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How streaming analytics is helping companies decide which shows to produce

streaming analytics

Streaming platforms are all the rage these days with every major media company now offering their streaming services for their audience.

Netflix used to be the only major player in the streaming industry a few years ago, but newcomers like Hulu, Apple TV+ and Disney Plus have now started to break the monopoly of Netflix.

To understand how much the new streaming services have grown, one need only look at Disney Plus. The service has seen a meteoric rise in viewership and subscriber base, which is very impressive considering Disney only started its streaming service in 2019.

Its impact has been such that it now looks like every single show Disney is offering on the streaming platform will be successful.

The influx and the success of streaming services on the market, though, is the result of changing audience preferences and the increased accessibility to the internet, which is likewise fueled by the widespread use of smartphones.

The streaming industry is even eating away at the market share of theatres. All this boils down to the quality of content provided on these streaming platforms. 

One tool that is helping creators create quality content is data analytics, more specifically, streaming analytics.

In this blog post, we will explore how streaming analytics is helping these platforms produce successful TV shows, movies, and documentaries.

Streaming platforms are using viewing statistics to improve their existing offerings

Streaming platforms have multiple flagship shows which they bank on to attract new viewers. Stranger Things, Sherlock Holmes and Peaky Blinders are such shows, and each has millions of fans. There is a good reason for this phenomenon.

These shows ran for multiple seasons and got progressively better with every season, which was the only reason fans kept tuning in.

Sherlock Holmes, for example, was picked up by Netflix from BBC because decision-makers at the streaming giant figured out that there is a demand for clever shows like it. Netflix not only bought the show but also increased the production quality. A factor that contributed to creating higher customer engagement.

Data analytics played a huge role in Netflix picking up the show. If streaming analytics hadn’t revealed the potential of the show, the streaming platform would not have picked it up and the show would have stayed a small-time production on the BBC. The same is true for Peaky Blinders.

The point here is that streaming analytics is helping these companies validate their decisions and improve the production quality of their existing shows.

Data analytics is helping produce new shows

Annually, streaming services spend billions of dollars on content creation. The financial stakes are high, which is why decision-makers at these organisations use streaming analytics to ensure they make the correct production decisions and avoid investing financial resources on potentially unsuccessful productions.

Netflix, for example, has realised there is demand for shows that are grounded in reality or shows that are based on real events. In response to these findings, they have allocated millions to making movies and TV shows in this genre.

An example of this kind of decision making is Netflix’s decision to make a docuseries about the Gamestop and Wall Street saga.

Disney Plus too is making more Marvel TV shows, because analytics have revealed that there is demand for long-form superhero entertainment. Initially, Marvel only planned on making TV shows on Disney Plus. This has now been increased due to positive reception by fans for shows like Wandavision, and The Falcon and The Winter Soldier.

The bottom line here is that streaming companies use streaming analytics to make creative decisions on how they produce entertainment.

Streaming analytics is helping streaming platforms produce higher quality content

It seems like ever since Netflix became a phenomenon, more and more companies are trying their hand at streaming.

Most of these streaming services are using data analytics to make decisions on what content to produce, axe or improve.

As for us, the audience, streaming analytics is giving us quality entertainment to binge on.

How Netflix used big data and analytics to generate billions

Learn how Netflix became the most valued media company in the world, surpassing the likes of Disney, by leveraging big data and analytics.

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.

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