![]() Math and science can never completely solve the mystery of predicting film outcomes. With this tool, you'll get to know the people who have historically been interested in the subject matter of your film and come up with creative marketing strategies that feed the right content to the right people. You can allocate marketing resources much more effectively according to a target market demand (more bang for the buck). If you are an indie/low-budget filmmaker who is self-distributing your film or operating under a tight marketing budget, this information can be extremely useful. How can all of this help filmmakers succeed? ![]() With this tool, you'll get to know the people who have historically been interested in the subject matter of your film and come up with creative marketing strategies that feed the right content to the right people. Not so surprising! It's clear that by using the location map above, we can see where the demand is for the film. The movies below the benchmark are classified as under-performers. The movies above the benchmark are classified as out-performers, earning more revenue than the market benchmark. In the graph, 50% of all films are above the benchmark and the 50% are below. The green line in the middle of the graph is the benchmark that illustrates the relationship between marketing spend and revenue. How to measure a movie's success using dataĮach data point in the diagram represents a single movie, where its box office revenue is measured on the vertical axis and its marketing spend is measured on the horizontal axis. However, we do have data on the performance of most of his films, and we will analyze Relativity Media’s films’ revenue using Greenlight Essentials’ patent-pending software to draw useful insights from the results. Likely, he misused Monte Carlo without fully understanding the fundamental framework of statistical modeling. Without knowing all of the above information of Ryan Kavanaugh’s model (I don’t think he told anyone these type of models are usually highly proprietary), it is very hard to say what he did wrong. How do we formulize the revenue of a given film (Film’s Revenue = Actor, Plot, Production Budget, etc.)? Is it even possible?Īll of these are factors that can determine the performance of the final model, and many of these factors are assumptions. However, for complex problems like movies, there are many more unknown variables. In the restaurant example, we already know that the underlying model is Profit = Revenue - Expense, with the randomness of the revenue and expense matching the randomness of the dice. While Monte Carlo is useful for recognizing risks and variation involved in the already built model, it certainly cannot tell you which variables to put into the model and how to formulate the relationships-such as between actors, directors, and plot-in any given film. Most of the time, making these choices means taking an educated guess: by observing real-world data, you try your best to match it. The person who builds the model needs to design the underlying input variables the model is going to use-the relationships between these variables and the statistical distribution he/she is going to use for the model. Statistical model-building is both a science and an art. However, Monte Carlo does not build models. Monte Carlo itself is a very useful tool to estimate and illustrate risk for that reason, it is a popular method to use in the financial world. In the real world, though, we simulate a million times using computers before drawing the final conclusion. Why did Relativity Media’s Monte Carlo model not work? Is there no way to use algorithms to make better decisions in the film industry? We discuss these questions and more below.įrom the above example, we can see how using Monte Carlo simulation, rather than just a point estimate, gives much more insight into risk. In reality, it was an epic failure.Īfter producing and releasing films for nearly a decade, on July 30th, 2015, Relativity filed for Chapter 11 bankruptcy following protracted lawsuits and missing loan payments. Kavanaugh drew in billion-dollar investments from the hedge fund Elliott Management and claimed that Relativity could reduce the risk in the uncertain business of filmmaking by using a Monte Carlo model. They said it was the future of Hollywood. In 2004, Ryan Kavanaugh and Lynwood Spinks founded Relativity Media. This is in no way sponsored content and no money changed hands for more, see our comment below. Then it all went horribly wrong.Įditor's note: author Jack Zhang is the CEO of an analytics company, Greenlight Essentials, and the following post represents his opinions on Relativity Media and the overall role analytics platforms play in the film industry. Relativity Media claimed they'd cracked the box office code.
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