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.

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

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.

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.

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

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.

Here is the full breakdown of Netflix subscribers by region.

Netflix Prize consists of about 100,000,000 ratings for 17,770 movies given by 480,189 users. Each rating in the training dataset consists of four entries: user, movie, date of grade, grade. Users and movies are represented with integer IDs, while ratings range from 1 to 5.

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

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?

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

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 ---

The average Netflix user spends 3.2 hours per day streaming content on Netflix.

The dataset comprises the 20 countries with the greatest increases in the volume of average daily VPN searches in March and April 2020. For each country, the trends in search volume for VPN terms subsequent to the initial spike were also recorded every seven days aftewards.

The company reported that its users are 49% women and 51% men.

A dataset was compiled to elicit the reactions of a group of deaf people to their experience of watching a colloquial Egyptian movie subtitled intralingually into Modern Standard Arabic (MSA). The researchers designed a survey that includes two sections. The first section contains demographic information, including gender, education level, age, and MSA reading speed. The second section comprises five main constructs: (1) movie watching habits, (2) technical aspects, (3) linguistic and paralinguistic information, (4) attitude, and (5) future actions and recommendations. The researchers contacted two centers for the deaf in Egypt and Egypt to provide them with the contact details of some of their members who are willing to take part in the study. They were sent a Google Drive link that includes the Egyptian movie Boushkash with Netflix MSA subtitles. The members were then asked to fill in a Likert-type questionnaire that is designed using Microsoft Forms. Snowball sampling method was also used as the researchers asked the respondents to share the links with their deaf fellows. Responses from 126 participants were received. The responses of 20 participants were used for piloting, i.e., to examine the questionnaire's reliability, while the remaining 106 respondents were used in the dataset of the present study. The percentage of respondents who either 'Strongly Agreed' or 'Agreed' with the item was calculated. In addition, the standard error of the mean "M (S.E.)" for each item was also calculated. The data will be useful for translators and subtitlers interested in examining the impact of intralingual subtitles on the audience with hearing impairment. Policymakers and governments can use the data to identify the needs of this minority group and take action to force national T.V. channels to add intralingual translation to their various programs.

BackgroundMental health conditions and psychiatric disorders are among the leading causes of illness, disability, and death among young people around the globe. In the United States, teen suicide has increased by about 30% in the last decade. Raising awareness of warning signs and promoting access to mental health resources can help reduce suicide rates for at-risk youth. However, death by suicide remains a taboo topic for public discourse and societal intervention. An unconventional approach to address taboo topics in society is the use of popular media.MethodWe conducted a quantitative content analysis of mainstream news reporting on the controversial Netflix series 13 Reasons Why Season 1. Using a combination of top-down and bottom-up search strategies, our final sample consisted of 97 articles published between March 31 and May 31, 2017, from 16 media outlets in 3,150 sentences. We systematically examined the news framing in these articles in terms of content and valence, the salience of health/social issue related frames, and their compliance with the WHO guidelines.ResultsNearly a third of the content directly addressed issues of our interest: 61.6% was about suicide and 38.4% was about depression, bullying, sexual assault, and other related health/social issues; it was more negative (42.8%) than positive (17.4%). The criticism focused on the risk of suicide contagion, glamorizing teen suicide, and the portrayal of parents and educators as indifferent and incompetent. The praise was about the show raising awareness of real and difficult issues young people struggle with in their everyday life and serving as a conversation starter to spur meaningful discussions. Our evaluation of WHO guideline compliance for reporting on suicide yielded mixed results. Although we found recommended practices across all major categories, they were minimal and could be improved.ConclusionDespite their well intentions and best efforts, the 13 Reasons Why production team missed several critical opportunities to be better prepared and more effective in creating social impact entertainment and fostering difficult dialogs. There is an urgent need to train news reporters about established health communication guidelines and promote best practices in media reporting on sensitive topics such as suicide.

This is a simulated dataset exploring how lifestyle habits affect academic performance in students. With 1,000 synthetic student records and 15+ features including study hours, sleep patterns, social media usage, diet quality, mental health, and final exam scores, it’s perfect for ML projects, regression analysis, clustering, and data viz. Created using realistic patterns for educational practice.

Ever wondered how much Netflix, sleep, or TikTok scrolling affects your grades? 👀 This dataset simulates 1,000 students' daily habits—from study time to mental health—and compares them to final exam scores. It's like spying on your GPA through the lens of lifestyle. Perfect for EDA, ML practice, or just vibing with data while pretending to be productive.

Analysis of ‘FAANG- Complete Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data on 14 February 2022.

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

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

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