Conclusions and Demonstation Videos

Learning - Through this project, we learned a number of key lessons.

1. The Importance of Presentation - Data means nothing if it cannot be presented in a clear and understandable manner. We had to select the type of charts we used to present each insight in order to deliver the maximum impact. For example, Visualization 5, which showed the relationship between Disney movie income and US GDP performance, called for a different type of chart from Visualization 4, which needed to show the proportion of ratings within the different genres. Using a Pie chart for Visualization 5 would have made no sense, while using a Line graph for Visualization 5 would likewise have failed to show any relationship in the data.

2. Dealing with Complex and Unfamiliar Syntax - While trying to present our data in novel ways, we discovered that we often did not know the exact names and arguments for each type of function. We had to learn the syntax of these Plotly visualizations by reading the documentation online. We quickly realized that it was impossible to memorize every single function in every library that was available for data visualization. A far more productive method was to hone our research skills so that we could read and learn new syntax as quickly as possible.


Conclusions - In our project, we analyzed Disney movies’ income by relating them to their genre, rating, and the overall performance of the US economy. In doing so, we drew many interesting insights.

Some were relatively predictable: Movies with more family-friendly ratings like G and PG tended to bring in the most income because they were suitable for a wider audience. The income of Disney movies also generally followed the positive trend of US GDP growth from 1960 to 2016. From this, we can infer that the general increase in prosperity within the wider United States contributed heavily to Disney’s success over the years. As the economy expanded, the disposable income that families could spend on entertainment such as movie-going increased, and this boosted Disney’s movie revenue.

Other insights, however, were more unexpected: For example, the genre with the highest average income turned out to be Musicals rather than Adventure or Action movies. This could be due to the relatively smaller sample size of Musicals (16 in total), compared with 129 Adventure movies. Nevertheless, the data still suggests that Musicals have the highest probability of being a financial hit for Disney compared to other genres.

Ultimately, however, It was clear through our analysis that it is hard to pinpoint any particular coherent strategy or secret formula that led to Disney’s success: For instance, even though movies released during peak months like May, June, November, and December earned the most income, Disney did not release more movies during these periods. They could still have capitalized on the spike in movie-going interest during the Summer and Winter holiday seasons by saving their most anticipated blockbusters and franchises for these periods. However, more in-depth and qualitative research would be required to determine the truth of this hypothesis, which is beyond the scope of our project.

In sum, while our investigation led us to discover interesting trends in the income of Disney movies based on their genre, rating, and the performance of the wider US economy, it is clear that the recipe behind Disney’s movie magic, which has captured the imagination of millions, cannot simply be quantified by numbers.