Attention is a scarce resource in our information-rich digital world. Content is abundant and supply exceeds demand by far. Music streaming-services are providing databases with more than 30 million songs in stock. The on-demand streaming-service Netflix has more than 10 000 pieces of documentary, movies or series available. At first glance every single wish can be fulfilled due to overall availability of content. But then again, the hardest part lays in choosing the right movie that matches your mood at the very moment of browsing through the large database.
The solution to avoid a paradox of choice leads to an algorithm which predicts your future actions. The more information a provider such as Netflix receives about your favourite genres, actors, directors and watching behaviour, the better the algorithm gets into shape. It compares your track record with that of other users who are sharing similar tastes. Thus it creates and optimizes a personal dashboard for every single user which includes customized recommendations.
A big change is occurring in the Creative Industries nowadays. Music, movies or books have been produced on the premise of intuition and experience. Decision-makers never knew if the produced content will make it or not. Just take the music industry in the past, for example. Eight out of ten artists didn’t make any money for a record company. One reached the break-even point. Only one artist skyrocketed and cross-subsidized the rest.
Big Data is changing the setting. Predictive analytics are now becoming the state-of-the-art tool in calculating the next big hit by analysing customer data and behaviour. Netflix was one of the first movers in harnessing user insights to create successful content. The far-ranging impact of “House of Cards” speaks for itself. In 2012/2013 the political drama series was the most streamed content in more than 40 countries and won three Emmys plus two Golden Globes.
Netflix had more than 30 million users when “House of Cards” was drafted. The company crawled through its customer database and found three intersections of interests:

  1. Director David Fincher (“The Social Network”) had a loyal fan base that followed his filmography. Plus, his movies have rarely been interrupted while watching. This insight is of utmost importance when you want to hire a director who is outstanding in sustaining tension.
  2. Actor Kevin Spacey (“American Beauty”) was one of the most popular actors on Netflix.
  3. The BBC mini-series “House of Cards” from 1990 was also popular amongst people who liked the productions directed by Fincher and movies starring Kevin Spacey.

Based on these facts, Netflix decided to produce content instead of being ‘just’ a service-provider. “House of Cards” was a (calculated) lucky punch for Netflix. It boosted brand image and loaded it up with a new kind of quality content that gained acclaim amongst critics and the audience likewise.
The approach underlines Netflix’ approach of being a highly user-oriented company. Be remembered: from the very beginning of the streaming days onwards every Netflix-user has been able to watch content independently whenever and wherever wanted. In recent times, Netflix commissioned other productions like “Daredevil” or “The Unbreakable Kimmy Schmidt” that stood the test of time amongst their subscribers. Of course these series were influenced by insights and watching behaviour.

Big Data as a support to create content bottom-up by looking at customer preference

Big Data as a support to create content bottom-up by looking at customer preference

By now, Netflix is a big player in Big Data business. 37 per cent of the U.S. internet-traffic is Netflix-related. More than 62 million people worldwide do have a regular monthly subscription – let alone 42 million people in the US, where more and more people are turning their backs on traditional TV.
Netflix provokes a revolution in the movie industry. Customized watching and Big Data productions are just the beginning. By the end of August, Netflix is going to hit the cinemas with its first movie production “Crouching Tiger, Hidden Dragon II: The Green Legend.” For the first time in history a movie is going to be released in cinemas and on-demand at the same time. This tackles the traditional process of content exploitation. Traditionally, cinemas hold exclusive rights in being the first venue to showing movies. After 90 days, content will be released step by step via DVD, Pay-TV, Streaming and Free-TV. There is no such thing like a value chain that Netflix urgently wants to disrupt.
This brings us to the question of power: Are data-driven decision-making companies able to turn industry mechanisms upside down by using bottom-up crawling techniques instead of making decisions top-down?
The Netflix case is surely not reserved to Creative Industries. Predictive analytics for designing new products have been applied cross-industry-wide. It seems that data-paradigm is spilling over.

Erich Renz

Posted by Erich Renz

Ever since he took up his studies of the Creative Industries in Mannheim (GER), Erich has been devoting himself to the impact of arts, media and design on both society and economy. His pursuit of research brought Erich to Brisbane (AU) and Amsterdam (NED). At Queensland University of Technology he assessed Indonesia’s government-led creative-transformation. Furthermore, he published a paper on creative city making at the University of Applied Sciences in Amsterdam in which he examined the influence of creative and knowledge-workers on Amsterdam’s thriving “Knowledge Mile”. In doing so, he developed a toolkit for analysing business models of enterprises based solely on information available on their respective online presence. Meanwhile Erich has returned to Munich (GER) to finish his Master’s thesis on data-driven product and business model development in the Creative Industries.

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