Netflix's global subscriber base has reached an impressive milestone, surpassing 300 million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly 20 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 one 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 70 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 70.1 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.

Netflix is distinctly more popular with younger consumers in the United States than with older generations. According to the findings of a recent survey, around 75 percent of respondents aged 18 to 34 subscribed to Netflix as of mid-2021, compared to just 44 percent of those aged 65 or above.

Netflix predicts further subscriber loss

Netflix is the most popular subscription video-on-demand (SVOD) service worldwide. Millions of viewers from various demographics access the platform each day, but despite its availability in over 190 countries and its ever-expanding content catalog, Netflix reported a subscriber loss of around 200 thousand in the first quarter of 2022. It was the first time in over a decade that the streamer experienced a drop in user numbers, but according to the company, this downward trend might very well continue in the second quarter of the year. According to company reports, Netflix expects to lose an additional two million subscribers by mid-2022.

Cracking down on password sharing

Credential sharing has become an essential part of the video-on-demand (VOD) experience. Companies can stand out in today’s crowded streaming space by offering viewers to create multiple profiles and split subscription costs with other people in their household – which might be particularly appealing to younger audiences. Netflix is one of the first services to have provided multiple subscription options at various price tiers, but even so, the company has also acknowledged that millions of people share their login data without paying for additional accounts. In 2021, Netflix was estimated to have lost over 1.07 billion U.S. dollars in revenue due to password sharing. In 2022, the company reacted by announcing to charge additional sub-account fees for people streaming content outside the primary account holder’s household.

This is the official data set used in the Netflix Prize competition. The data consists of about 100 million movie ratings, and the goal is to predict missing entries in the movie-user rating matrix. |Attribute| Value| |——|—-| | Data Set Characteristics: | Multivariate, Time-Series | | Attribute Characteristics: | Integer | | Associated Tasks: | Clustering, Recommender-Systems | | Number of Instances: | 100480507 | | Number of Attributes: | 17770 | | Missing Values? | Yes | | Area: | N/A | #Data Set Information: This dataset was constructed to support participants in the Netflix Prize. There are over 480,000 customers in the dataset, each identified by a unique integer id. The title and release year for each movie is also provided. There are over 17,000 movies in the dataset, each identified by

Netflix continues to dominate the UK streaming landscape, with 17.1 million households subscribing to the service in the fourth quarter of 2024. This marks a significant increase from 16.7 million subscribers in the same period of the previous year, demonstrating the platform's enduring popularity despite fierce competition in the video-on-demand landscape. Netflix's competitors While Netflix remains the leading subscription video-on-demand service in the UK in terms of customer numbers, Amazon Prime Video boasts the largest content library among major SVOD platforms, with over 42.6 thousand hours of content available as of May 2024. However, when it comes to market share based on user interest, Netflix still holds the top spot, edging out providers such as Amazon Prime Video, Disney+, and Apple TV+. Demographic preferences Interestingly, streaming preferences vary across age groups. Among viewers aged 65 and above, Amazon Prime Video is the preferred choice in the UK for 36 percent, while Netflix captures one-third of this demographic. This contrasts with the overall market dominance of Netflix, suggesting that older audiences may have different content preferences. The generational divide in streaming habits is further illustrated by data from Flanders in Belgium, where millennials show a slightly higher Netflix usage rate compared to Gen Z, both significantly outpacing older age groups.

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

Dataset from Netflix's 10-K annual reports, which include externally audited data about financial activities of businesses based in the US. For a description of the data compiled see the .docx document. The code included was used in the following research:

Title: Evidence of diseconomies of scale in subscription-based video on demand services.

Abstract: This study provides evidence of diseconomies of scale in Netflix, a major subscription-based video on demand (SVOD) service provider. This contradicts the common belief in prevalent economies of scale for such e-businesses. We, however, rely on a comprehensive analysis of a dataset where we have collected and combined publicly available and audited financial data, mostly coming from Netflix's 10-K reports. In our analysis we employ several user-cost models, namely a baseline linear model, a power law model, an exponential model, and a logarithmic model. Such models often appear (in different variations) in economics literature, but are almost inexistent in the rhetoric around SVOD business models. Corroborating the applications of all these mathematical models on the financial data of Netflix identifies a super-linear increase in costs with expanding user basis, indicating the rising per-user costs that defines diseconomies of scale. These findings provide critical insights into SVOD service scalability, challenging prevailing assumptions and informing expectations about cost dynamics in this industry.

Explore comprehensive insights in Leads Rank TLD profiles, featuring detailed information for .netflix. Utilize our API for efficient data access.

According to the most recent data, 83 percent of consumers in the United States were using a subscription video-on-demand service in 2023, an increase of over 10 percentage points in five years. It is no secret that one of the most popular platforms (and certainly the one with the most U.S. subscribers) is Netflix. The number of Netflix streaming subscribers in the United States and Canada passed the 70 million mark for the first time in early 2020.

