Net flight Economics: digital transformation, AI application and subscription economy logic


Net flight Economics: digital transformation, AI application and subscription economy logic

Original title: Enlightenment from "net flying economics"

 

Wu Chen

If you want to use a word to describe the American Internet giant enterprise, it must be FAANG. Some people call it "Big Fang Tooth Gang" (FANG means poisonous teeth in English). Facebook, Amazon, Apple and Google are all familiar with the Big Fangs. Netflix, by contrast, is not so impressive. It's also the youngest brothers in the Big Fangs. However, Netflix, an online video streaming media company, is a good success story if it is to be evaluated from a series of dimensions such as digital transformation, business model innovation, artificial intelligence applications and the subversion of traditional business.

By the third quarter of 2018, Netflix had 137 million subscribers, one third of them in the United States. According to the average monthly subscription fee of $10 per month, the annual income of these users will reach US $16 billion 400 million. Similarly, Netflix plans to invest $12 billion to $13 billion in exclusive TV content this year, transforming itself from a traditional video content channel into a streaming media platform that integrates the entire process of original program production and distribution. The intensity of net money spending is comparable to that of Amazon.

Why is net flying willing to devote all its income to the production of original content? Why can Internet fly put all his income into creative content creation so much? What kind of new business mode does it represent? Some people have used the economics of net to describe this business mode. If summarized in one sentence, "Netflix Economics" is a business model that can fully mobilize the internal digital assets of large enterprises, using artificial intelligence to establish direct and exclusive links with paying customers for precise marketing. Amazon and Netflix may have many similarities among the big fangs, but Netflix ranks first in terms of industry focus.

The card house is AI.

Victory with user insight

To understand the economics of net flying, we must start with the "card house".

Starting in 2013, "House of Cards" is the first original series invested by Netflix, but it is also the beginning of the subversion of the traditional American television industry. "House of Cards" is famous not only for its popularity among American TV fans, but also for its many first movies.

It was the first TV play that was released in the 13 episode of the season. Prior to that, whether it was free of charge for the three major American TV companies or paid cable stations such as HBO, it was normal for a season of TV series to broadcast one episode a week. Net flying does not mean playing cards, but lets the audience shout. There are people watching the drama all night.

It's also the first show to be filmed by Netflix's high-level shooting board without making any samples, and is scheduled for two seasons at a time, with an investment of up to $100 million in the first quarter. You know, the usual practice is that the producer watches one or two episodes of the sample film to decide whether to reserve, and in the course of the season broadcast, according to the ratings to decide whether to cut or renew. The practice of net flight has further overturned the whole business mode of traditional TV.

Traditional TV, whether it's a free radio network or a paid cable TV, ultimately requires consumers to search for the programs they want, using audience ratings collected by companies like Nielsen as a benchmark for the quality of the programs. In the case of traditional television in the United States, whether ABC, one of the three major radio networks, or HBO, which is famous for original movies and dramas, face the same problem: Before launching new programs, the company's top executives have no way to judge whether the audience's ratings for a particular program are high or low. So, the traditional television industry in the United States spends an average of about $400 million a year, sifting out about 100 of the five or six hundred scripts for the creative team to produce a pilot film, and only a third of the samples will be approved and funded for the first season. Immediately after the show was released, it entered a ratings championship, and the lower-ranking series were quickly cut off. Usually only after the first season ends, only 12 or 3 episodes can be renewed for the second quarter.

Netflix is able to do this because it has long recognized the importance of accumulating and managing digital assets, especially for a company that is directly oriented to tens of millions of users. When it was a traditional enterprise that rented DVDs by mail more than a decade ago, it was clear that understanding users'preferences and choices was its most important asset. In the era of mailing DVD, they began to accumulate user preferences and big data, allowing each subscriber to score movies while sending back DVD; in the era of streaming media, as an online video platform, it strengthened the collection of user behavior data. Knowing the choices of millions of subscribers, Netflix had a good understanding of users'preferences before starting to shoot "House of Cards", and laid a good foundation for its construction and continuous improvement of recommendation algorithms.

Five years ago, the net flight algorithm was very simple. After analyzing the viewing habits of a large number of users, it is found that many users like the original BBC series "House of Cards" (the American version of "House of Cards" is a remake of the BBC series). Many users watch the series over and over again, and can not stop at a glance. These acts seem to be the direct expression of users'preferences in Netflix. On the other hand, Netflix found that audiences who like to watch the BBC version of "House of Cards" also like to watch movies starring actor Kevin Spacey, and they are also very cold to the director of "Social Network" David Finch.

