My Process

Most organizations–from corporations to sports teams to armies to universities to government agencies to non-profits–think they use a sound process to make important decisions.

But they aren’t. They are arriving at the easiest decisions.

I’ll use a media example to show the worst process. Basically, political pundits opine and opine and opine for months about an upcoming election. Then when it happens, they go on air and, if they were wrong, ignore what they said before, or, if they were right, embrace it wholeheartedly. Either way, they develop a narrative to suit whatever the data says. That’s an easy process.

The opposite process is much harder. In my opinion, the gold standard of openness is the journalism of FiveThirtyEight. Nate Silver and his team go out of their way to be open with their process, even explaining how their election model works (or doesn’t). They also write articles owning up to mistakes when they make them. The highlight was when Silver admitted that he didn’t acknowledge the data that showed Trump’s underlying strength during the primaries. He corrected and didn’t make that mistake in the 2016 general election.

I wish corporate America had such an open process.

I want to emulate Nate Silver’s approach to tough problems. He has politics and sports covered (along with some other great journalists), so I’m going to focus on entertainment. I want to ask big, interesting questions, and use a sound decision-making approach to find unique insights and answers.

Honestly, I want to get better at analyzing problems and hopefully making better decisions overall. I want to be more accurate. To do so, I am going to use the decision-making process I wish I could have used in my last job. Yeah, I stole Nate Silver’s philosophy, but I’m tailoring it to my style.

That’s the process I want to explain here, once, so I can reference it in all future “analysis” articles. (I’ll explain what those are in a separate post.) I want to be very open with my process. This way, when people criticize my conclusions, I can point to my process and ask for recommendations for how to improve that. Hopefully, it will also make other people better at understanding their processes.

Part 1: Blink Analysis

I start the process with my “blink” analysis. Yes, I stole the name from Malcolm Gladwell’s book. Like any long Gladwell topic, you never quite know how well the initial ideas have stood up to longer-term academic scrutiny.

But the core idea is fantastic. As soon as you hear something, you get an instant reaction. And I try to capture that as soon as I get an idea for a question. I try not to think about it, but just put my thoughts on paper. Consider this my “looking at a statue and calling it fake” analysis for those who have read Gladwell’s book.

My working theory is that most of us do this ten times a day in conversation. You hear some news, “Did you hear that so-and-so was hired at such-and-such company?” and draw a conclusion, instantly. We just don’t acknowledge it. I’m trying to acknowledge it up front.

Part 2: The Gut

My next step is the “gut”. Here, I try to write down what I think about a question, but my goal is to reflect a bit deeper on it. Whereas the blink is a paragraph, this could run to a page or more.

The best analogy is the “case question” in an interview. In my analysis articles, I’ve asked a challenging business question (to myself) and now I need to see if I can answer it. And like a case study question, I won’t have any data to go from. I can divine some data I think I know or know for sure, but I can’t use the internet to confirm it. So my “gut” analysis is deeper than blink, but without the numbers that come from part 3 of the process, my analysis.

Honestly, when I set up this process, I realized how often this is essentially what we do in a business meeting. Sure we get initial blink answers, but as we have a conversation about an important issue or the future of entertainment (say at a conference talk or in a group or around a table in a meeting), we’re having a “gut” conversation. We can’t pull up numbers, so we do our best guesses.

The reference book here is Thinking Fast and Slow, or The Undoing Project, which are stand-ins for the work of professors Kahneman and Tversky. Two of the founders of behavioral economics, they basically show that “blink” goes wrong when it relies on misleading heuristics. In other words, your blink can often mislead you. The gut stage is supposed to short circuit that quick analysis to allow a moment of reflection.

That said, the gut may be the most dangerous or seductively appealing of the three stages. My “gut analysis” is NOT my final answer. The gut can be tremendously misled by assumptions we haven’t fully explored because we don’t have enough time to do it. Or the data doesn’t support it. It gives us unearned confidence.

This is why I think Hollywood overall makes most of its bad decisions, it stops after the gut decision-making. So how do we make our gut a little more accurate? We do the analysis.

Part 3: The Analysis

This is really the meat of the process. This is where we make better decisions. And it’s the toughest part.

So what is it? It is about deciding on a framework, and putting numbers to that framework to derive an answer. It won’t be the same process each time—that’s asking too much from any framework—but every process should have some numbers. Those numbers are then analyzed. Throw in more quantitative and qualitative data along with history and experience. But it is ultimately boiling down to some numbers, whose judgement I have to accept.

