netflix big data case study

How Netflix uses Data Analytics: A Case Study

The contribution of big data and analytics in the success of netflix..

By Sarthak Niwate on 2021-04-28

Netflix’s current company valuation is $234 billion. It is currently renowned as the most valued company/media company in the world and transcends even Disney. The success lies in a secret term that is no secret (but the way it’s used in a certain way is a secret) — customer retention.

Customer retention may be defined as the process of engaging the customers and appealing to them to use the service or buy the product.

Now, this may look like a simple tactic at first glance but do note that this is considered by many as the most powerful tactic used by any media company. And Netflix used it so intelligently that their customer retention rate is extremely impressive and keeps increasing over the years.

Can you guess the total subscribers of Netflix? Up until December 2020, Netflix subscribers (paid subscription) amounted to a whopping 203.66 million. This is an excellent milestone for Netflix, as it has crossed the 200 million mark for the first time.

Netflix has really gone a long way ahead of its competitors because of its more successful TV shows and movies that have garnered attention and a high number of views. This has helped escalate the rate of subscriptions. Netflix has been more successful in identifying the true interest of customers or audiences.

Are you wondering why people choose Netflix? And why you chose it?

I came across an informative blog that talked about the top reasons why people choose Netflix. I thought that I should share this here.

How Netflix uses data and big data analytics?

The question looks so simple and straightforward, but only people having the background or experience of working, studying, or playing with data can understand the depth of this question.

For any company or organization, data collection is essential. Imagine Netflix with its 203 million subscribers. Studying the traits of the data of this many customers would be a tremendous task. Netflix uses the collected information, converts them into insights, results, or visualizations, and recommends TV shows and movies as per customers’ preferences and interests. Just read this line again — it almost feels like a supernatural talent or power.

You should be able to relate if you’re a Netflix user. According to Netflix’s study, viewer activity depends on personalized recommendations and the results are true for over 75% of subscribers. Diving deeper into it, several data points have been collected and a detailed profile of each subscriber has been generated. It's hard to believe but, the profile of a subscriber created by Netflix is much more detailed than the information or preferences provided by the subscriber at the beginning of their Netflix usage.

If I want to generalize this, data collected by Netflix is mostly about customer interaction on the application or webpage and responsiveness to shows or movies. To put it simply, if you’re watching any TV show or movie on Netflix, it knows the date, location, and device being used to watch, as well as the time of your watching. On top of that, Netflix also knows about how and when you pause and resume your shows and movies. They also take into consideration if you are completing the show or not, how many hours, days, or weeks to complete the episode or a season or a movie.

Ultimately, it tracks every action taken by the user on Netflix and considers it as a data point. How many metrics will be there in total which Netflix might be using for data collection?

The work is not yet completed! What are some of the extreme points you can think of after reading the last paragraph? Give it a try. You will realize the amount of effort and intelligence required of and implemented by Netflix. Do you have a habit of watching your favorite scenes repeatedly? Then Netflix knows that. Netflix captures screenshots of scenes that viewers watch repeatedly and it categorizes you as per the rating. It keeps track of how many times you search before choosing to watch a show and even what keywords you have used in your search. Imagine how beautiful that data would look if gathered properly. And then after the collection of data, the data is cleaned and a buzz word is implemented, that being the ‘recommendation algorithms’.

At this point in time, you might have understood the reason behind the success of Netflix’s tremendous ability to collect, process, and use data.

Netflix’s ability to collect and use the data is the reason behind its success. It results in better customer retention per year. The study says the rate of customer retention is increasing on Netflix because 80% of users follow the recommendation, and the recommended show or movie is streamed.

Have you ever heard of ‘green-light original content’? Green-light means being allowed to do something. So, green-lit original content is verified or rated content approved on the basis of various touchpoints taken from the user database.

Big data and certain analysis techniques are used for custom marketing, say, for example, to promote a TV show or a movie Netflix releases (which might have various promos or trailers). If a viewer watches content that is more centered on women, the viewer will get a trailer of a movie that is more focused on female characters in that movie.

However, the same applies to many aspects like someone watches movies of certain directors only or certain actors or actresses only. This in and out study or report of each customer reduces the time spent to research on marketing strategies because Netflix already knows the interests and sensitive likes or dislikes of their subscribers.

This is nothing but tracking the actions of subscribers and collecting their data based on this. One technique that is very traditional and Netflix uses that too is to take feedbacks from subscribers. The feedback is then converted into a rating and then the team works on system improvement or recommendations.

Netflix veteran Joris Evers says that there are 33 million various versions of Netflix.

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How Netflix used big data and analytics to generate billions

Netflix is successful thanks to big data and analytics.

With a company valuation of over $164 billion, Netflix has surpassed Disney as the most valued media company in the world. Their success can be attributed to their impressive customer retention rate, which is 93% compared to Hulu’s 64% and Amazon Prime’s 75%. However, it’s not just their ability to retain most of their 151 million subscribers that have made them successful.

Netflix has flown ahead of its competitors because it also makes more successful TV shows and movies, hits like ‘House of Cards’, ‘Orange Is The New Black’, and ‘Birdbox’ have garnered a lot of attention and high viewership, driving up the rate of subscriptions. Netflix has also been more successful in identifying what their audience wants.

In 2017, 93% of original TV shows were renewed . A contrast to cable television where there is only a 35% chance of a show being renewed after the first season. What is the secret to their success? Big data and analytics.

How Netflix uses big data and analytics

So, how does Netflix use data analytics? By collecting data from their 151 million subscribers, and implementing data analytics models to discover customer behaviour and buying patterns. Then, using that information to recommend movies and TV shows based on their subscribers’ preferences.

