A Research On Frequency Of Google Searches For Ten Pairs Of Films Essay

How has the correlation between the overall financial success of a film and the frequency of Google searches within a week of the film’s release changed from 2004 to 2009?

It is no secret that the film industry has amassed a dominating role in modern society. With the year 2015 seeing over 1.3 billion cinema ticket sales, the importance and prevalence of the silver screen is undeniable. Equally vital to the twenty-first century consumer are online search engines, specifically Google which handles more than 40,000 searches per second. In today’s technological world, most questions or topics of interest are typed into Google, such as those questioning the showtimes of a movie, the actors in it, or a brief synopsis. These searches can imply interest in a film and therefore predict its success. Growing up in this electronic world has allowed me to develop an interest in both films and Google and specifically how the quantity of Google searches can indicate the success or failure of an upcoming film.

In order to go about finding the correlation, I initially decided upon specific parameters. One of these parameters was to focus on the years 2004-2009. I chose a five year period to receive substantial data without receiving too much. My reasoning for these specific years was personal: I remember movies released during that time period and many of the movies used in my analyses are ones I personally enjoy.

For my investigation, I analyzed ten different pairs of films: 2 pairs for each year. Each pair contained similarities in certain fields that could impact financial success. These fields were kept the same so there would be a greater possibility of Google searches having some relevancy. For example, if a multimillion dollar film was released with an all-star cast during peak summer movie times, it is more likely to be financially successful than a film with a low budget and unknown cast released during winter. To keep these factors constant, each pair of films was released on the same day, are from the same genre, had productions budgets in the same range , and had a similar amount of name brand recognition cast. While there is no specific way to measure famousness of a cast, I scrutinized the stars in the film and compared them to the STARmeter feature on the Internet Movie Database (IMDB); if they were ranked in the same categories, they were considered to have equal name recognition. In total, I calculated 10 analyses, two for each year studied and for the analyses from the same year both occurred at different times through the year. (For example, the analyses from 2004 included two films from early April and two films from early September: different times when it comes to the scope of moviegoing.) One genre I avoided was documentary as documentary production budget and factors of success is not comparable with other films.

Once I had decided upon the pairs, I compared their overall box office income upon release. The films that did better financially were placed first and are marked as Film 1 in both Table 1 and Graph 1. Once the financial success portion was determined, I moved on to determine the prominence in Google searches using a tool known as Google Trends.

Google Trends is a tool released from Google itself which tracks how popular a certain word or phrase was in relation to its most popular point. This “popularity” refers to the amount of times the term was searched over a week. For example, if the maximum amount of searches a certain topic received in a week was 60, a week in which that search received 30 searches would be depicted as 50 since 30 is 50 percent of the maximum, 60. These points marking popularity are placed along a graph for easier comparison as can be seen in Figure 1.

One pair that I used included the films Mean Girls (2004) and Envy (2004). Both of these films are comedies released on April 30, 2004 with production budgets within the same range. Additionally, both Mean Girls and Envy featured star studded casts including actors such as Tina Fey and Lindsay Lohan, and Ben Stiller and Jack Black. Despite the previously outlined similarities, Mean Girls earned over $86 million at a domestic box office while Envy only reached $12.1 million.

After locating this $73.9 million difference, I analyzed the graph present through Google Trends, as is shown in Figure 1. To create this graph, I specified both “Mean Girls” and “Envy” referred to movies and focused specifically on the year of 2004 surrounding the release of the movie. The dots on both lines of the graph refer to the time in which the films were released.

Figure 1

According to the information from Google Trends, the percent increase for Mean Girls (80 to 100) yields a 20% increase. Envy, on the other hand, decreased from 23 percent to 18, a percent change of -5. When these differences were recorded in Table 1, I calculated the absolute value of the differences. I used the absolute values of change for the two movies to most accurately answer my initial question. My main inquiry details the way annual progression affects the correlation between Google searches and a movie’s financial success. This effect can be both negative and positive, explaining the reason an addition of absolute value was necessary so the overall change, whether positive or negative, could be calculated.

Table 1 shows the results received for the 10 pairs analyzed:

Table 1

The prerelease and postrelease sections correlate to the order of the films in pairs. The films highlighted in blue refer to the film written first under the “films” category while the films in red refer to the second. The films whose totals and differences are highlighted in blue had a higher gross box office earning: the amount of money the film made.

In order to compare the data in a visual way, I placed the numerical data on a graph:

Of the 10 pairs analyzed, 7 implied that films which received a higher change in the amount of Google searches also yielded higher box office success. For each of the 3 films in which the opposite was true, the other pair from the same year yielded results showing more Google searches correlated to higher gross box office success. Therefore, I concluded that films with the greater amount of change in searches between the weeks prior to and following the films release will likely yield greater overall box office success.

Given the numerical data and graph, I determined that Google searches have a high correlation to box office success in regards to films. These films, which spanned a broad range of ratings, genres, and budgets, implies that searches have a high correlation to success.

When considering my experiment, there were some variables which could have been better controlled. While I did attempt to keep many factors the same, I did not do so in terms of film ratings. Logically, films rated R are at a disadvantage in comparison to films rated G, as the amount of people who could watch them is lesser.

Additionally, the IMDB source I used to ascertain if a cast has high brand name recognition was pertinent as of November 15, 2016 instead of the date when the films came out. Some films made their stars famous and therefore higher on the list. Also, some actors may not have ranked in the “Top 500” section when their film was released, but would do so now. Therefore I cannot accurately rank the “famousness” of the cast initially when their film was released.

How to cite this essay: