Skip to content

Used descriptive and exploratory data analysis to pitch recommendations to stakeholders of a fictional, newly-formed Microsoft movie studio

Notifications You must be signed in to change notification settings

paul-lindquist/microsoft-studio-movie-analysis

 
 

Repository files navigation

banner_image

Movie Analysis for Microsoft Studios

Authors: Paul Lindquist, Steven Addison, Sumedh Bhardwaj, Wahaj Dar

Overview

This project postulates that Microsoft is launching a movie studio. We use exploratory data analysis to pitch recommendations to their stakeholders.

Business Problem

As this is a new venture for Microsoft, we want to be cognizant of cost and profitability. We offer several, data-driven approaches to maximize return.

Questions we'll answer:

  • What genres of movies are lower risk to target as an entry point into the industry?
  • Are there types of movies or specific directors and actors that yield higher revenues?
  • Who are Microsoft's competitors and can anything be modeled from their successes?
    • Does Microsoft already have a catalogue of content that can be turned into movie franchises?
  • When is the most profitable time to release movies?

Data

We use historical and categorical data from the box office and current movie landscape – release date, directors, actors, etc. – to examine correlations.

Focuses:

  • Particular focus given to cost (budget) and gross revenue (worldwide)
  • Drawing a correlation between revenue and genres, directors, actors, release date, etc.
  • Sourced from: Box Office Mojo, The Numbers, IMDB

Methods

This project uses descriptive analysis.

Results

Horror movies yield the lowest average cost and highest average profit margins, offering a strategic, lower-risk entry point into the industry:

graph1

A handful of directors helm movies that historically yield high revenues. It would be worthwhile exploring the availability of these directors to direct a big budget movie:

graph2

The top 2 competing studios with the highest revenues (Buena Vista/Disney, Warner Bros) own blockbuster comic book franchises (Marvel, DC Comics, respectively). A comparable franchise could be fashioned from high-grossing Microsoft games (Halo, Gears of War, Forza Motorsport, etc.):

graph3

Target the release month of big budget movies for May, June or July, as these 3 months historically yield the highest average revenues:

graph4

Conclusions

Based on our analysis, we make the following suggestions for the business:

  • An early focus should be placed on making horror movies. According to the data, they historically yield the lowest average cost and highest average profit margins. They'll serve as an initial, lower-risk entry point.
  • When moving into big budget, blockbuster movies, target specific directors and/or actors who've historically yielded high box office revenues. Top 3 directors: Russo brothers, Joss Whedon, James Wan. Top 3 actors: Robert Downey Jr., Chris Evans, Dwayne 'The Rock' Johnson. Top 3 actresses: Scarlett Johansson, Jennifer Lawrence, Bryce Dallas Howard.
  • Explore creating movie franchises out of Microsoft intellectual property, namely video games. The top 2 competing studios with the highest revenues (Buena Vista/Disney, Warner Bros) own Marvel and DC comics, respectively. A comparable franchise could be fashioned from high-grossing Microsoft games (Halo, Gears of War, Forza Motorsport, etc.)
  • Release big budget movies in May, June or July. These 3 months historically yield the highest average revenues.

For More Information

Please review our full analysis in our Jupyter Notebook or presentation deck.

For additional questions, please contact Paul, Steven, Sumedh or Wahaj.

Repository Structure

├── README.md                           <- The top-level README for reviewers of this project
├── Group_4_Notebook_FINAL.ipynb        <- Narrative documentation of analysis in Jupyter notebook
├── Project_Presentation.pdf            <- PDF version of project presentation
├── data                                <- Both sourced externally and generated from code
└── images                              <- Both sourced externally and generated from code

About

Used descriptive and exploratory data analysis to pitch recommendations to stakeholders of a fictional, newly-formed Microsoft movie studio

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%