The role of data tracking in Netflix’s creative success

Netflix was one of the first online streaming services and is by far the most popular, but is that all it deserves credit for? By tracking what visual and aural triggers you respond to, Netflix have taken content creation and promotion to the next level. Macquarie Uni actuarial student Abhishek Maran explores the role of data tracking in Netflix’s creative success.

Netflix was one of the first corporations to combine data with the entertainment industry.

Moving into the online streaming sphere in 2007, Netflix quickly realised the potential for the collection of large amounts of data by launching its USD$1M competition, the ‘Netflix Prize’.

The competition was aimed at creating the best algorithm to improve the accuracy of predictions about how much someone was going to enjoy a movie based on their prior preferences.

Since then, Netflix has evolved into a data mining behemoth, bringing you content that you can binge watch repeatedly without leaving the comfort of your bed.

As Netflix popularity and user base has expanded, the service’s predictive capabilities have improved significantly.

This has only been possible through the collection of a wider range of data points.

Data Set A

  • Times when you stop, pause, rewind or fast forward the content.
  • Days and times when you watch certain content such as rom-coms on Saturday night at 7pm, and Family Guy on Tuesdays at 10pm.
  • The specific dates you watch (e.g. what movies are popular on Valentines’ day)
  • Your location when you watch such as your home or at work.
  • What device you use to watch content. (e.g. TV for movies, Laptop for binge watching shows in bed)
  • At what points during the show you stop watching and move on. In addition, they also track whether you resume watching later.
  • What rating you assign a piece of content.
  • Your search history.

Whilst the above seem standard for companies of Netflix’s ilk, a few others are also known to be tracked:

Data Set B – where Netflix sets itself apart from competitors

  • How you browse and scroll through selections. I.e. Do you pause and read descriptions, or just skim through until you see a title/cover you like?
  • The types of trailers, promotional posters, words, colours and sounds you respond best to i.e. most likely to click on, and follow through.

 Data Set A can give you a tailored service, but Netflix has gone above and beyond with Data Set B, which focuses on creating a completely unique iteration of its service for each of its users.

The ‘perfect’ TV show

In 2013, the first ‘Netflix Original’ was released: House of Cards.

Produced by David Fincher and starring Kevin Spacey, the show was part of a meticulously orchestrated plan Netflix concocted as they aimed to release the ‘perfect’ TV Show.

From their various points of data, it became clear that their users preferred movies starring Kevin Spacey and movies produced by David Fincher who is also involved in ‘The Fight Club’ and ‘The Social Network’. So as the saying goes ‘por que no los dos’?

Soon enough, the series was released in typical binge watching fashion such that all the episodes were available at once. Currently the series has been renewed for a 5th season and is rated 84% on Rotten Tomatoes. However, the series’ success isn’t entirely reliant on Spacey or Fincher, but rather the promotional strategy created from studying Data Set B.

From analysing the gathered data, different versions of the poster containing different themes/colours/actors were created to appeal to a wider range of viewers.

Additionally, the distribution of the multiple preview trailer was based on people’s interests e.g. those who adored Spacey, were shown a trailer where he was the prominent figure.

Taking the industry to new heights

Fortunately for Netflix, ‘House of Cards’ was not a fluke as it has gone on to release other successful TV shows such as “Marvel’s Daredevil”, “Orange is the New Black”, “Stranger Things” and many more.

Moreover, as Dave Hastings, Netflix’s director of product analytics, and data science said at a Wharton Customer Analytics Initiative Conference in 2015 “You do not make a $100 million investment these days without an awful lot of analytics,”.  

Netflix have further forged the road of big budget productions having released 126 ‘original series’ and films in 2016 alone (far more than any other American Network), pumping $6 Billion into the industry.

Clearly, the collection and analysis of data is a key ingredient in the recipe to revolutionise the television industry and push it to new heights of originality and creativity.

Like all entertainment companies, Netflix wasn’t 100% successful. It experienced its fair share of poor ratings. But, unlike other production firms, Netflix is playing on another level by experimenting with data insights to produce new content. This begs the question; does data or creativity rule decision making? As Netflix continues to produce and release their Original films and shows, it is important to realise the effect creativity has on the success of a show. Going back to “House of Cards” it is evident that a lot of inspiration was derived from the original British version of the show. More importantly, the data gathered from people watching the British version was used to implement/retain (i.e. the audience retention times, and the pausing times of viewers) the popular features whilst using creativity to improve the other unpopular features.

On the other hand, the Netflix reboot of the popular series “Arrested Development” was received extremely poorly by existing fans due to its lack of loyalty to the original format of the show. Whilst Netflix may have had good intentions with rebooting the series, in this case it’s likely that the data created from binge watching users was grossly misinterpreted. However, such strategy of reviving dead TV shows does bring exposure from outside Netflix’s user base, hence generating money from them in the short run, but is this strategy the most profitable in the long run?

With more new shows and films to be commissioned, the expense of doing so will almost certainly increase on a yearly basis. Hence it is likely that Netflix may shift their focus from creating good pieces of content inspired by data towards one entirely dictated by data for the sake of protecting their bank balance. Whilst Big Data is inherently useful towards casting and setting creative guidelines, it must still be remembered that the flow and illogical nature of creativity must be present for a piece of content to be deemed excellent and human.

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