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.

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.

This dataset, titled "Netflix Stock Data and Key Affiliated Companies", provides comprehensive insights into the stock performance of Netflix (NFLX) alongside several key companies that have played a significant role in Netflix's growth and operational success. These companies include major technology and media giants such as Amazon (AMZN), Intel (INTC), Warner Bros. Discovery (WBD), Sony (SONY), and others.

Dataset Overview:

The dataset includes daily stock data for Netflix and a selection of companies that contribute to its content distribution, technological infrastructure, cloud services, and content licensing. The selection of affiliated companies highlights the broad ecosystem of services and technologies that power Netflix's streaming service and its original content production.

Key Companies Included:

1. Netflix (NFLX) – The leading global streaming platform that revolutionized the way we consume media.
2. Amazon (AMZN) – Provides critical cloud infrastructure via Amazon Web Services (AWS), enabling Netflix to scale globally.
3. Intel (INTC) – Supplies advanced processors and server technologies that help Netflix handle massive data loads.
4. Akamai Technologies (AKAM) – A key content delivery network (CDN) provider that ensures fast and reliable streaming.
5. Warner Bros. Discovery (WBD), Paramount Global (PARA), and Sony (SONY) – These media companies have been essential content partners for licensing and streaming rights, fueling Netflix’s growth before and during its original content development.

Features:

• Daily stock prices for Netflix and its key partners over time.
• Open, High, Low, Close, Volume and other financial metrics for each company.
• The dataset allows users to analyze correlations between Netflix and companies that provide essential services like cloud infrastructure, content delivery, and licensed content.

Use Cases:

• Financial Analysis: Evaluate stock trends, price movements, and performance relationships among Netflix and its affiliated companies.
• Business Strategy: Understand how various partnerships and technological dependencies have influenced Netflix’s rapid growth.
• Machine Learning: Use the data for stock prediction models, time-series analysis, or risk assessments by understanding the historical volatility of these companies.

Potential Analysis:

• Stock Price Correlation: Investigate the correlation between Netflix’s stock and its key partners, such as Amazon and Intel.
• Impact of Technological Partners: Assess the importance of key tech partnerships in supporting Netflix’s streaming infrastructure and business operations.
• Influence of Media Partnerships: Examine how the stock performance of media companies like Warner Bros., Sony, and Paramount align with Netflix’s success.

Conclusion:

By analyzing the historical stock data of Netflix alongside these affiliated companies, users can gain deeper insights into how a diverse set of industries—including technology, media, and cloud infrastructure—come together to create the backbone of Netflix’s success. This dataset serves as a valuable resource for financial analysts, machine learning enthusiasts, and business strategists interested in the interconnections between these influential companies.

How to Use:

• Download the dataset, and explore the stock data for the companies mentioned.
• Perform exploratory data analysis (EDA), build models to predict stock prices, or conduct business analysis to uncover the relationships that drive Netflix’s market success.

This dataset provides a solid foundation for understanding the financial landscape surrounding Netflix and its key partners.

Here is the full breakdown of Netflix subscribers by region.

Employment data for Netflix revealed that roughly 29 percent of Netflix's employees in the United States as of 2023 were Asian, and over 12 percent were Hispanic. The majority of employees working for the streaming giant are white.

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

Netflix reported **** million paid streaming subscribers across the United States and Canada in the fourth quarter of 2024. This marked a growth of over **** million compared with the same quarter of the previous year. Why is Netflix losing subscribers? The EMEA (Europe, the Middle East, and Africa) region is Netflix's top-performing market in terms of subscribers, surpassing North America in the third quarter of 2022 for the first time. The company reported losing an estimated *** million users worldwide in the second quarter of 2022, with the number of Netflix users standing at approximately *** million that quarter. But why have audiences canceled their subscriptions? One reason for the unprecedented drop in account holders is Netflix's monthly fee, which has been increasing rapidly over the past few years. On top of that, viewers have also voiced criticism over Netflix's cancellation of popular shows and its lack of big movie franchises. What are audiences watching? Netflix's vast content library offers anything from reality TV to Hollywood blockbusters, with shows and movies delivered in many languages. As of mid-2024, European countries such as Slovakia, Bulgaria, and Slovenia boasted the largest content catalogs on Netflix. In the U.S., where audiences could choose from approximately ***** titles, “NCIS” and “Suits” ranked among the most popular streaming series on Netflix in 2023. As of that year, fan favorites “Stranger Things” and “3 Body Problem” were the most expensive Netflix original series, with production costs of ** and ** million U.S. dollars per episode, respectively.

I extracted this data to find the unpopular movies on Netflix. The dataset I used here comes directly from Netflix movies data, which consists of 4 text data files, each file contains over 20M rows, over 4K movies, and 400K, customers. Altogether over are 17K movies and 500K+ customers!

I made some modifications and I extracted the e df_avgRating_with_usersCount.csv from the original data after applying some mathematical operations to get the average ratings and the count of users who made the ratings for each movie in movie_id below. Feel free to browse and use the data within your notebooks.