A key to making this work is to do the analysis myself. In most cases that will mean asking the right questions, finding the data, building the models and thinking about all that I’ve analyzed. The key, though, is doing the work myself. I specifically want to avoid the thoughts of others as long as possible in this part.

Why? Because I want to be part of the experts in the crowd, not the crowd-source.

Let’s explain that. Take any one expert. Usually, they are pretty inaccurate. Sometimes they have hot streaks, but overall they can be wrong often. A real world example is NFL experts picking games. Any one expert gets a lot wrong. But if you poll a dozen experts, they’re more accurate than the majority of experts. In entertainment, the gold standard is Gold Derby, whose poll of Oscar predictors is more accurate than most other predictors.

Another example is Nate Silver taking a poll of polls. He doesn’t run his own poll—a new set of data that can be inaccurate—but he polls the polls to make them more accurate. In this analogy, I actually want my analysis to be like the NFL expert or individual poll or the individual Oscar predictor. I’ll be inaccurate often, but if you combine my analysis with others, you’ll have a more accurate picture of the world.

By doing the analysis myself, I’ll come up with unique insights. This means I’m adding analysis into the world, not simply parroting opinions I’ve read elsewhere. I don’t want to be the person collecting the crowd-source, but adding data and analysis into the world you can’t find anywhere else.

My caution is I’m doing this because I can. I don’t do this exhaustive analytical process for fields I’m weak in. Take fantasy football. I can’t watch every football game in the NFL, so I don’t try. Instead, I rely on experts who do and statistics that have predictive accuracy. I think I can add my own individual expertise into the analysis of the entertainment industry because I excelled at this analysis in business school and in my career, and I consider myself an expert. I wouldn’t be writing all these words otherwise.

Part 4: Research and Other Opinions

Doing analysis myself doesn’t mean I ignore other opinions and analysis. After I’ve done my opinion, I head to the crowd. See what they think. Then, I weigh whether any of it has a strong enough weight to change my mind.

In some cases, like comparing “Atom Tickets to Fandango to Movie Pass”—a future article I’m working on—there won’t be a ton of people weighing in on the issue. Or there won’t be a substantive analysis to compare my own to. In other cases, like evaluating The Walt Disney Company-21st Century Fox acquisition, lots of people analyzed that deal at the time.

There is another analogy to polling that provides the one caution at this stage. One of the things Nate Silver worries about is when he sees polls cluster around the same result. This worries him because he thinks pollsters are subtly manipulating their polls to arrive at a conclusion. The polls do this because they don’t want to seem like outliers with the negative connotation that comes from that.

I think this phenomena plays out all the time in the world of business. Doing your own analysis takes a lot of time, so it is easier to let someone else do it. So a lot of the business world (and stock market) cluster around similar opinions even if their own analysis pointed somewhere else. (Or would point to it if they took the time to do a clear process.)

So after I do my own analysis, I’ll read other people’s thoughts and update my thinking. See where I go against the grain and see where I don’t. This may be in my analysis or in later updates, I haven’t decided yet.

So that’s my ideal process. Now it’s time to see how it works.

Theme 1: It’s Not Data, It’s Decision-making

The big buzzword in business is still “data”. Or better yet, “big data”. Or more complicated sounding, “analytics”. Better than just analytics is “advanced analytics” which is like analytics but more advanced. With all that data, a bunch of “algorithms” are figuring everything out.

Take your pick. Sure other trends have picked up in the last few years—how about disruption anyone?—but since I started business school, data (or one of those words related to it) is everyone in business’s obsession. Companies launch whole businesses now who run off business models entirely related to collecting customer data. Facebook’s Cambridge Analytica revelations just brought this to the fore.

And this “data revolution”—another term—has come to entertainment.

Especially the streaming video services. They get so much data that was never there before, including the entire viewing history of a customer (and their shopping history on Amazon). So when I talk to MBA students—either at alumni events or recruiting events or in a classroom—I invariably get asked this question: “How did [your streaming platform] use data to pick which TV series and movies to make?”

I can’t help myself from sighing. And being slightly sarcastic. So instead of answering, I usually flip the tables.

I ask a simple question (if say I am in a classroom). Something like, “Let me ask you, when you decided to take this course, what data did you rely on to make the decision?” Usually, the answer is some stumbling around, ultimately ending on someone had recommended the course to them. I point out that this is data, a qualitative piece, but still a personal recommendation.

Notice that word I paired the word “data” with “decision”. So let’s use a real-world example. From my life, but anonymized to protect the innocent.

A group of Hollywood executives are sitting around a table. They only have the budget to renew one more TV show to release in the upcoming year. The decision is around two shows. The first I’ll call, “Cop Show”. It’s a show about police officers. Yeah, creative title. The other show is called, “Awards Show”. If you went into a laboratory to make a TV show to win awards, this was it.

