectrum, but this helped put what jobs are in that side of the spectrum. And how close or far they are from the creative end. While I think everyone in Hollywood could learn something from my website, the business side could probably apply the most.

And it helped convince me this is a niche I could grow. There is a gap, in my opinion, between investor-focused publishers, who mainly parse 10Ks for stock price information, and the Hollywood trades, who focus on the who is cast in what.

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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