Predicting Independent Film Sales in 2020 and Beyond

Here’s a very quick sampling of headlines from Variety after the Sundance Film Festival the last several years:

“Sundance [2015]: Sales are Booming, but will Filmgoers Respond?”

“Sundance [2016]: How Netflix and Amazon are Dramatically Shaking Up the Market”

“Sundance 2017: Why the Market Is Still Hot”

“Sundance 2018: The Slowest Sundance Ever?”

I’d add, in 2019 Variety’s headline wasn’t sexy enough to make the list, but it’s first of five points was, “The Era of the Mega-Deal Returns”.

Those are great headlines for aspiring filmmakers. One natural reaction would be to say, “Hey, independent film is booming! Let’s get on it.” Of course, midway through this year we saw this headline also from Variety: 

Yikes! How will that performance impact the independent film landscape? If Amazon and Netflix pull out their hundreds of millions of dollars will it collapse? Maybe? As if to confirm our worst fears, here’s Amazon Studios head Jen Salke on Amazon’s “evolving” strategy:

If “pivot” is code for “spend less”, that’s bad for smaller films. 

Independent film financing has been in my head over the last few months because of a consulting engagement (see post script) and the question I keep asking myself is probably the same one most filmmakers are. It also happens to be a great prompt for a prediction: how will independent film sales do at Sundance (and other festivals) in the next few years?

We’ll follow my “EntStrategyGuy Prediction Process”--and another example here--but in full-disclosure, today is not actually about showing that we can make predictions, but the limits of predicting the future. Especially when we don’t have enough data to do so.

Step 1: Lay out the problem

In this case, I see a few options for our “y” or “dependent variable”. (For those who took statistics a few years back, that means the “outcome”, basically.) To start, we could estimate the number of projects sold at film festivals. Alternatively, we could care more about the value of those sales. Or, maybe just look at the top deals (meaning the most expensive film sold). Or a count of films sold above a certain threshold. Say the number of seven or eight figure film sales. All those are totally valid outcomes/results/y/dependent variables here.

We could also look at the “average”, but it will be slightly misleading. Film sales at festivals tend to be logarithmically distributed (as is all entertainment): most sell for nothing; a few make a million or so, and a tiny sub-handful go for tens of millions. 

Thinking about it, I’d go with one of two options:

Option 1 - Total Sales - In 2020-2022, the average annual total sales amount from independent film festivals—either Sundance alone or with others—will average $XXX.

Option 2 - Number of mega-deals - In 2020-2022, the number of films sold for seven or eight figures from independent film festivals—either Sundance alone or with others—will be X.

Now we predict the bold.

Two notes on definitions before we keep going. I’ve been using “independent” film here, and that’s now a loaded term that sometimes is even applied to studio films. In the Sundance film case, I really do mean films financed independently and then sold to distributors. While most of these would be what I call “prestige” films, which are films produced to compete in awards festivals, that isn’t mandatory. These also tend to be “low budget” films, which again isn’t mandatory, but usually the case.

Step 2: Set the baseline

One of the simplest steps to making better predictions is to just start with the baseline of what you’re measuring. You’d be surprised how often your guess of the baseline doesn’t match the reality, especially if you read only headlines.

And here’s where we run into trouble. Essentially “option 1” is straight out for this project. While parts of entertainment are very open—Nielsen ratings, box office gross, Youtube views—other parts are as dark as can be—film sales, talent contracts, streaming views. So searching for total film sales at Sundance, I couldn’t find any reliable numbers, mainly because most sales are kept hidden.

Option 2 isn’t much better, but we have slightly more data. Fortunately, while they are still mostly hidden, for the high end deals, the details are usually leaked to the trades. So we know the prices for films like The Big Sick, Patti Cake$ and Mudbound, for example.

