Introduction
It is critical for every business to obtain success in its sphere. In the framework of cinematography, box office revenues determine the success of a movie and organizational performance in total. Trying to gain more, Hollywood studios conduct research studies to find out what their audience is willing to see. In order to streamline and enhance this process, Josh Lynn of Piedmont Media Research developed an algorithm that is likely to assist the professionals greatly.
Description of Piedmont Media’s Algorithm
Piedmont Media’s algorithm is targeted at film studios that produce numerous movies, such as those found in Hollywood. Its audience does not include the representatives of the general public because it focuses on financial predictions, in which people are mainly not interested.
This algorithm provides an opportunity to predict the success of a film before it is screened. As a result, it gives a chance to develop appropriate promotion and advertising strategies that can attract more cinemagoers. Using a Piedmont Media’s algorithm, professionals can measure the “Consumer Engagement” score, which also allows them to define approximate box office revenue. The algorithm allows us to find out an element that worsens the film’s financial performance and generate resolutions to minimalize its effect.
An overall “Consumer Engagement” score consists of two scores: the first score is obtained when the movie is run on its own and the second one when it is run along with the names of actors. The algorithm focuses on demographics to define people’s interests and see their feeling about what a movie is. Thus, professionals can define how to run a film to increase box office revenue.
Analysis & Critique
Piedmont Media’s algorithm does not really provide accurate predictions. “Consumer Engagement” score on which it is concentrated goes from 1 to 1,000 but rarely exceeds 500, which proves that the current scale is not adapted yet. In addition to that Piedmont’s algorithm does not provide clear predictions. For example, in “Podcast: The guy who predicts whether a movie will bomb, months before it’s made” it is stated that “anything scoring a 400 should open to about $90 million to $100 million and that anything scoring about a 300 will make $50 million to $60 million” (Avirgan, 2015, para 1).
Still, the difference of $10 million is rather substantial and should not be ignored as well. In this framework Smith and Mahmood (2015) in “Big data, big problems?” underline that large amounts of information need to be initially gathered to obtain detailed objective results. However, in “Netflix’s secret special algorithm is a human”, Wu (2015) also states that the majority of these data can be ignored because only a few critical insights are enough for judgment. So even being not very accurate, Piedmont Media’s algorithm can provide enough information to define the most advantageous way to run a movie.
The algorithm benefits its users allowing them to save the time needed for research and define the source of its success. Defining people’s interests without special tool requires much time and numerous surveys that should be repeated as a new movie comes out. Still, it is stated in “The Tyranny of Algorithms: A Future tense event recap” that algorithms can gather information about people’s interests using social media with minimal people’s interference (Bosch, 2015). In addition to that, according to “In defense of algorithms” even a poor developed algorithmic guidance is better than its absence when it comes to critical decision-making (Villasenor, 2015). The scale of scores in Piedmont Media’s algorithm should be fixed because the error deals with millions of dollars, but even in this way, it allows us to get an insight into people’s expectations and feelings.
Conclusion
Thus, Piedmont Media’s algorithm should gather more information and improve its scale for the “Consumer Engagement” score to provide more accurate results and prevent unexpected losses. Still, even in a current condition, it should be used by film studios to determine approximate revenue and find out whether the emphasis on the cast can attract more cinemagoers or it is better to focus on the very movie.
References
Avirgan, J. (2015). Podcast: The guy who predicts whether a movie will bomb, months before it’s made. Web.
Bosch, T. (2015). The Tyranny of algorithms: A Future tense event recap.Web.
Smith, C., & Mahmood, M. (2013).Big data, big problems? Web.
Villasenor, J. (2015). In defense of algorithms. Web.
Wu, T. (2015). Netflix’s secret special algorithm is a human. Web.