Now, we have to be clear, there is no lack of data. The studio execs have reams of it. They know have how many customers watched each show. They know what shows customers rated highly. They know what critics thought. They have surveys of customers and have conducted focus groups. They think one show will probably stay more popular, but will never win awards. The other show will likely win awards, but customers didn’t watch the first season. But they have a ton of data to draw on at that table to make these conclusions.

Of course, in addition to all the data on the table, there is a lot of data off the table. Pieces of information influencing the executives, but that doesn’t make the strategy powerpoint that justifies the decision. For instance, some of the development execs have friends helping make one of the TV shows. Some development execs are considering whether a popular but uncritically acclaimed show will help their chances at getting their next jobs. And the marketing execs want the easiest show to market.

To top it off, some people just like one of the shows a lot. It’s their favorite show on television. That happens.

This is why I asked the students about the “decisions” they had to make. The execs have a ton of data…how do they use that data? Data doesn’t make decisions, the people do.

Big decisions, like what MBA school to attend, have a lot similarities with picking a TV show at a network, which is why I used it as an example. A prospective MBA student has tons of data to pour through. Though a lot of that data is qualitative. And some of it is on the table, and some is subconscious. And a lot of it has vague predictions about the future: which school will I enjoy the most? Which school will help me get the best job? And in what field? That’s making assumption about the future. Every MBA student had to decide where to go to business school; likely, they didn’t have a data-driven process. They picked the highest ranked school from a list of school rankings whose methodology they probably barely understood.

Because, honestly, people don’t really like using data to make decisions.

Let me say that again, because it is the biggest myth in our business world right now: people don’t like using data to make decisions.

Most huge corporations already have a well-worn, time-tested, established system to make decisions called: HIPPO. Highest Paid Person’s Opinion. (I’m not the first to write this.) Basically, you have a CEO making anywhere from $1 million to $50 million dollars. What if you gather a bunch of data that says they’re about to make a huge strategic mistake? Will they change their mind?

Of course not! They’re the boss.

To reverse course would admit that they were wrong. And this goes all the way down the chain. Say you are a middle manager who is convinced to make some key decision. You decide to change how your team processes something or other related to payments. But you also have a Business Intelligence team. A BI team that developed a sound forecasting methodology, with clean, reliable data and that team’s new methodology argues against your strategy. Whose opinion do you take, the data or you?

You pick you! And that’s what development executives not just at the streaming service I worked for, but for all of Hollywood, do on a daily basis. It’s what financial executives do when making investment decisions. It’s what corporate strategists do when determining their strategy. You can develop a data-driven approach to making decisions, and really think about what decisions you make, or you can go with your gut.

This isn’t to say business folks don’t use data. They have tons of data to read. Like those studio executives around the table. They make reports justifying strategic decisions that are loaded with data. And they may even listen to it a bit. Let it inform what they think, at some level. But the key is executives are the ones making the decisions, not methodologies. Not the data. And with tons of data, more data than ever, it is even easier than ever to construct a narrative that you’re making sound decisions.

This is why I return back to the core theme of this blog: it isn’t about data, it is about decision-making. The goal isn’t to just have data; it’s to use that data to make better decisions. Making decisions is about much more than regression analysis or random forests or neural networks or some other complicated algorithm. It is about asking the right questions, to even know what decisions you are making. Then you figure out your process, your methodology, your data and how you will measure success. Then you let the process make the decision, not your gut.

You cannot make wise decisions without data, but instead of focusing on the “data’ we need to focus on the “decisions”. This idea will come up so often, when I write about entertainment strategy, that I felt the need to make it my first “theme” of my writing, a point I will return to again and again.

As an undergraduate, I heard a description of hierarchy that has stayed with me Basically, the professor said, “salary in an organization is determined by the value of the decisions you make. If you’re decision is, ‘Do I empty this trash can?’, you get paid the salary of the janitorial staff. If you’re decision is, ‘Should we enter China or India as our next market?’ you get paid like a CEO. One of those decisions is valued at cents (the trashcan) the other in millions or billions (entering new markets).”

This site is concerned with those decisions as they relate to entertainment. And making them better.

Data isn’t the solution, better processes and decision-making are. And a lot of that better decision-making will say, ironically to this entire post, to use more data in places where it isn’t being used at all (lots of parts of entertainment) to help make better decisions. In the long run, we can even identify who is making the best decisions and reward them, instead of the executives at the top who likely aren’t making the best decisions, or ensuring they even have a good decision-making process.