According to Netflix, over 75% of viewer activity is based off personalised recommendations. Netflix collects several data points to create a detailed profile on its subscribers. The profile is far more detailed than the personas created through conventional marketing.

Most significantly, Netflix collects customer interaction and response data to a TV show. For example, Netflix knows the time and date a user watched a show, the device used, if the show was paused, does the viewer resume watching after pausing? Do people finish an entire TV show or not, how long does it take for a user to finish a show and so on.

Netflix even has screenshots of scenes people might have viewed repeatedly, the rating content is given, the number of searches and what is searched for. With this data, Netflix can create a detailed profile on its users. To collect all this data and harness it into meaningful information, Netflix requires data analytics. For example, Netflix uses what is known as the recommendation algorithm to suggest TV shows and movies based on user’s preferences.

Netflix’s ability to collect and use the data is the reason behind their success. According to Netflix, they earn over a billion in customer retention because the recommendation system accounts for over 80% of the content streamed on the platform. Netflix also uses its big data and analytics tools to decide if they want to greenlight original content. To an outsider, it might look like Netflix is throwing their cash at whatever they can get, but in reality, they greenlight original content based on several touch points derived from their user base.

For example, Netflix distributed ‘Orange is the New Black’ knowing it would be a big hit on their platform. How? Because ‘Weeds’, Jenji Kohan’s previous hit performed well on Netflix in terms of viewership and engagement.

Netflix even uses big data and analytics to conduct custom marketing, for example, to promote ‘House of Cards’ Netflix cut over ten different versions of a trailer to promote the show. If you watched lots of TV shows centred on women, you get a trailer focused on the female characters. However, if you watched a lot of content directed by David Finch, you would have gotten a trailer that focused the trailer on him. Netflix did not have to spend too much time and resources on marketing the show because they already knew how many people would be interested in it and what would incentivise them to tune in.

In addition to collecting data on subscriber actions, Netflix also encourages feedback from its subscribers. One feedback system is the thumbs up/thumbs down system that replaced their rating system, the system improved audience engagement by a significant margin, which enabled them to customise the user’s homepage further. According to Joris Evers, Director of Global Communications, there are 33 million different versions of Netflix.

Key takeaways

Powerful analytics models can process terabytes of data to churn out meaningful information. Judicious use of data analytics is the main reason for Netflix’s success. In fact, big data and analytics are so vital to Netflix’s success that you may as well call them an analytics company instead of a media company. Netflix’s success highlights the value of data analytics because it presents an incredible insight into user’s preferences allowing them to make smart decisions that deliver maximum ROI on their choices.

Want to learn about the positive effects of big data and analytics? Find out more at Selerity .

If you’re interested in big data analytics for your organisation, take a look at our Selerity analytics desktops . With it, you access a cutting-edge SAS pro analytics environment that you can leverage for a variety of analytics applications. Get in touch with us for more details .

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Digital Innovation and Transformation

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• Assignment: Competing with Data Challenge

Netflix: Disrupting creation with big data

Netflix was founded back in 1997 with the aim to disrupt video store with a new business model, DVD by mail. Since its inception, Netflix business model has shifted drastically. Today the company is valued at over USD 128 billion and is considered one of the most innovative streaming companies in the world.

It is not surprising for a company in today era to leverage big data. However, Netflix interaction with machine learnings dated back to the year 2006. Back when the company business model was mere to deliver DVDs by mail, Netflix launched the “Netflix prize” campaign to crowdsource movie rating prediction algorithm. The company set the prize money of USD 1 million to incentivize the crowd. As the business model of the company changes over time, big data play an even more instrumental role in the company.

Given the original business model with the help of the crowdsourced algorithm, Netflix was working with a limited number of data. The company only have a history of movie ratings, rental dates, and basic customer profile. Netflix leapfrogged into the next level of big data game when the company introduced its streaming service in 2007. Overnight, Netflix was able to track data around customer behavior in addition to the original data gathered. For example, the company will know how much time you spend picking the movie, what time of day did you watch it, when did you pause, did you re-watch etc. This allows the company to improve it’s backend operation through peak time vs. downtime analysis while learning more about the customer.

As the internet infrastructure improves, the company emphasize on leveraging big data to better serves the customer. One example of this effort is elements tagging. Netflix invests a lot of resources to tag many small elements in the movies. This effort allows them to create a massive granular data set beyond just movie ratings. The effort allows them to create almost 77,000 micro-genres. This enables the company to select the best movie possible to recommend its customers. This effort equipped Netflix with a massive data advantages over any of its competitor. Through better data and recommendations algorithms, Netflix creates the better customer experience. The company was able to claim value through a massive USD 1 billion savings each year from improved customer retention.

The company close relationship with big data continues as Netflix enter the business of show creation. As the company venture into content creation, its leverage big data to engineering a “guaranteed success” show. Netflix invested USD 100 million on 2 seasons of House of Cards, a series based off of British TV show. Through its network of 27 million subscribers in the US and 33 million worldwide (at the time), Netflix knows that the British version of the TV show was performing well. Furthermore, the company knew that there was a massive demand for the work of director David Fincher. Finally, Netflix also knew that any show starring Kevin Spacey has a high likelihood of success. Bringing these three factors together gave Netflix confident in producing the show.

Pathways to a Just Digital Future

The content of the show was not the only part that the company created through big data. Even the poster creation was influenced by machine learning. Through observing elements of historically successful posters, Netflix replicated the elements to create its own version for House of Cards. This cutting-edge effort in leveraging big data to help produce creative content was unheard of in the creative industry. The traditional creative film industry does not have the data nor the technology to compete with Netflix.

