The world has transformed in ways previously unimaginable with the emergence of Big Data and Analytics. In a rapidly flourishing and emerging industry similar to the motion picture industry, new avenues came into existence with data analytics that is useful for analyzing data in the past, make marketing decisions creatively, and accurately predicting the fortunes of future movie releases.

The timing of the film release is crucial to the success of a film. To facilitate, date of the movie release studios choose and pre-announce the date of target on a weekly basis long before the original release of their forthcoming films.

Their choices of release dates then the subsequent changes are tactical thinking about numerous aspects of local holidays, sports events, cultural events, political situation and so on. Predictive analytics using the historical films release data and their box office performance can help us identify the ideal release date of the films to maximize performance at the box office.

Imagine a scenario where a movie is set for the release and suddenly a new film is announced by the competitor, and the production team should decide whether to postpone or go with the release.

Changes in the entertainment industry?

One of the essential elements of predictive analysis is the collection and storage of large volumes of data. The films industry has always seen rapid growth. Over the last few years, that growth rate has skyrocketed significantly due to the insurgents of alternative distribution platforms such as online platforms and mobile platforms. The motion picture industry is also inherently rich in data which makes it an exciting realm to work for data analysts and statisticians.

Earlier, the films industry used to utilize the knowledge of specific industry trends, the basic rule of thumb approaches and traditional wisdom and intuition to predict the success or failure of particular films. This method was never very accurate or reliable and has been found wanting in many areas over the years.

In the present day, with the emergence of Big Data and the exciting opportunities that are promised through data mining and analysis, the industry is actively in the process of formulating a new, improved and reliable method of accurately predicting the success and failure of a particular film. Stakeholders in most major film industries of the world are turning towards data scientists and analysts with the intention of increasing their success rate through the help of data analytics. Currently, many major films studios around the world are actively as a prime means to gauge the possibility of success of many of their projects.

First Principles of Data Analytics for the Film Industry

The primary factor that helps decide the possibility of success for a particular film is the knowledge about what makes people interested and heightens their curiosity. This knowledge can be achieved with a degree of accuracy through the analysis of various online sources including video views and comments, search engine results, social media content and ratings on expert websites. Reports of previous success records of other films of the same genre or those casting the same persons can also be brought into the fold to deliver accurate outputs.

The primary goal is to be able to accurately forecast the possible box office returns for a particular film using the relevant kinds of data. In pursuit of this analysis, the analytics experts are expected to have a significant store of valuable information, including the records of films by the same director and Production Company, other films of the same genre, similar casts, types of story and different avenues of marketing and promotion. Apart from these essential factors, there are other influential factors like engagement related to please and trailer launches, social media buzz and public forum comments.

A typical pathway of analyses of this kind can be the following:

• Initial categorization of past, present and future releases with a breakdown by techniques for cluster analysis.
• A similarity check using sample plot points with other films of perceived similarity to ascertain the degree of similarity.
• Using the model derived from the above steps and past data collections to arrive at an approximate estimation of the net return of the particular films in question.
• Building an accurate and reliable statistical model of the above factors, as well as other intrinsic factors like awareness, interest, and curiosity.

Getting Amplified Results

While the above processes only a rough outline, there are many ways data scientists can employ to get much more accurate, fine-tuned results. With the amount of data readily available and the number of sophisticated tools, techniques and data processing platforms that are abundant today, astonishing levels of accuracy can be achieved with predictions. The first step towards making this is to ensure that the right audience gets targeted.

To achieve this, individual filmgoers can be treated as potential customers and then careful data research should be undertaken to ascertain which of these potential customers are most likely to influence the opinion of others. In this regard, it can be beneficial to factor in both prospective audience members and also theater owners, who might have their strategies regarding the scheduling of particular films to expand their occupancy and profits. Factors like demographics also play a significant role in this regard.

Conclusion

The long-term gain from this approach is that films studios have the outside chance of avoiding major box office catastrophes. The process of making films and then starting with the analytics will gradually give w

ay to letting the analytics dictate the course of the movies and provide creative direction to maximize reach and revenue in the long-term. With all the power of the Big Data, that magic formula is not very far out of reach for motion picture studios.