Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

Analysis of ‘Netflix subscribers and revenue by country’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pariaagharabi/netflix2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

I prepare this dataset for one of my courses to show how Netflix’s subscription figures and Netflix's revenue($) have grown in four different regions: - the United States and Canada, - Europe, the Middle East, and Africa, - Latin America, - Asia-Pacific over the last 2.5 years. According to the final month of the quarter 2020(March) was being the start of the global coronavirus pandemic in many countries, Netflix noted that it added 26 million paid new subscribers in the first two quarters of 2020 alone; in 2019, the company added 28 million subscribers in total.

Dataset Description: This dataset contains four CSV files. 1. DataNetflixRevenue2020_V2.csv: three columns Area, Years, Revenue.

1. DataNetflixSubscriber2020_V2.csv: three columns Area, Years, Subscribers.

2. NetflixSubscribersbyCountryfrom2018toQ2_2020.csv: eleven columns Area, Q1 - 2018, Q2 - 2018, Q3 - 2018, Q4 - 2018, Q1 - 2019, Q2 - 2019, Q3 - 2019, Q4 - 2019, Q1 - 2020, Q2 - 2020

3. Netflix'sRevenue2018toQ2_2020.csv: eleven columns Area, Q1 - 2018, Q2 - 2018, Q3 - 2018, Q4 - 2018, Q1 - 2019, Q2 - 2019, Q3 - 2019, Q4 - 2019, Q1 - 2020, Q2 - 2020

--- Original source retains full ownership of the source dataset ---

Here is the full breakdown of Netflix subscribers by region.

Here is the full breakdown of Netflix global subscribers by year since 2013.

In this post, you'll see how the Netflix platform is evolving, how many users Netflix has and how they perform against the growing competition.

TV Shows and Movies listed on Netflix

This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine.

In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset.

Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.

Inspiration

Some of the interesting questions (tasks) which can be performed on this dataset -

1. Understanding what content is available in different countries
2. Identifying similar content by matching text-based features
3. Network analysis of Actors / Directors and find interesting insights
4. Is Netflix has increasingly focusing on TV rather than movies in recent years.

Netflix has been met with tons of competition from major multinational companies. These are the key Netflix Statistics you need to know.

Context

Netflix held the Netflix Prize open competition for the best algorithm to predict user ratings for films. The grand prize was $1,000,000 and was won by BellKor's Pragmatic Chaos team. This is the dataset that was used in that competition.

Content

This comes directly from the README:

TRAINING DATASET FILE DESCRIPTION

The file "training_set.tar" is a tar of a directory containing 17770 files, one per movie. The first line of each file contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format:

CustomerID,Rating,Date

• MovieIDs range from 1 to 17770 sequentially.
• CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users.
• Ratings are on a five star (integral) scale from 1 to 5.
• Dates have the format YYYY-MM-DD.

MOVIES FILE DESCRIPTION

Movie information in "movie_titles.txt" is in the following format:

MovieID,YearOfRelease,Title

• MovieID do not correspond to actual Netflix movie ids or IMDB movie ids.
• YearOfRelease can range from 1890 to 2005 and may correspond to the release of corresponding DVD, not necessarily its theaterical release.
• Title is the Netflix movie title and may not correspond to titles used on other sites. Titles are in English.

QUALIFYING AND PREDICTION DATASET FILE DESCRIPTION

The qualifying dataset for the Netflix Prize is contained in the text file "qualifying.txt". It consists of lines indicating a movie id, followed by a colon, and then customer ids and rating dates, one per line for that movie id. The movie and customer ids are contained in the training set. Of course the ratings are withheld. There are no empty lines in the file.

MovieID1:

CustomerID11,Date11

CustomerID12,Date12

...

MovieID2:

CustomerID21,Date21

CustomerID22,Date22

For the Netflix Prize, your program must predict the all ratings the customers gave the movies in the qualifying dataset based on the information in the training dataset.

The format of your submitted prediction file follows the movie and customer id, date order of the qualifying dataset. However, your predicted rating takes the place of the corresponding customer id (and date), one per line.

For example, if the qualifying dataset looked like:

111:

3245,2005-12-19

5666,2005-12-23

6789,2005-03-14

225:

1234,2005-05-26

3456,2005-11-07

then a prediction file should look something like:

111:

3.0

3.4

4.0

225:

1.0

2.0

which predicts that customer 3245 would have rated movie 111 3.0 stars on the 19th of Decemeber, 2005, that customer 5666 would have rated it slightly higher at 3.4 stars on the 23rd of Decemeber, 2005, etc.