Through an endless focus on leveraging big data to improve customer experience regardless of the business model, Netflix was able to dominate the streaming industry. The company was able to overcome the lack of data in the film industry by creating its own set of data and was able to combine data with creativity. There is a massive opportunity for Netflix in the creative industry. It will be interesting to see how Netflix, through its big data and algorithms, disrupt Hollywood industry.

https://insidebigdata.com/2018/01/20/netflix-uses-big-data-drive-success/

https://www.smartdatacollective.com/big-data-how-netflix-uses-it-drive-business-success/

https://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/

https://www.wired.com/insights/2014/03/big-data-lessons-netflix/

https://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html

https://www.nasdaq.com/symbol/nflx

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Please note you do not have access to teaching notes, netflix leading with data: the emergence of data-driven video.

Publication date: 20 January 2017

Teaching notes

By 2009 Netflix had all but trounced its traditional bricks-and-mortar competitors in the video rental industry. Since its founding in the late 1990s, the company had changed the face of the industry and threatened the existence of such entrenched giants as Blockbuster, in large part because of its easy-to-understand subscription model, policy of no late fees, and use of analytics to leverage customer data to provide a superior customer experience and grow its e-commerce media platform. Netflix's investment in data collection, IT systems, and advanced analytics such as proprietary data mining techniques and algorithms for customer and product matching played a crucial role in both its strategy and success. However, the explosive growth of the digital media market presents a serious challenge for Netflix's business going forward. How will its analytics, customer data, and customer interaction models play a role in the future of the digital media space? Will it be able to stand up to competition from more seasoned players in the digital market, such as Amazon and Apple? What position must Netflix take in order to successfully compete in this digital arena?

To examine the benefits and risks of investment in analytical technology as a means for mining customer data for business insights. Students will develop a strategy position for Netflix's investment in technology and its digital media business. Students must also consider how new corporate partnerships and changes to the customer channel model will allow the company to prosper in the highly competitive digital space.

• Blockbuster
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Walker, R. , Jeffery, M. , So, L. , Sriram, S. , Nathanson, J. , Ferreira, J. and Feldmeier, J. (2017), "Netflix Leading with Data: The Emergence of Data-Driven Video", . https://doi.org/10.1108/case.kellogg.2016.000232

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You do not currently have access to these teaching notes. Teaching notes are available for teaching faculty at subscribing institutions. Teaching notes accompany case studies with suggested learning objectives, classroom methods and potential assignment questions. They support dynamic classroom discussion to help develop student's analytical skills.

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Big Data Lessons From Netflix

• Partner Content
• Author: Phil Simon. Phil Simon

In a data-driven environment like Netflix, data visualization plays a key role. It must. In The Visual Organization , I offer the following definition of data visualization. Dataviz signifies the practice of representing data through visual and often interactive means. An individual dataviz represents information after it been abstracted in some schematic form. Finally, contemporary data visualization technologies are capable of incorporating what we now call Big Data .

According to its corporate blog, Netflix considers data visualization to be of paramount importance. Many of Netflix’s major systems contain significant dataviz components. And, like other Visual Organizations covered in this section, Netflix uses data-visualization tools on a continuous basis, not occasionally. That is, Netflix employees routinely look to existing dataviz tools to tweak algorithms, garner new insights, and solve pressing business issues.

Jeff Magnusson serves as the manager of data platform architecture at the company. On June 27, 2013, at the Hadoop Summit, he provided a rare window into the Netflix Big Data ethos. Magnusson presented with Charles Smith, a colleague and a software engineer. The two talked about how data should be accessible, easy to discover, and easy to process for everyone. The title of the talk: “ Watching Pigs Fly with the Netflix Hadoop Toolkit. ” During their presentation, Magnusson and Smith laid out three key tenets of the Netflix data philosophy:

• Data should be accessible, easy to discover, and easy to process for everyone.
• Whether your dataset is large or small, being able to visualize it makes it easier to explain.
• The longer you take to find the data, the less valuable it becomes.

These canons explain why Netflix is the quintessential Visual Organization. At the heart of its business lie some of the most sophisticated Big Data tools on the planet, including no shortage of dataviz applications. At a high level, these tools serve the interests of two critical constituencies: customers and technical professionals. It’s important to note, however, that satisfying both masters ultimately benefits everyone: executives, stockholders, nontechnical employees, and others.

Customer Insights

Look at the covers of House of Cards and the 2010 version of Macbeth that ran on the PBS series Great Performances .

At first glance, they are eerily similar. They both display older white men with blood on their hands—Kevin Spacey and Patrick Stewart, respectively—against primarily black backgrounds. Figure 3.1 illustrates the detailed color breakdown:

Figure 3.1 manifests the obvious: the covers of the two shows are much more similar than dissimilar. At the same time, though, subtle differences exist—and Netflix can precisely quantify those differences. What’s more, Netflix can see if they have any discernible impact on subscriber viewing habits, recommendations, ratings, and the like.

Figure 3.2 shows a similar color analysis of the House of Cards, Arrested Development, and Hemlock Grove, an American horror thriller and Netflix origi­nal program that premiered on April 19, 2013.

Given the cost of producing high-quality original content, why would Netflix create the cover for a new series in a vacuum? Why wouldn’t decision-makers look at the company’s vast trove of data? With subscribers bombarded by nearly unlimited options, why leave such a potentially critical aspect completely to chance? After all, Netflix possesses the data to make the most informed business decision possible. No, Netflix didn’t invite outsiders to pro­duction meetings for Hemlock Grove and House of Cards . Still, you can bet that its head honchos carefully reviewed subscriber data when selecting the covers to these series.

