“Nobody knows anything… Not one person in the entire motion picture field knows for a certainty what's going to work [at the box office].”
- William Goldman, a two-time Oscar-winning screenwriter
Gone are the old days when movies were solely concept-driven. The only moment when data came into the limelight in this industry was in the case of noting the box office digits and ticket sales. Whether the movie would be a success or a failure was a mystery, it was futile to guess its outcome in advance and the producers were made to depend solely on their own reasoning. Yet in the present scenario, this is no longer the case with alternative distribution platforms having handed the film industry participants a source for gathering data.
The conventional path adopted for forecasting success has been replaced with data analytics. The film industry has now steered towards data science for enhancing the outcome of their films.
We’ve often observed cases where many movies or film trailers tend to go viral and have a great degree of buzz surrounding them and this buzz may or may not imply that the movie is actually worth the hype. In the present world, this hype can be deciphered through varying online sources which include the views and comments on the film-related videos, search engine outcomes, the feedback on social media, as well as the ratings for the movie on the websites of critics.
Data analysts can observe the prior success of related genres of the film with similar casts with the objective of enhancing the precision of the prediction. For effectively predicting the revenue scope of any movie, data scientists are required to keep a massive repository of data which includes the success of movies by same directors, production firms as well as casts, same genre films, similar kind of storylines and the type of promotional channel adopted.
Originally complex demographics created on the basis of factors like age and gender were used for determining groups of target audience. This is no longer the case with an ocean of data being generated through the comments, likes and shares on social media platforms which have made it probable for Hollywood to get a more advanced understanding of its audience.
This implies that the contemporary film studios can specifically reach out with their content to the suitable audience based on the chances that that group of the audience will be engaged in that content, alongside also bringing back value to the studio by being interested in that content.
The capacity to interpret big data patterns like the viewing behavior as well as the user feedback cycles has been revolutionizing the manner in which firms focus on creating content. Exceptional insight into the preferences of the audience is brought through social media which plays a hand in making the data science possibilities of predicting the character competency, plot lines, and actors on viewing behavior, endless.
In order to maintain their present business models, the movie industries need to ensure that their audiences keep revisiting the theatres. For this, determining what propels higher audience engagement becomes crucial.
There are many considerable factors that influence the interest of the audience in a movie. These range from the movie ticket’s price, the types of movies being provided, the number of varying movies being provided, the remake or sequel ratio, the competency of marketing strategies, the ratio of international blockbusters to domestic, the age appropriateness for movies to the size, location and technology used in the theatres.
Factors influencing Audience engagement
Each of these factors can be impacted or swayed to a considerable extent by Big Data.
Although in the present days, owing to the leveraging of data science in the industry still being at the initial stage, data science’s power in this sector is still pretty subdued.
In the current scenario, the best areas in which data science can cogently boost audience engagement is by enhancing the quality of the films being provided, the usefulness of the marketing strategies as well as the number of varying types of films being provided.
At the end of the day, the audience is invested in the world of movies with the sole intention of entertainment and enjoyment, owing to which the quality of the movie they watch is foremost for them. This is one area where data science can step in.
You can check out our blog on the role of IoT in movies
Adopting an approach towards movie production which is propelled by data from the initial stage itself ensures that only promising scripts with a maximum potential are finalized. This in turn will assist in predicting the future response to related movies or sequels.
It will also essay a role in enhancing the marketing strategy and the kinds of films being provided through data-driven film-making. The enormous collection of data from sources like social media platforms will enable a more targeted form of marketing.
Targeted marketing would ensure that the type of audience who would prefer and enjoy the movie would go to view it as well, which in turn would raise the engagement of the audience. By gathering more precise data regarding what their audiences prefer to view, movie theatres can provide an additional variety of movies. The very data pool facilitating the precise target marketing would be able to inform theatre owners on the type, plot, and genre which their audience demographic would prefer watching.
The accuracy of the data analysis will enable the theatres to sketch out the movies which they display based on the possible number of audiences for each of them.
An excellent example of how data science has played a hand in revolutionizing streaming platforms would be Netflix. What used to be a mere mail-order DVD service back in the late 1990s is now a leading streaming platform whose name stays in the minds of a majority of the content devouring audience.
The platform's turning point took place in 2006, when it set up the Netflix prize contest, offering around a million dollars to the group that could offer the most suitable algorithm for adopting prior ratings to forecast film ratings in the future by using merely four data points namely the ID of the customer, the movie ID, the date the film was watched as well as the rating of the film. This laid the seed for the platform's leading and renowned recommendation engine.
Presently various of the leading shows on Netflix for instance House of Cards were set up owing to the intricate predictions generated through the massive number of data points gained from the platform’s users. Through the interest of the audience towards genres and cast and previous works relating to the concept of the show, the platform’s executives were able to predict the success of House of Cards before the show even started.
Yet another intriguing example of the adoption of data science in the world of the film industry is that of IMDb, i.e the Internet Movie Database. Being equipped with a massive database of over a million films and an enormous number of users, the platform allows anyone to furnish fresh content, edit the prevailing entries, as well as facilitates them with the right of rating any movie on the scare of 1 to 10. This data can be theorized through items like tables, graphs, or charts from the expansive categories, such as the preferred genre, actor, storyline, or director, to the highly specific ones, such as the most underrated films in a particular area which are age-restricted.
This plays a hand in contributing towards the complex process of aiding studios in measuring how favorable an outcome a movie will have in advance, getting an idea on whether a particular concept will be leading the box office charts or crash landing towards the ground, helping the studios in averting any flop cases.
In the past couple of decades, data on enormous collections of movies and TV series have been gathered and accumulated by researchers. In a number of categories interrelationships have been discovered, from the kinds of characters, the power held by the stars, the budget, the buzz over a certain film to the intricacies of the plot.
The buzz allows all the populace to remain updated over any development regarding the movie from sources like social media or through reviews. Yet this buzz is definitely not all that the data analysis in the industry is all about. It is pivotal that data analytics be adopted at each stage involved in the creation of the film, from its generation to post-production and its screening.
Predictive analytics can aid producers, production firms, and executives in determining accurate decision-making, predicting trends, and in comprehending the preferences of the viewers in an advanced manner.
The present scenario has established that films are no longer being produced in the conventional manner they used to be. A great degree of rationale and scrutiny is invested behind determining the type of movie to be created, who should create it as well as how long it is supposed to be.
The data science progress, as well as the regular boost in the algorithms for the movie outcome, have recreated the pathway adopted by the film industry for producing movies. Seeing the massive degree of data available at the behest of the film industry players, the power of data analytics in the sector will only rise, owing to which the future of the film industry will definitely observe a more systematic approach towards the industry’s operations with the employment of data analytics at every step.
6 Major Branches of Artificial Intelligence (AI)READ MORE
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
Top 10 Big Data TechnologiesREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
Deep Learning - Overview, Practical Examples, Popular AlgorithmsREAD MORE
7 Types of Activation Functions in Neural NetworkREAD MORE
What Are Recommendation Systems in Machine Learning?READ MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
Introduction to Time Series Analysis in Machine learningREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE