Netflix has completely transformed the entertainment sector by offering a wide selection of films and TV shows. By evaluating this data, content producers, marketers, and decision-makers can discover significant insights.
Data related to the content on this platform has been obtained from Kaggle and is analysed in this project.
Neither the ratings or reviews given by the users nor the popularity of the shows have been mentioned so it is difficult to comment on the ideal steps for teams behind the movies or shows to make.
Therefore, each of the possible factors have been assessed with those uncertainities in mind and assuming that Netflix has acquired streaming rights based on how successful that title might be, releases accordingly, and continues to update its strategy based on previous acquisitions.
Interactive Tableau Dashboard
The dashboard has been created with useful visualizations amongst the ones in all the tabs (worksheets or single visualizations).
It has been modified slightly for better readability on this website but it is compatible with Desktop only. Please refer to the above links to view the dashboard or the GitHub link to view its image. Some visualizations's outcome have aleady been discussed in the Jupyter notebook.
Firstly, the type of titles can be selected from the dropdown menu after which a
title from the set of associated titles can be selected from the dropdown menu for the shows.
The genre, a short description, notable cast, and the length of the title is displayed for the selected title.
As suggested in the Jupyter notebook, USA has the most number of titles followed by almost a third of that number in India and then the UK at almost 200 lesser titles.
More than two thirds of the tiles have been observed to be movies while the rest were TV Shows however, in total, there were 4265 movies and 1969 TV shows which is lesser than half the previous value.
TV-MA is the most common rating for movies and TV shows (almost half of that of movies) which means that shows that are meant for mature audience or adults only (17+ ages) are present the most on Netflix. This gives us an idea of the target audience of Netflix and the age group that Netflix' users are comprised of. TV-14 recommended for 14+ ages comes a close second. Rated titles that are least preferred are NC-17 which is slightly similar to TV-MA but narrower in range.
The highest number of addition of titles on Netflix has been in 2019 according to the given dataset with 1546 movies and 803 TV shows.
In case of release years of these titles, the top year for movies has been 2017 with 682 of those and for TV shows it has been 2019 with 400 of those.
The first movie and TV show were added in 2008 and this dataset has only one record of that year for each title types. The oldest TV show is from 1925 and the oldest movie is from 1954.
Finally, without filtering any data here in Tableau, documentaries make up most of Netflix followed by standup comedies and a combination of drams and independent movies. This is slighly varying compared to the code on the Jupyter notebook due to deletion of some records after data processing.
The corresponding visualizations and analysis is present in the Jupyter notebook linked in the beginning. According to the data given and the visualizations made in this Python notebook, 1hr 30min movies that have an international background and a that of action and comedy are prominent on Netflix. Releasing content in the US, India, and the UK that too in off-peak times of June, February, or April among others could be kept in mind.
All of the above factors should be kept in mind when negotiating a price with Netflix to gain the most profit. Success of the cast and directors could also be examined to quote a fair price however, not enough data has been given to infer this so it has been avoided in this project.