At Netflix, comparing the hues of similar pictures isn’t a one-time experi­ment conducted by an employee with far too much time on his hands. It’s a regular occurrence. Netflix recognizes that there is tremendous potential value in these discoveries. To that end, the company has created the tools to unlock that value. At the Hadoop Summit, Magnusson and Smith talked about how data on titles, colors, and covers helps Netflix in many ways. For one, analyz­ing colors allows the company to measure the distance between customers. It can also determine, in Smith’s words, the “average color of titles for each customer in a 216-degree vector over the last N days.”

In a word, wow .

How many organizations understand their customers to this extent? I would hazard to guess that few do. Most companies would love to know even half as much about their customers as Netflix does.

This begs the obvious question, how? Through Big Data and dataviz, Netflix seamlessly delivers mind-boggling personalization to each customer. At the same time, Netflix can easily aggregate data about customers, genres, viewing habits, trends, and just about anything else. Equipped with this data, Netflix can attempt to answer questions that most organizations can’t or won’t even ask. With respect to color and covers, these include the following:

• Are certain customers trending toward specific types of covers? If so, should personalized recommendations automatically change?
• Which title colors appeal to which customers?
• Is there an ideal cover for an original series? Or should different colors be used for different audiences?
• And plenty more.

Simon Says: Think Visually

In short, Visual Organizations like Netflix can ask better questions and make better business deci­sions based upon superior data, dataviz tools, and a culture that recognizes the importance of both.

Watch the trailer for  The Visual Organization   here , then share your thoughts below.

Phil Simon  is a frequent keynote speaker and recognized technology expert.

This post was adapted from his new book, “The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions” Wiley, 2014.

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How Data Analytics can be a Game Changer: A Netflix Case Study

As per McKinsey, machine learning that incorporates a wide plethora of algorithms in the past few years is evolving faster due to the advent of analytics. With businesses investing heavily in cloud and the rapid digitization of the professional ecosystem, analytics is all set to become a significant aspect in deciding the fate of organizations.

Data Analytics – How Can It Transform Your Business?

As per a study by SAS , more than 70% of organizations believe that data analytics plays a vital role in getting precise insights. The study also said that out of ten organizations, six of them said that leveraging analytics makes them more innovative. Analytics is slowly but steadily evolving in the competitive landscape. Industry leaders are using analytics to make decisions that can help them to stay ahead of their peers, besides exploring better revenue opportunities, new markets, and building a better relationship with their customers.

The very reason why business models of Uber, Airbnb, and Spotify are sustaining is data and analytics. When you digitize your interactions with customers, you create a window to get immense information. This customer information could be utilized for making effective marketing strategies, better products, and making more sales.

A lot of C-suite leaders now understand the importance of data and understand the risk it carries if not secured correctly. What is startling is and makes investment in data and analytics even more important is the kind of ROI it gives. In the Journal of Applied Marketing Analytics, Jacques Bughin says, the ROI on data and analytics is better than the investments done in computers during the 1980s.

The power of data and analytics is also harnessed to improvise core operations or create new business models from scratch. The most exceptional example is Netflix. Netflix has efficiently used its customer data to refine its recommendation engine and give a better experience to the users. Not only that Netflix has surpassed Disney as the most valued media company in the world with a valuation of more than $160 billion. One of the biggest reasons for their success is their impeccable customer retention rate. Their customer retention rate is more than a staggering 90% which is far better than Hulu’s 64% and Amazon Prime’s 75%.

The second most important reason why Netflix is way ahead of its competitors is- Content Creation . The kind of quality shows and movies it makes like “Orange is The New Black”, “Sacred Games”, and “BirdBox”. These shows have received a thunderous response across the globe resulting in a steady rise in subscription rates. One of the primary reasons why they succeed in making better content is that they understand what their audience wants to see leveraging data and analytics.

So, How Does Netflix Leverage Big Data and Analytics?

Netflix has digitized its interactions with its 151 million subscribers . It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns. It then leverages that information to recommend movies and TV shows customized as per the subscriber’s choice and preferences.

As per Netflix, around 80% of the viewer’s activity is triggered by personalized algorithmic recommendations. Where Netflix gains an edge over its peers is that by collecting different data points, it creates detailed profiles of its subscribers which helps them engage with them better.

Netflix collects information on how a user interacted and responded to a TV show or a movie. If we go into details, it collects the following data: –

• Time and date when a user watched a show
• The device used to watch the show
• If the user pauses the show, do they resume watching
• Does the user binge-watch an entire season of a TV show?
• If they do, how much time does it take to binge watch it?

More than that, Netflix has ratings that the viewer gives to the content they watch, the number of searches they do, and what they search. The information collected is enough for creating a detailed profile of a user, and this is exactly what Netflix does. It leverages data analytics to make a robust recommendation algorithm that suggests the best content to the subscriber as per their needs and preferences. The user no more must endlessly search through streams of content to find out what he or she wants to watch. Netflix makes the job easier for them in the process, giving them a better and customized viewer experience.

The recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers which has helped Netflix earn a whopping one billion via customer retention . Due to this reason, Netflix doesn’t have to invest too much on advertising and marketing their shows. They precisely know an estimate of the people who would be interested in watching a show.

Apart from monitoring the online behavior of their users, Netflix has a feedback system in place. They encourage feedback from their audience, which further helps them understand their preferences and helps them in suggesting better shows and creating better content.

Why Investing in Data Analytics is Important?