Netflix as the most used video streaming service in the U.S. To say Netflix has the monopoly on the U.S. streaming market would be an understatement, and with a wealth of original content appearing all the time, Netflix’s appeal is built to last. Data shows that Netflix has more viewers than Hulu and Amazon in the U.S., leaving services such as Disney+, Apple TV+, and ESPN+ trailing far behind. How to satisfy subscribers? However, the threat of new competitors could cause Netflix's subscriber base to dwindle if video consumers decide to go elsewhere. Upcoming services ranging from the long anticipated Disney+ to Warner Bros. Discovery's HBO Max and Discovery+ will likely draw some customers away from Netflix by virtue of what they can offer, and as new services enter the market, they will likely reclaim their own. Additionally, recent price increases in light of an upcoming recession led to losses in Netflix's subscriber numbers in the first half of 2022.

Netflix is currently facing the challenge of retaining subscribers worldwide, with data from the second quarter of 2023 revealing that Germany and Spain experienced the highest churn rates at nine percent. Australia followed closely, while France had a relatively low cancellation rate at just two percent. Impact of the new business model The subscription video-on-demand (SVOD) market has experienced a significant increase in the past decade, driven by major players, like Netflix, Amazon Prime Video, and Disney+. However, the anticipated slowdown in revenue growth, coupled with the increasing popularity of ad-supported content and subscription cycling, underscores the evolving dynamics of consumer preferences within the streaming industry. Netflix's introduction of a basic plan served with commercials in late 2022 had a significant impact on subscriber behavior in the U.S., with the ad-supported tier accounting for 30 percent of sign-ups in September 2023. Growth despite challenges Netflix's global position remains strong. With over 269 million paid subscribers worldwide, the company's revenue continues to grow, reaching nearly 9.4 billion U.S. dollars in the first quarter of 2024. While challenges persist, such as the increasing popularity of other streaming services and the impact of price increases on subscriber numbers, Netflix's plan to generate revenue from sources, like licensing, ads, and merchandise, positions the company for future profitability.

The AI-based recommendation system market is experiencing robust growth, driven by the increasing adoption of AI across various sectors. The market size in 2025 is estimated at $2977.2 million. While the provided CAGR (Compound Annual Growth Rate) is missing, considering the rapid advancements in AI and its widespread application in personalization, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 15%. This growth is fueled by several factors, including the exponential increase in data volume, advancements in machine learning algorithms (like collaborative filtering, content-based filtering, and hybrid approaches), and the rising demand for personalized experiences across e-commerce, online education, and entertainment platforms. Companies like AWS, Google, and Netflix are leading the market, investing heavily in research and development to enhance their recommendation engine capabilities. The diverse application segments, ranging from e-commerce to healthcare, contribute significantly to market expansion. The adoption of AI-powered recommendation systems is expected to continue its upward trajectory, driven by the need for businesses to improve customer engagement, increase sales conversions, and enhance overall user experience. Further growth will be propelled by the increasing sophistication of algorithms enabling more accurate and relevant recommendations. The integration of AI-based recommendation systems with other technologies, like big data analytics and cloud computing, will further amplify its impact across diverse industries. Despite this optimistic outlook, challenges remain, including data privacy concerns, the need for robust data security measures, and the potential for algorithmic bias. Addressing these challenges will be crucial for sustained and responsible market growth in the coming years. Strategic partnerships and collaborations among technology providers and businesses across various sectors will play a vital role in shaping the future trajectory of this rapidly evolving market.

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.

Data revealed that Pakistan was the least expensive place in the world to get Netflix as of October 2023, with a monthly subscription for the standard plan costing 2.90 U.S. dollars. Egypt and Nigeria followed, with monthly prices for a Netflix subscription amounting to 3.88 and 4.65 U.S. dollars respectively. Netflix’s plans and costs In addition to the standard package, Netflix provides four different plans in its home country, the United States. The premium version is the most expensive plan, but customers can watch content on four different devices, while the standard supports two devices and the basic plan just one device. In order to offset income and subscriber losses, the streaming giant launched a fourth tier in November 2022: basic with ads. This plan, which is partly financed through advertising, can be acquired for a monthly fee of less than five U.S. dollars. Netflix’s measures in times of crisis Facing constant and growing competition, as well as an economic downturn, Netflix is making changes in an effort to maintain its dominant position within the video streaming industry. While the service has recently started to crack down on account sharing in major countries, it announced in February 2023 to cut prices in over 100 territories, including especially Central and South American, African, and Asian countries. The latter affects around 10 million Netflix customers, and should boost subscriber additions and combat churn in these markets.

Data on the subscription video-on-demand (SVOD) market in the United States has revealed that revenue from this format reached around 37.1 billion U.S. dollars in 2023, over 20 percent higher than the figure recorded the previous year. SVOD services like Netflix have proved incredibly popular among U.S. consumers and have meant that traditional providers have taken a big hit when it comes to subscriber numbers.