The challenge is that while I could make this table…

…it’s not complete. I found articles summarizing film sales, sometimes even “complete lists” but invariably they missed a film here or there. This is especially likely for films in the seven figure or lower range. Still, it gave me this chart/table which is a good summary of how noisy film sales can be:

What does that tell us about the trend lines right now? Well, there isn’t one. Essentially, 2014 to 2016 saw surges in sales, then 2018 was a big fall off. Then by 2019 big deals were back, though the market overall felt soft. (And the decline in seven figure deals in 2019 is probably more a lack of film sales reporting, as opposed to lack of sales.) In other words, the market can experience highs and lows in back to back years. Understanding why should make us even more nervous for our forecasting enterprise.

Step 3: Analyze the Narratives

After I have a baseline, I start reviewing the drivers of the outcomes. What statisticians would call “categorical” variables. Or what critics would call the “narratives”.

A “categorical variable” is a descriptor of your data set. If you asked yourself, what are the most important categorical variables for “independent film sales at Sundance”, here’s some ideas I’d come up with:

- Number of bidders

- Quality of films

- Success of films in previous years at box office

- Success of films in previous years at Academy Awards

Presumably, those things are all correlated with more sales. Box office and Academy Awards imply the business model is working; if the films are high quality, presumably they’d make more money.

But the one thing I can’t get past—and the variable that dooms this enterprise—is the first variable: the number of bidders. And it’s a variable that is really small and very powerful. 

Here’s my abbreviated history of independent film buyers. In the 1990s, Harvey Weinstein invented the idea driving independent films to profitability by running awards campaigns. (This is not a comment on Weinstein as a person—which is obviously very negative—but he did popularize this business model.) During the 2000s, then, all the major studios launched or built out their mini-major studios focused on these independent/prestige films. You had Fox Searchlight (21st Century Fox), Focus Features (Universal) and Sony Pictures Classic (Sony) competing with The Weinstein Company. Some smaller companies copied them too, like A24, IFC Films, Relativity and Open Road.

Then Netflix and Amazon launched headfirst into the independent/prestige world. With huge checkbooks, the prices exploded. Basically, whether or not Amazon decided to spend tens of millions at Sundance determined if the market was hot or cold. Which is what happened again this year. 

The challenge is that at most we’re talking about 4-6 huge buyers here. That’s a small sample size. And if one changes their minds—either buying or not buying—that changes our outcome. Which is what makes this noisy.

As for the future, one could make arguments either way. Maybe Hulu or HBO Max join the fray, seeking to pay for buzzy independent films, which would send costs even higher. Or they could not, and Amazon and Netflix could start curtailing their spending. 

In other words, the decisions of a handful of independent actors could singlehandedly swing this market. Which makes predictions extremely fraught. Which means we need to give a huge confidence interval.

Step 4 - Making My Prediction

So much so, that I’m not going to make a prediction. I’ll give a range, because that’s all I feel comfortable saying. So I’d say that the number of seven figure deals in the 2020-2022 time range could be 1 at the low end to 12 at the high end. That’s my “90% confidence” interval, meaning 9 times out of ten I expect that number of sales. At the eight figure range? I’d say anywhere from 0 to even 5 films could sell for eight figures. Anything above five films seems like a 10% or less likelihood.

Is this prediction that helpful? Not really. Which is sort of my point today. Making predictions is a fraught enterprise even with great data, so it’s worth knowing when the data is poor or small—as this is both—then it gets even harder.

Now, if you’re making an independent film, should this data stop you? Probably not. If you secured financing somehow, make your project. If you’re a financier, though? Well, I’d have a few more questions for you.

Post Script

Full disclosure: One of the reasons independent film sales interest me is that one of my current consulting gigs is for a friend making an independent film. He has an original pitch and already secured half the funding, so I’m helping with the other pieces, including a larger business plan. And how do I know the pitch is good? Well I have a guy who reads scripts for me that I trust—he analyzes scripts with the same honesty that I analyze business plans—and it’s one of the best scripts he’s read. 

Still, I focus on the economics. Along the way, I pulled some data on independent films. I asked myself, “How could I turn this research into an article?” And this is the result. 

(Also, if you’re a financier or distributor interested in hearing the pitch—which wasn’t the point of this article, but always be selling—send me a note.)