There is a data explosion today, and the need for analytics has been growing exponentially. Tools and software are being developed to get precise insights from data.

If you want to know your customers better, find revenue opportunities, and tap into new markets. You need to have a mechanism that helps you gain better insights. As an organization, investing in data analytics will give you four significant benefits.

1. A Deeper Understanding of Customers

Earlier companies would generally categorize customers based on age, gender, and location. Now with the help of AI, one can map the digital footprints of their customers. Decision-makers can go through crucial behavior patterns of customers like price sensitivity, brand affinity, affluence, and preferences. These kinds of data mapping help in understanding your customers better enhancing your ability to build better products and services for them.

2. Early Detection of Problems in Products And Services

More than half of the professionals across North America and Europe are heavily dependent on analytics to enhance the quality of their products and services as per research from Forbes Insights and Cisco. Analytics can give you precise insights on the kind of concerns customers have, their changing needs, and based on that, you can innovate your offerings.

3. Identifying Better Marketing Strategies

With various digitization channels for customer interaction available now, businesses are adopting an omnichannel approach to engage with customers. Using analytics, marketers can get inputs on how to have meaningful engagements with customers across all channels. Also, analytics can help analyze successful marketing programs and identify strategies that yield better ROIs.

4. Finding Ways To Reduce Expenses

Once you start getting insights at departmental levels, it will help you identify areas where you can curb your costs. Insurance companies saved a good amount of money by identifying patterns of fraud and dismissing false claims.

How To Harness The Power of Data?

As an organization, do not be afraid of change. If you are yet to use analytics as an organization, start with small steps, fail faster, and make a steady transition. Do not go for overnight results instead practice consistency and prioritize your efforts.

The first step you need to do as a decision-maker is incorporating data and analytics into the core vision of the organization, focus on nurturing a data-driven culture. Slowly but steadily create a powerful data infrastructure and hire talent to operate it, make sure to highlight your data-driven culture in your employer branding campaigns.

Your success doesn’t lie in adopting the most powerful technology rather digitization of your organization from the bottom. Companies like Netflix, Amazon, and Google, who are leading the analytics game, gradually transitioned to a data-savvy culture. It wasn’t all overnight but a gradual process that took a few years. Not only did they heavily invest in analytics but they have also kept themselves observant of the changing trends of artificial intelligence. They are putting the case strongly before all other organizations- if you want to survive in the market, you need to invest in data analytics, and that is not negotiable at all. Contact Us for more details.

References-

1. https://seleritysas.com/blog/2019/04/05/how-netflix-used-big-data-and-analytics-to-generate-billions 2. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-companies-are-using-big-data-and-analytics

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Netflix and Big Data

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Rocco Bellanova , Gloria González Fuster

Surveillance Studies often look at cultural products as pedagogical or heuristic devices, as if they were windows into the popular representation of surveillance practices. However, artworks may also be the (by-)products of consumers' surveillance. Online platforms like Netflix harvest vast amounts of data about clients' behaviour, so to predict their interests and produce more successful, profitable creations. In this chapter, we discuss how to think about surveillance with and against Netflix, focusing on the tensions between databases and narratives, and between politics and data-driven fiction. We explore how surveillance practices are both presented and performed when Big Data gleaned from viewers is used to tailor-script a series questioning mass surveillance, such as House of Cards. We argue that surveillance then displays itself as an embodied and transformative experience. While viewers can figure its inner workings in a more concrete manner, they are, at the same time, turned into data-breeding publics.

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Eva-Patricia Fernández-Manzano graduated in audiovisual communication and obtained her PhD degree (2005) from Complutense University of Madrid. She has a Master's in audiovisual business management from Instituto de Empresa, and a Master's in big data and business intelligence management from Escuela de Organización Industrial (EOI), both in Madrid. She is a professor at the Rey Juan Carlos University on media management. Her research focuses on how media companies work with technology. She has published several papers and books about media and big data, the last one being Big data, eje estratégico de la industria audiovisual (UOC, 2016).

Blake Hallinan

How does algorithmic information processing affect the meaning of the word culture, and, by extension, cultural practice? We address this question by focusing on the Netflix Prize (2006–2009), a contest offering US$1m to the first individual or team to boost the accuracy of the company’s existing movie recommendation system by 10%. Although billed as a technical challenge intended for engineers, we argue that the Netflix Prize was equally an effort to reinterpret the meaning of culture in ways that overlapped with, but also diverged in important respects from, the three dominant senses of the term assayed by Raymond Williams. Thus, this essay explores the conceptual and semantic work required to render algorithmic information processing systems legible as forms of cultural decision making. It also then represents an effort to add depth and dimension to the concept of “algorithmic culture.”

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Emanuele Arielli

Taste and the algorithm-DRAFT-1. Aesthetics in the digital age: externalization and automation of taste 2. Algorithmic customization 2.1 Photography as networked perception 2.2 Music and the extractions of acoustic attributes 2.3 Film: the calculation of movie culture 3. Categorizations and the structuring of perception 4. From suggestion to profiling: the subject as a bundle of traits 5. The internalization of algorithmic taste: influences on creativity 6. Conclusion: " Know thy Algorithm " Abstract Today, a consistent part of our everyday interaction with art and aesthetic artefacts happens through digital media where our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented and we are then fed back through tailored information and contents. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamic of creative process. All are involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. Concerning this issue, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, showing how aesthetics could contribute in the ongoing debate about the impact of today's " algorithmic culture " .

adrian athique

This article considers the future of audience research in an era of big data. It does so by interrogating the dynamics and potentials of the big data paradigm in an era of usergenerated content and commercial exploitation. In this context, it is proposed that the major dynamics of big data are a conjoint application of numerology and alchemy in the information age. On this basis, the potentials of new data techniques are addressed in light of the critical gap between audience data and the audiences themselves.