With big data and algorithms to sort out audiences'approved scripts, actors and directors, Netflix executives believe that the drama packaged together is worth a bet, and it is a gamble.

Obviously, Netflix's gambling has not only achieved commercial success, but also shocked critics artistically. Because of the House of Cards, Netflix won several Emmy nominations in just six months, and eventually won the Emmy and Golden Globe awards. By contrast, HBO waited 25 years before waiting for the first Emmy nomination.

Ted Salandos, chief content officer of Netflix, once said, "Our goal is to become HBO quickly, not make it so easy for HBO to catch up with us." This sentence has been verified again and again after net flying decides to start the "card house".

Artificial intelligence has pushed "recommendation" to the front desk.

The "card house" subversion of the television industry stems from the use of big data. Five years ago, Netflix's viewing habits of 25 million subscribers were used to judge the audience's preferences, and based on this, to create the audience's favorite dramas. Five years later, it has made great progress in the application of big data and artificial intelligence.

First of all, the data collected by net flight are very detailed. Every day, it collects the behavior data of tens of millions of users. Each contact point includes the time, place, time and device for viewing the video. Every user's behavior of viewing the video will be clearly marked, such as when to pause, fast forward or playback. Of course, it also includes everyone's rating and collection of the drama. History and comments and comments on social media.

Big data experts believe that good big data needs both width and depth, that is, "Big N" and "Big D". The former refers to the large number of samples of data, while the latter emphasizes that the granularity of each data point should be very fine. The big data accumulated by net fly coincided with both.

Second, the level of artificial intelligence recommendation developed by net flight has also gone up several steps. Early rough algorithm recommendation mainly predicts users'future preferences based on users' past usage information. If you search for a washing machine online, the advertisement of the washing machine always pops up on your computer screen like a shadow. If you click on an article about history, you will be constantly recommended. History related articles. Netflix's algorithm is more intelligent because it not only has a more careful observation and analysis of each user's video viewing history, but also has a better understanding of individual users'preferences. At the same time, its accumulated video viewing history of hundreds of millions of users helps to better classify users and find interrelated features. Together, these two insights help Netflix better recommend videos that each user may be interested in.

Smart recommendation has changed Netflix and the business logic of the entire television industry: from relying on each user's own search to personalized recommendation for each user.

Recommendation is more efficient than search, because the individual's vision and experience are limited, but recommendation can be based on the choice of millions of people, digging out content that you never thought of but touched you.

Recommendation also allows minority TV dramas to have a market, so long as they can accurately find the crowd who loves it. With in-depth user insight, Netflix divides hundreds of millions of users into about 2000 groups according to their tastes and interests, with different recommendations for each group. Netflix filmed a season of soap opera last year that critics thought was very watery, but it was loved by young boys and girls, which is an example. Netflix has also begun to tap into sequels of some hilarious but non-hilarious dramas, because it has the ability to find accurate users than ordinary TV stations, without worrying about the audience ratings.

This insight into users also enables Netflix to more accurately determine how much an original series should spend on investment, and to calculate reasonable purchase costs by analyzing the coverage, attraction and retention of a particular series to specific user groups. In addition, because recommendation is more intelligent, Netflix seldom leads users to watch programs that their people don't like, so few people lose trust in Netflix because they see programs they hate.

Subscriber economic logic in the era of digital economy

Netflix has brought much more changes than that. It is also a typical example of the digital economy in which enterprises embrace subscribers'economic logic.

A simple definition of subscriber economic logic is that a well-run business can lock in a 70% revenue business model every year in the first year, because at least 70% of subscribers will continue to pay for their services in the new year. Subscriber economic logic is not a new concept. Newspapers and magazines began to rely on subscribers'income more than a hundred years ago. HBO, a cable movie channel without advertisements at all, receives most of its revenue from subscribers. Understanding and understanding subscribers, locking subscribers, and enhancing subscriber stickiness have become the most important relationship of subscriber economic and business model, which we all know.

The change brought about by Netflix is the application of big data and artificial intelligence to the establishment of strong links with paying users. Its success also represents the strong attraction of subscriber economic logic in the digital economy era.

Subscriber's economic logic requires enterprises to change their business model from selling goods to providing services. Netflix is a good example. Netflix no longer focuses on how many movies can be sold to users, or how many movies users will watch. Its focus has become: What programs do users need? What can we do to satisfy users' needs in a long term and sustainable way?

Subscribing to economic logic has pushed many business models to change.

For example, net flight is not.


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