You must make predictions for all customers for all movies in the qualifying dataset.

THE PROBE DATASET FILE DESCRIPTION

To allow you to test your system before you submit a prediction set based on the qualifying dataset, we have provided a probe dataset in the file "probe.txt". This text file contains lines indicating a movie id, followed by a colon, and then customer ids, one per line for that movie id.

MovieID1:

CustomerID11

CustomerID12

...

MovieID2:

CustomerID21

CustomerID22

Like the qualifying dataset, the movie and customer id pairs are contained in the training set. However, unlike the qualifying dataset, the ratings (and dates) for each pair are contained in the training dataset.

If you wish, you may calculate the RMSE of your predictions against those ratings and compare your RMSE against the Cinematch RMSE on the same data. See http://www.netflixprize.com/faq#probe for that value.

Acknowledgements

The training data came in 17,000+ files. In the interest of keeping files together and file sizes as low as possible, I combined them into four text files: combined_data_(1,2,3,4).txt

The contest was originally hosted at http://netflixprize.com/index.html

The dataset was downloaded from https://archive.org/download/nf_prize_dataset.tar

Inspiration

This is a fun dataset to work with. You can read about the winning algorithm by BellKor's Pragmatic Chaos here

Analysis of ‘Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/netflix-showse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Netflix Suggestion Engine

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Source

Access to the study can be found at The Concept Center

This dataset was created by Chase Willden and contains around 1000 samples along with User Rating Score, Rating Description, technical information and other features such as: - Release Year - Title - and more.

How to use this dataset

• Analyze User Rating Size in relation to Rating
• Study the influence of Rating Level on User Rating Score
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

In the fourth quarter of 2024, Netflix generated total revenue of over 10.2 billion U.S. dollars, up from about 8.8 billion dollars in the corresponding quarter of 2023. The company's annual revenue in 2024 amounted to around 39 billion U.S. dollars, continuing the impressive year-on-year growth Netflix has enjoyed over the last decade. Netflix’s global position Netflix’s revenue has been heavily impacted by its ever-growing global subscriber base. The leading Netflix market is Europe, Middle East, and Africa, surpassing the U.S. and Canada in terms of subscriber count. Netflix has also significantly increased its licensed and produced content assets since 2016. Despite concerns among investors that the company’s content spend was negatively affecting cash flow, Netflix’s plans to amortize its content assets long-term along with generating revenue from other sources such as licensing and merchandise should ensure the company’s future profitability. Netflix’s original content Netflix is also fortunate in that many of its original shows have been a hit with consumers across the globe. Shows such as “Orange is the New Black,” “Black Mirror,” and “House of Cards” won the hearts of subscribers long ago, but newer content such as English-language shows “Bridgerton,” “Wednesday,” and “Stranger Things,” as well as local TV shows such as “Squid Game” have also been favorably reviewed and proved popular among users.

Overview

Streaming Services Statistics: Streaming services have transformed the entertainment landscape, revolutionizing how people consume content.

The advent of high-speed internet and the proliferation of smart devices have fueled the growth of these platforms, offering a wide array of movies, TV shows, music, and more, at the viewers' convenience.

This introduction provides an overview of key statistics that shed light on the impact, trends, and challenges within the streaming industry.

Netflix produced more than 2,769 hours of original content in 2019. This was a huge 80.15% increase compared to 2018. Netflix had over 2,000 originals at the beginning of 2021.

Company Description

Netflix, Inc. operates as a streaming entertainment service company. The firm provides subscription service streaming movies and television episodes over the Internet and sending DVDs by mail. It operates through the following segments: Domestic Streaming, International Streaming and Domestic DVD. The Domestic Streaming segment derives revenues from monthly membership fees for services consisting of streaming content to its members in the United States. The International Streaming segment includes fees from members outside the United States. The Domestic DVD segment covers revenues from services consisting of DVD-by-mail. The company was founded by Marc Randolph and Wilmot Reed Hastings Jr. on August 29, 1997 and is headquartered in Los Gatos, CA.