Hopefully this website, over time, will help you and your company do that better.

Why Does Hollywood Make Bad Movies?

Before I start writing on a new project, I often feel compelled to explain myself. Why am I putting my words to paper (or coded into bits, since you’re reading this on the web)? Why are my words worthy of your time to read? Why do I want to get these thoughts out there?

On a really simple level, I think I can provide insightful analysis on a topic (entertainment strategy) that I haven’t really seen. I’ve seen good journalism on the goings-on of entertainment; I’ve read good writing on the statistics of entertainment; I’ve seen good articles on the future of entertainment in general; but I haven’t seen the one place that ties it all together.

That’s a gap in the market I think I can help fill. Still, what’s driving me to write to fill that gap?

Basically I want to answer the question in the headline, “Why does Hollywood make bad movies?”

This seems to be the du jour criticism of Hollywood. And it has been since I started reading the Los Angeles Times annual preview of upcoming movies as a child. Critics have consistently bemoaned the state of the US film industry and the quality of its movies. They’ve criticized the number of sequels, which in recent years has turned to criticizing blockbuster franchises and/or superhero movies. But it’s not limited to film. Those same critics have bemoaned the state of TV or video games, even as we entered a golden age of television and independent games studios thrive.

Those same critics who bemoan the state of Hollywood have also tried to offer explanations for why Hollywood makes bad movies: It’s studio executives trying to make more money off sequels. It’s studio executives trying to make movies to sell toys. Or happy meals in the 1990s. Or its studio executives who won’t give creative freedom to creative types. Or it’s studio executives who care about international box office.

So it’s the studio executives. Hmm. It’s like we could modify the NRA slogan: “Hollywood doesn’t make bad movies, studio executives make bad movies.”

The critics are onto something, but they just can’t explain why. Or I’ve never heard an explanation I love. After blaming bad movies on studio executives, they can’t answer the key logical fallacy they just exposed: why would these studio executives willingly make bad movies? They don’t want to make bad movies.

Instead, I’ve always felt the explanation for why Hollywood makes bad movies is fairly simple: it’s the decision-making.

But I feel a bit like Han Solo after Obi Wan Kenobi drops the “without Imperial involvement” line in the Mos Eisley Cantina. If you asked, “The decision-making. Hmm, can you explain that to me?” Well, that’s the rub, isn’t it? Which is how Han Solo responds.

Well, it isn’t just decision-making. Studio execs make these decisions because of how they perceive or understand the business of entertainment. So it’s decision-making in the pursuit of the business of entertainment.

Studio executives make decisions to further their interests and, ostensibly, the interests of their company. And these decisions are are influenced by winner-take-all economics with logarithmic returns. The winners tend to be blockbusters, with additional revenue streams, so from a portfolio perspective, executives decide to make more blockbusters. Yet, blockbusters are poorly correlated with awards success, which is highly correlated with critical acclaim. So that’s what many studio execs try to do, and why many critics criticize their movies.

But that paragraph feels woefully incomplete. Each of those sentences could be its own post explaining the decision-making and economics and business and organizational behavior of studio executives. All the things that influence the decisions they make on a daily basis. Basically, each part of the above paragraph could be one paragraph explaining how Hollywood does and doesn’t make good decisions.

That’ll bring us back to the question at the start of this post. All those ideas–and more–align on the same theme, “Bad movies get made because people make the decision to make bad movies.” That’s right, instead of wondering why this happens in confusion/befuddlement—a sort of throwing up our hands and saying, “Man where do these bad movies come from?”—I want to say, “Bad movies are made because studio heads, development execs, marketing execs, creative talent and others make bad decisions.” And they make these decisions because of how they understand the business of entertainment.

Honestly, I don’t see another website out there that is trying to explain how and why Hollywood works. I want to try to do that.

But it won’t just be movies. Or TV shows. I want to dig into the business of entertainment and media (and tech when they intersect) across all forms of entertainment. And that means studying and explaining and analyzing and critiquing the strategy of various entertainment, tech and media companies. By studying their strategy, we can learn more about the business decisions they make around everyone’s favorite product, content.

That’s why this website exists. I want to explain Hollywood and how it works from a business perspective, with side trips into economics and data. I want to explain Hollywood in the lens of the decisions people make, and how that affects what makes it into the marketplace. I think these explanations could help business people and creative types make better decisions. And hopefully help us all understand why Hollywood does what it does. (But really the people in Hollywood.)

If we know why we make bad decisions, well, maybe we can make fewer bad decisions and more good and great decisions.

Welcome and happy to have you.

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