Shamelessly, the latest hit of Netflix movies, is a compendium of popular topics that dominate conversations on social networks. From feminism to romantic love, the film has all the ingredients to become an audience success. But it is not the first Netflix production that seems to fit the idea of a product built exclusively to please preferences.
Last year’s ratings phenomenon, Alerta Roja also immediately became the most watched show on the platform. With a combination of the most popular stars on the Hollywood scene and a basic story, the Netflix movie immediately caught on with the audience. And with its 193 million subscribers worldwide, it’s quite a milestone. Netflix, converted into the universal big screen television, seems to have found the ideal formula to create instant hits.
But it is not random. In reality, Netflix has all the means to create manufactured phenomena or at least analyze the preferences of its audience for its benefit. The platform appears to have refined its ability to translate data from its algorithm into beautiful content to a surprising degree. From the rise of Korean horror and science fiction productions, to performances by popular casts. For Netflix, the method of success comes directly from the public’s multiscreens.
Netflix’s game to please the audience
Recently, Hwang Dong-hyuk, creator of the Squid Game, commented that for ten years he tried to sell his series to channels and platforms. But that only during the complicated period of the pandemic had he found receptivity for his proposal. The funny thing is that what might seem like a market analysis point could actually be more than that. In reality, during the first and complicated months of isolation and quarantine, a good part of the audience turned to streaming. And that caused some curious phenomena.
Contagion, 2011
To begin with, Contagion (2011) by Steven Soderbergh became the most viewed content on various platforms during the first months of the pandemic. It was an unusual reaction, which in one way or another reflected the great global conversation about the surprising and universal health emergency. But what seemed inevitable, and also part of predictable pop culture reactions, became data. Much of the material that platforms can use to their advantage comes from usage data. And during the first weeks of forty and become the main option of the public, Netflix had enough material to analyze.
From game to test
The next thing that happened is that Netflix turned the data into a series of Netflix shows, documentaries and movies, created to answer the big questions of its audience. Or at least, those that were reflected in the searches and the hours of transmission. Throughout 2020 and 2021, Netflix content was brimming with studies on contagion, medicine, and rapid response to the pandemic. Also from series and movies related to captivity and the apocalypse.
The Squid Game | Netflix
But in addition, Netflix also took into account the fact that the pandemic had brought the conversation about social inequality to social networks. Like a chain reaction, much of the new content delved into the topic. And for 2021, the most watched show, the hit of the season was a series that seemed to tie each of the themes together. The Squid Game, with its air of gore and harsh social commentary, became the most watched series on the platform. Chance? Not so when you look at the subscription service’s methods of success.
The art of predicting success
Of course, Netflix is not the only or the first service to use an algorithmic formula to analyze data and predict audience tastes. Also, Spotify does it in the musical section, which allows it to develop a precise rating and recommendation system. But on Netflix, the structure has been made specific. The greater the amount of time of use, the more data on movies and favorite series, the discarded and hours of consumption you will obtain. The seemingly harmless recommendation list that invites the selection of similar material is only the tip of the iceberg of a more complex system.
The data also complement each other. Netflix collects the interactions of its users with its platform. The data ranges from the content itself, to the devices, places and hours of use. The combination allows building an exact profile on the user’s preferences. Which in addition can even add the way in which the subscriber consumes the content.
The platform records the search by adding specific dates and time periods. This allows us to understand what the user wants, when and depending on what circumstance. The Netflix algorithm allows you to analyze what you want to see and deduce what you will want to see in the future. But what appears to be limited to on-screen recommendations also allows the channel to choose its future content. So far, the idea that Netflix could create content based on specific user preferences seems surprising and even a bit far-fetched.
But if we analyze the way in which even movies without great relevance become immediate successes, it allows us to analyze the phenomenon from another point of view. Netflix’s new content — the productions you’ll invest in, what you’ll use in the future — becomes a prediction based on viewership.
Netflix and big data
The branch of artificial intelligence called Machine Learning allows you to create machine learning models based on specific data. Netflix certainly uses the method to emphasize the most successful content. In particular, in how the audience interacts with the platform and the way preferences are modified. These patterns, which also encompass all the information that Netflix has at their disposal, explain the platform’s string of successes.
It is clearly not a completely automated or infallible method. Netflix uses from probabilistic graph models or reinforcement learning algorithms. Each method makes Netflix a personalized experience. But also, that its content is less and less independent and more related to the taste of the public. For every movie that Netflix produces to benefit from the big data at its disposal, it loses the ability to diversify its content.
