Movies and films are the primary source of entertainment for audiences across the world, so much so that they are almost synonymous with the word entertainment. This claim is only helped by the fact that the valuation of the global film industry stood at $38 billion as of 2016, and is expected to grow significantly in the coming years. The lucrative opportunities persistent in the film industry are urging production houses, both large and small, to produce and launch multiple movies within a short period of time. Consequently, numerous movie titles are released in a year, and people only recall few titles that are produced by reputed production houses. Another problem faced by the film industry is estimating the ROI, or in simple terms, whether the movie will be a success in the box office. Production houses allocate millions of dollars of the budget into the production of the films. As a result, it is essential for them to predict the success of the movie.
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Data Analytics Evolution in the Film Industry
Historically, movies have always been about arts; the only data relating to a film would come from box office numbers and ticket sales. It was impossible to predict the success of the movie beforehand, and producers would usually have to rely on their judgment. However, that’s no more the case today as alternative distribution platforms have given players in the film industry a source to collect data. They have replaced traditional approaches to predicting success through data mining and data analytics. It is not surprising that the film industry is turning toward big data to increase the success rate of their movies.
Data Points to Look Out For
It is not uncommon for certain movie trailers to go viral and a specific movie to be hyped about a lot, regardless of if it’s actually good. Today, such interests and curiosity can be accurately measured from different online sources including search engine results, social media feedback, video views and comments, and ratings on the critic’s website. Data analysts can look at the past success of a similar genre of the movie with similar casts to improve the accuracy of prediction. To accurately forecast a movie’s revenue potential, data scientists need to have a large repository of data including the success of titles by the same director, production company, or casts, movies of the same genre, type of story, and marketing and promotional channels used.
Achieving Accurate Results
To get more precise, fine-tuned results, data scientists should consider the data points for analysis. They have to create prediction models and algorithms to judge the success of the movie accurately. With an abundance of data available to analyze, the algorithms should consider the right target audience to achieve the desired level of accuracy. The right target audience can be pinpointed by analyzing moviegoers on an individual level. It is important to ascertain which of these potential customers are the most likely to influence the opinion of others. Additionally, seasonality also plays a vital role in determining the success of the movie. It is the major reason why similar movies generate varying revenues as it launched for events like major festivals, holidays, or weekends. Competitiveness is another factor that influences the earning potential of a movie, for instance, an average moviegoer would only watch one movie a week, so a particular title would have to compete with various others released in the same week.
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Predictive Analytics in the Future
The advancements in data sciences and constant improvement in the movie success algorithms would redefine the way the film industry approaches movie production. In the future, it would be unlikely to see flops denting profits in the quarterly sheets of production houses. With a wide range of data available for decision-makers in the film industry, the data analytics and prediction models will only get better. As a result, we will see the film industry releasing the movie more systematically with data analytics gaining as much prominence as the director and casts of the movie.