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The recent introduction of video streaming services into the Australian television industry has already had a significant impact on the local broadcast and subscription ecology. Undoubtedly, the sudden influx of foreign content and choice, paired with increased content deregulation, will have considerable ramifications for the Australian production industry in the long term. While many have predicted widespread devastation, this paper suggests that access to ‘big data’ and increased audience ‘addressivity’ (Hallinan and Striphas 2014, 13) could in fact serve to protect and promote the continued production and consumption of local drama content via this ‘new screen’ option.

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This introduction to the special issue on data and agency argues that datafication should not only be understood as the process of collecting and analysing data about internet users, but also as feeding such data back to users, enabling them to orient themselves in the world. It is important that debates about data power recognise that data is also generated, collected and analysed by alternative actors, enhancing rather than undermining the agency of the public. Developing this argument, we first make clear why and how the question of agency should be central to our engagement with data. Subsequently, we discuss how this question has been operationalized in the five contributions to this special issue, which empirically open up the study of alternative forms of datafication. Building on these contributions, we conclude that as data acquire new power, it is vital to explore the space for citizen agency in relation to data structures and to examine the practices of data work, as well as the people involved in these practices.

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Netflix on AWS

Netflix is one of the world’s leading entertainment services with over 260 million members in more than 190 countries. Netflix uses AWS for nearly all its computing and storage needs, including databases, analytics, recommendation engines, video transcoding, and more—hundreds of functions that in total use more than 100,000 server instances on AWS.

Customer Stories  | Architecture | Additional Resources

EXECUTIVE SUMMARY

Netflix entertains the world, providing a wide variety of TV series, films and games to hundreds of millions of members across the globe in over 30 languages. Netflix builds diversity, inclusion, equity, and a global outlook into everything it does, and by fostering a culture of courage, empathy, and curiosity, Netflix can move faster to develop new stories and better ways of sharing them with its members around the world. Netflix relies on AWS to help it innovate with speed and consistently deliver best-in-class entertainment. AWS provides Netflix with compute, storage, and infrastructure that allow the company to scale quickly, operate securely, and meet capacity needs anywhere in the world. Moreover, Netflix, a leading content producer, has used AWS to build a studio in the cloud. This virtual studio enables Netflix to engage top artistic talent, no matter the location, and Netflix artists and partners have the freedom to collaborate without technological or geographical barriers.

CUSTOMER STORIES

Powering creativity and collaboration with netflix on the aws fix this podcast.

Discover how Netflix uses AWS to build flexible, remote workstations to attract and retain creative talent that can now collaborate from virtually anywhere.

Netflix Empowers Remote Artistry with Low-Latency Workstations Using AWS Local Zones

Netflix is dedicated to facilitating collaboration among artists and content creators around the globe through remote workstations and offering a seamless content-creation experience closer to artists. Learn how Netflix achieved this goal by deploying its visual effects studio closer to artists using AWS Local Zones, helping it achieve single-digit millisecond latency and keep its remote workstations running smoothly.

Global Production ft. Netflix

In this keynote presentation at SIGGRAPH 2021, Laura Teclamariam, director of product and animation at Netflix, and Rahul Dani, director of studio engineering at Netflix, discuss how content production today is truly global and what that means for storytellers.  

The Courage of Innovation: A Conversation with Vernā Myers, Vice President of Inclusion Strategy at Netflix

Netflix is a company that believes in invention and reinvention. Netflix's vice president of inclusion strategy, Vernā Myers, shares some of the secrets behind Netflix's culture, addressing the importance of having the courage to break new ground, how the company approaches diversity, inclusion, and equity, and why curiosity is one of the most important traits to creating an inclusive culture.

With the new Amazon EC2 G5 instances, we can provision higher-end graphics workstations that offer up to 3x higher performance compared to workstations with EC2 G4dn instances. With G5 instances, content creators have the freedom to create more complex and realistic content for our viewers.” 

Stephen Kowalski Director of Digital Production Infrastructure Engineering, Netflix

Producing “The Crown" in The Cloud

Production of the fourth season of Netflix’s episodic drama “The Crown” faced unexpected challenges, as the world went into lockdown for the COVID-19 pandemic just as post-production VFX work was slated to begin. By adopting a cloud-based workflow on AWS, Netflix’s in-house VFX team of 10 artists was able to seamlessly complete more than 600 VFX shots for the season’s 10-episode run in just 8 months, all while working remotely. 

By adopting a cloud-based workflow on AWS, "The Crown” in-house VFX team was able to complete more than 600 VFX shots, working remotely, in just 8 months.

Netflix Uses NICE DCV on AWS to Build VFX Studio in the Cloud for Artists Globally

Netflix expanded into content production in 2012 and is now one of the world's leading studios. With a culture of continual innovation, the company wanted to build a visual effects (VFX) studio in the cloud to attract top VFX and animation artists worldwide and enable seamless collaboration between global teams. Using NICE DCV and Amazon EC2 G4 Instances, Netflix built remote workstations without having to choose between responsiveness and image quality. Learn how Netflix went from beta to launch in just 1 year, reducing technological and geographical barriers for artists while optimizing costs. 

How Netflix Thinks About Cybersecurity

AWS chief information security officer Steve Schmidt sits down with Jason Chan, vice president of information security at Netflix, to talk about security strategy, building a security program, Zero Trust, and cats as a unique threat model.