Here you could find my previous notebook on Kaggle to extract the dataset

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

In 2023, Netflix reportedly had an almost equal share of male and female employees working for the company worldwide. The employees were reported as 51.6 percent female and 45.8 percent male, with 1.4 percent recorded as additional gender identities.

In 2024, Netflix revealed that it had 89.63 million paying streaming subscribers in the United States and Canada. North America had long been Netflix's biggest market, though subscriber numbers in the EMEA region surpassed that in the U.S. and Canada for the first time during 2022. The number of paid streaming memberships in Asia Pacific grew the most, by 13 percent compared with the previous year.

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.

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

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

Context

This dataset contains OTT + Video Streaming Platforms - Revenue and User Stats 2011-21

Project / Tasks

• Time Series Analysis.
• Market Analysis.
• Analysis and predict future.
• Find patterns.

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About OTT, Video Streaming Platforms

OTT stands for “over-the-top,” which refers to any TV or video content that's streamed over the internet. This includes any web or app-based streaming service, like Netflix, YouTube, Disney Plus and many more. There's a wide range of OTT platforms, including Netflix, Disney+, Hulu, Amazon Prime Video, Hulu, Peacock, CuriosityStream, Pluto TV, and so many more. Unlike OTT platforms, YouTube is a social video platform that was originally designed to allow everyday consumers to share moments caught on video. YouTube has attempted to enter the OTT market a number of times with limited success, since the market clearly sees YouTube as a place for free content.

Content

| | File | File Type | | -- | ---------------------------- | --------- | | 1 | LibrarySize.csv | CSV file | | 2 | MinuteSharing.csv | CSV file | | 3 | AppUsage.csv | CSV file | | 4 | NumSubscribers.csv | CSV file | | 5 | Revenue.csv | CSV file | | 6 | Revenue.csv | CSV file | | 7 | AdRevenue.csv | CSV file | | 8 | LiveTVSubscribers.csv | CSV file | | 9 | NumSubscribers.csv | CSV file | | 10 | Profit.csv | CSV file | | 11 | Revenue.csv | CSV file | | 12 | SubscriptionRevenue.csv | CSV file | | 13 | Valuation.csv | CSV file | | 14 | ContentSpend.csv | CSV file | | 15 | NumSubscribers.csv | CSV file | | 16 | NumSubscribersByRegion.csv | CSV file | | 17 | Profit.csv | CSV file | | 18 | Revenue.csv | CSV file | | 19 | RevenueByRegion.csv | CSV file | | 20 | Revenue.csv | CSV file | | 21 | Users.csv | CSV file | | 22 | AdRevenue.csv | CSV file | | 23 | ConcurrentViewers.csv | CSV file | | 24 | HoursWatched.csv | CSV file | | 25 | MostViewedGamesOnTwitch.csv | CSV file | | 26 | Revenue.csv | CSV file | | 27 | TwitchAgeDemographics.csv | CSV file | | 28 | TwitchGenderDemographics.csv | CSV file | | 29 | TwitchStreamers.csv | CSV file | | 30 | AppUsage.csv | CSV file | | 31 | NumSubscribers.csv | CSV file | | 32 | Revenue.csv | CSV file | | 33 | AppUsage.csv | CSV file | | 34 | NumSubscribers.csv | CSV file | | 35 | Revenue.csv | CSV file | | 36 | TopPlatforms.csv | CSV file | | 37 | PremiumSubscribers.csv | CSV file | | 38 | Revenue.csv | CSV file | | 39 | Users.csv | CSV file |

Download

• kaggle API Command !kaggle datasets download -d azminetoushikwasi/ott-video-streaming-platforms-revenue-and-users

Disclaimer

• The data collected are all publicly available and it's intended for educational purposes only.

Acknowledgement

• Cover image taken from internet.

Appreciate, Support, Share

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

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

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.

The Netflix Prize was a competition devised by Netflix to improve the accuracy of its recommendation system. To facilitate this Netflix released real ratings about movies from the users (voters) of the system. Any set of movies can be transformed into an election via a process outlined by Mattei, Forshee, and Goldsmith.This data set includes all 5 candidate elections with at least 350 voters generated by this process from 300 randomly chosen movies. Extending beyond prior work by Mattei et al. we allow for weak preferences, i.e., a voter is indifferent between a set of movies if he assigns the same rating to each of them. Thus, there are 541 possibilities to rank a given set of five movies.The archive is gzip compressed and includes 165,672 elections in PrefLib.org's TOC file format (Orders with Ties - Complete List).

Context

During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommender systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys. In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy or anything else depending on industries).

Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. As a proof of the importance of recommender systems, we can mention that, a few years ago, Netflix organised a challenges (the “Netflix prize”) where the goal was to produce a recommender system that performs better than its own algorithm with a prize of 1 million dollars to win.

Content

These datasets contain attributes about products sold on ModCloth Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed).Data includes user/item interactions.

Inspiration

Apply different paradigm, methods and algorithms to recommand right Product to the right Users, during right Time.

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*