Shareholders

Mutual fund holders 49.41% Individual stakeholders 4.17% Other institutional 31.86%

Contact Information

Netflix, Inc. 100 Winchester Circle Los Gatos California 95032

P: (408) 540-3700 Investor Relations: (408) 809-5360 www.netflix.com

Source and Inspiration

The data is collected from Yahoo Finance. Inspiration is the release of the fifth season of my favorite Netflix show Money Heist (La Casa de Papel)

Industry data revealed that Slovakia had the most extensive Netflix media library worldwide as of July 2024, with over 8,500 titles available on the platform. Interestingly, the top 10 ranking was spearheaded by European countries. Where do you get the most bang for your Netflix buck? In February 2024, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 21.19 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 6,500 and 6,900 titles. On the other end of the spectrum, Pakistan, Egypt, and Nigeria are some of the countries with the cheapest Netflix subscription costs at around 2.90 to 4.65 U.S. dollars per month. Popular content on Netflix While viewing preferences can differ across countries and regions, some titles have proven particularly popular with international audiences. As of mid-2024, "Red Notice" and "Don't Look Up" were the most popular English-language movies on Netflix, with over 230 million views in its first 91 days available on the platform. Meanwhile, "Troll" ranks first among the top non-English language Netflix movies of all time. The monster film has amassed 103 million views on Netflix, making it the most successful Norwegian-language film on the platform to date.

Netflix - TV Shows and Movies

This data set was created to list all shows available on Netflix streaming, and analyze the data to find interesting facts. This data was acquired in July 2022 containing data available in the United States.

Content

This dataset has two files containing the titles (titles.csv) and the cast (credits.csv) for the title.

This dataset contains +5k unique titles on Netflix with 15 columns containing their information, including:

• id: The title ID on JustWatch.
• title: The name of the title.
• show_type: TV show or movie.
• description: A brief description.
• release_year: The release year.
• age_certification: The age certification.
• runtime: The length of the episode (SHOW) or movie.
• genres: A list of genres.
• production_countries: A list of countries that produced the title.
• seasons: Number of seasons if it's a SHOW.
• imdb_id: The title ID on IMDB.
• imdb_score: Score on IMDB.
• imdb_votes: Votes on IMDB.
• tmdb_popularity: Popularity on TMDB.
• tmdb_score: Score on TMDB.

And over +77k credits of actors and directors on Netflix titles with 5 columns containing their information, including:

• person_ID: The person ID on JustWatch.
• id: The title ID on JustWatch.
• name: The actor or director's name.
• character_name: The character name.
• role: ACTOR or DIRECTOR.

Tasks

• Developing a content-based recommender system using the genres and/or descriptions.
• Identifying the main content available on the streaming.
• Network analysis on the cast of the titles.
• Exploratory data analysis to find interesting insights.

Other Streaming Datasets

• HBO Max TV Shows and Movies
• Amazon Prime TV Shows and Movies
• Disney+ TV Shows and Movies
• Hulu TV Shows and Movies
• Paramount TV Shows and Movies
• Rakuten Viki TV Dramas and Movies
• Crunchyroll Animes and Movies
• Dark Matter TV Shows and Movies

How to obtain the data

If you want to see how I obtained these data, please check my GitHub repository.

Acknowledgements

All data were collected from JustWatch.

Analysis of ‘1000 Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/chasewillden/netflix-shows on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

--- Original source retains full ownership of the source dataset ---

Here is the breakdown of Netflix’s revenue earnings year over year from 2011.

Context Dataset contains the list and metadata of all TV Shows and Movies available on Netflix currently about 7000 taken from the IMDB website. Upvote if you liked it.

Content netflix_list.csv

imdb_id : Unique show identifier. title : Title of the show. popular_rank : Ranking as given by IMDB when filtered by popularity. certificate : Contains the age certifications received by the show. Many null values. startYear : When the show was first broadcasted. endYear : Year of show ending episodes : Number of episodes in the show. 1 for movies. type : Movie or Series orign_country : Country of origin of the show language : Language of the show. plot : Synopsis of the show. summary : Summary of the story of the show. rating : Average rating given to the show. numVotes : Number of votes received by the show. genres : Genre the show belongs to. isAdult : 1 If adult content present. 0 if not. cast : Main cast of the show in list format. image_url : Link to poster image. Acknowledgements This is collected from IMDB website Data collected by web scrapping through the shows ranking pages with filtered to show Netflix related content(16000+ entries) and noting down the imdb_id, followed by single page search for each collected ID and unique title name.

Original Data Source:Netflix Movie and TV Shows (June 2021)

These are the top 10 countries for Netflix in terms of penetration rate.