Like other home-entertainment services, Netflix has seen temporarily higher viewing and increased member growth during this unprecedented time. In order to meet this demand our control plane services needed to scale very quickly. This is where the value of AWS and our strong partnership became apparent, both in being able to meet capacity needs in compute, storage, as well as providing the necessary infrastructure, such as AWS Auto Scaling, which is deeply ingrained in Netflix’s operations model.”

Nils Pommerien Director, Cloud Infrastructure Engineering, Netflix

Netflix Connects with 100 Million Customers in 190 Countries Using Amazon Simple Email Service

Before migrating to Amazon Simple Email Service (Amazon SES), Netflix maintained an in-house solution for sending email. This solution carried its own operational overhead, including running dedicated servers with email-delivery software and optimizing email-send practices for each Internet Service Provider. Netflix needed an email solution that was flexible, affordable, highly scalable, and that had global reach. Learn how Netflix uses Amazon SES to overcome these challenges and the benefits the company realized by using the service.

Before we migrated to Amazon Simple Email Service, Netflix had to maintain an in-house solution for sending emails. This in-house solution carried its own operational overheads, including running dedicated servers with email delivery software and optimizing email sending practices for each Internet Service Provider. We evaluated several email delivery solutions and decided on Amazon SES because it is flexible, affordable, highly scalable, has global reach, and promises excellent deliverability.”

Devika Chawla Director, Messaging & Contact Engineering, Netflix

ARCHITECTURE

Netflix realizes multi-region resiliency on amazon route 53.

What happens when you need to move 89 million viewers to a different AWS region? Netflix's infrastructure, built on AWS, makes it possible to be extremely resilient, even when the company is running services in many AWS Regions simultaneously. In this episode of This is My Architecture, Coburn Watson, director of performance and reliability engineering at Netflix, walks through the company's DNS architecture—built on Amazon Route 53 and augmented with Netflix's Zuul—that allows the team to evacuate an entire region in less than 40 minutes.

Find out how AWS for M&E transforms media workloads with the most purpose-built capabilities of any cloud.

Learn more about AWS for Media & Entertainment »

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Use of Analytics by Netflix - Case Study

This case study was done as a part of my class assignment for Introduction of Analytics. It explains how Netflix uses Big Data and why is so successful. Why I chose Netflix Netflix: Stepping into Streaming CLV used in Netflix How Netflix uses Big Data and Analytics Latest Relevant News!! Conclusion Read less

