(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
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