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• 1. Netflix Analytical CRM Individual Project Table of contents: Why I chose Netflix Netflix: Stepping into Streaming CLV used in Netflix How Netflix uses Big Data and Analytics Latest Relevant News!! Conclusion Sources By Saket Toshniwal IÉSEG School of Management
• 2. Why I chose Netflix Netflix is an interesting company because it sits in an ever-changing ecosystem populated by old and new economy players. On one side, you have movie and TV studios that produce feature-length movies and serialized TV shows that are, in many ways, identical to the movies and TV shows that were produced when the medium was invented. On the other side, you have a rapidly-evolving set of computer-enabled devices and data transmission systems that allow consumers to access and stream the studios media content in virtually any location with a power source and a fast Wifi connection. As a distributor, Netflix has been forced to evolve with these changes, and changes in content consumption methods have had a major impact on the home entertainment ecosystem and the profitability and power of the players involved. “There are 33 million different versions of Netflix.” – Joris Evers, Director of Global Communications At current count, Netflix has 69.17 million worldwide streaming customers. Having this large user base allows Netflix to gather a tremendous amount of data. With this data, Netflix can make better decisions and ultimately make users happier with their service. Netflix has Individual Data of each of its customer that enables it to use the data in the most effective ways. Traditional television networks don’t have these kinds of privileges in their broadcasting. Ratings are just approximations, green-lighting a pilot is based on tradition and intuition. Netflix has the advantage, because being an internet company allows Netflix to know their customers well, not just have a “persona” or “idea” of what their average customer is like. Thus, Netflix is one of the best companies that use customer responsive intimacy and analytics to leverage itself as a leader in its industry.
• 4. Netflix: Stepping into Streaming Beginning in 2007 Netflix began rolling out its content to subscribers through a video-streaming offering. The innovation was completing the vision its founder had when creating the Company as illustrated in his famous quote: “Eventually in the very long term, it's unlikely that we'll be on plastic media. So, we've always known that, that's why we named the Company Netflix and not DVDs by Mail." This move to streaming was well regarded by subscribers, technology enthusiasts, and Wall Street analysts alike. The rise of Internet video streaming began. Netflix launched the Netflix prize, offering $1 million to the group that could come up with the best algorithm for predicting how its customers would rate a movie based on their previous ratings. The winning entry was finally announced in 2009 and although the algorithms are constantly revised and added to, the principles are still a key element of the recommendation engine. At first, analysts were limited by the lack of information they had on their customers – only four data points (customer ID, movie ID, rating and the date that the movie was watched) were available for analysis. As soon as streaming became the primary deliver method, many new data points on their customers became accessible. Data such as time of day that movies are watched, time spent selecting movies and how often playback was stopped (either by the user or due to network limitations) all became measurable. Effects that this had on viewers’ enjoyment (based on ratings given to movies) could be observed, and models built to predict the “perfect storm” situation of customers consistently being served with movies they will enjoy. Happy customers, after all, are far more likely to continue their subscriptions. This culture in the organization helped them to identify its customers, differentiate between them, interact with ‘best-suited’ offers for them and customize the enterprise behavior to be more customer-centric approach.
• 6. Use of CLV at Netflix Customer Lifetime Value has helped Netflix in multiple ways :  them what their individual customer is worth;  them to estimate the value of your company’s overall customer equity;  enable the company to divide customers into tangible segments, separating the most valuable and committed customers into different groups and distinguishing them from the less valuable but numerous others;  create opportunities to help marketing managers to refine marketing practices and ensure that the right approaches are being made to the right customers;  them to better predict how certain customers in certain situations might act going forward; and  retain & develop existing customers, acquire new ones and reactivate potential sleeping customers.
• 7. How Netflix uses Big Data and Analytics Big Data analytics is the fuel that fires the “recommendation engines” designed to serve this purpose. 1. Predicting viewing habits Central element to Netflix’s attempt to give us films we will enjoy is tagging. It pays people to watch movies and then tag them with elements that the movies contain. It will then suggest you watch other productions which were tagged similarly to those which you enjoyed. Netflix has effectively defined nearly 80,000 new “mirogenres” of movie based on our viewing habits! Netflix Tracks:  When you pause, rewind, or fast forward  What day you watch content (Netflix has found people watch TV shows during the week and movies during the weekend.)  The date and time you watch  Where you watch (zip code)  What device you use to watch (Do you like to use your tablet for TV shows and your Roku for movies? Do people access the Just for Kids feature more on their iPads, etc.?)  When you pause and leave content (and if you ever come back)  The ratings given (about 4 million per day)  Searches (about 3 million per day)  Browsing and scrolling behavior and a lot more Netflix uses this data to predict its customer patterns. This data mining technique helps in cross selling, upselling, responsive optimization, and a lot in the development phase of aCRM. 2. Finding the next smash-hit series More recently, Netflix has moved towards positioning itself as a content creator, not just a distribution method for movie studios and other networks. Its strategy here has also been firmly driven by its
• 8. data – which showed that its subscribers had a voracious appetite for content directed by David Fincher and starring Kevin Spacey. After outbidding networks including HBO and ABC for the rights to House of Cards, it was so confident that it fitted its predictive model for the “perfect TV show” that is bucked convention of producing a pilot, and immediately commissioned two seasons comprising of 26 episodes. The ultimate metric which Netflix hopes to improve in the number of hours that customers spend using its service. This data helps in meta- tagging to deliver better customer-centric content on Netflix. 3. Quality of experience To this end, the way that various factors affect the “quality of experience” is closely monitored and models are built to explore how this affects user behavior. Improving user experience by reducing lag when streaming content around the globe, this reduces costs for the ISPs – saving them from the cost of downloading the data from Netflix server before passing it on to the viewers at home. By collecting end-user data on how the physical location of the content affects the viewer’s experience, calculations about the placement of data can be made to ensure an optimal service to as many homes as possible. Data points such a delays due to buffering (rebuffer rate) and bitrate (which affects the picture quality – if you’re watching a film on Netflix that suddenly seems to switch from razor-
• 9. sharp HD to a blurry mess, you’ve experienced a bitrate drop) are collected to inform this analysis. Netflix has used Big Data and analytics to position itself as the clear leader of the pack. It has done this by taking on other distribution and production networks at their own game, and trumping them through innovative and constantly evolving use of data. Many managerial questions can be asked and answered with the use of descriptive, predictive, and prescriptive analysis. 4. Defining future plan of action Netflix collects customer insights from customers to improve its operational, analytical, and strategical CRM policies. Creative techniques are used with Analytical CRM to improve business performance. Netflix uses optimum marketing campaigns that impacts the individual customers the most. For example, it identifies which customers spend more time on television, ipads, mobile, desktop and other digital devices. This identification is done by the number of hours spend streaming through Netflix on different devices by each individual customers. Thereafter, it sends marketing campaigns to their customers that impact them the most with highest ROI for Netflix. Netflix has an 80 percent success rate (at the very minimum) with original programming, compared to the 30 to 40 percent success rate for networks. These shows have primarily been picked by running data mining and other algorithms against the vast user behavior data available to determine the size of the possible audience and thereby the likelihood of success.
• 10. Latest Relevant News This news is just released today. It shows how Netflix is using data from human behaviors to give a better customer experience. Netflix socks to pause show if user dozes-off News Released on : 10:53 pm on 17 Dec 2015,Thursday “Netflix has developed a new censor-fitted pair of socks which will pause the running show on Netflix if the user falls asleep, resulting in no leg movement. The socks would be fitted with an LED indicator too which will let the user know, in cases of false positive, that the current show will be paused.”
• 11. Conclusion Now you see how Netflix makes informed decisions based on data. Clearly, data cannot make every decision; there are some situations where intuition has to take over. For instance, data could not predict that a show like Breaking Bad would be a success. The creator was a former writer on The X-Files, and dramas are 50/50. In these cases, decisions are heavily based on the people and team behind the idea of the show. Whether Netflix can make a successful show like this (one with little to no data) is yet to be seen. What analytics and data can do is give you insight so you can run a better business and offer a superior product. People with data have an advantage over those who run on intuition or “what feels right.” Do you have data to help you make decisions? If not, Netflix provides a good case for why you should do so. Netflix Rocks! Analytics Rocks Harder!!
• 12. Sources https://blog.kissmetrics.com/how-netflix-uses-analytics/ https://getpocket.com/a/read/902639174 https://pr.netflix.com/WebClient/loginPageSalesNetWorksAction.do?c ontentGroupId=10477 Thank You for you patient reading. Saket Toshniwal IÉSEG School of Management MSc Digital Marketing and Customer Relationship Management 2015- 16 Date : 17th Dec 2015