Netflix quickly grew into empire it has today, within 5 years it has grown from 26 million to over 100 million subscribers (source). With a simple monthly subscription based user model, they do not rely on advertising income, providing viewers solely with what they want to see whenever they want to see it. Especially, as their app is compatible with many screens beyond television. All that is needed is an internet connection. With supply side economies of scales, they are able to provide to an almost unlimited customer base with marginal costs close to zero.
Their most important asset however has not yet been mentioned. Data. Like many other internet companies, they are able to track every single move viewers make. Not only does this allow them to use it in a similar way as Amazon does by creating recommendation based on complicated algorithms and big data analytics. It also allows them to formulate predictions of which shows will be successful based on aggregated data from the behaviour of over 100 million viewers. Not only do they track what genre those viewers watch, but also what actors, colours, when pause was pressed and the device on which is watched to name a few. This information also provides critical insights when deciding on new titles, especially s they do not focus on the long tail. When deciding on House of Cards, they made many comparisons with Macbeth based on quantifiable data to create predictions and increase the likelihood their viewers would like it. With all this knowledge, Netflix is one of the most interesting and innovative companies in the film-industry. Finally, to provide an insight in the secret of Netflix, Jeff Magnusson, serving as data platform architecture manager at the company, provides three key point of Netflix’s data philosophy used during all their analysis:
1. “Data should be accessible, easy to discover, and easy to process for everyone.”
2. “Whether your dataset is large or small, being able to visualize it makes it easier to explain.”
3. “The longer you take to find the data, the less valuable it becomes.”
Sources:
https://www.fool.com/investing/general/2015/09/30/how-netflix-inc-really-creates-value.aspx
Hi Kim!
That is a really interesting article you wrote! I believe this is just the start of Netflix, and with the proper data analytics it could eventually beat all video streaming services. Especially when you think of the resonance marketing concept; developing products that produce favourable responses among targeted customer segments. I think this concept is key in the future of Netflix and its corresponding data analytics. With the adequate data analytics Netflix will be able to even further develop the desired favourable responses, causing even more media attention (and thus probably attract more viewers). I really wonder how Netflix will develop itself in future years, but I can definitely say that I am really hooked on Netflix!
Hi Kim,
It is interesting to see you describe a real-life example of a company that exploits the potential of data analytics. I believe Netflix also applies its strength in analysing data very well in predicting customer preferences. To elaborate, Netflix uses smart technology to classify its movies and TV series into distinctive categories, and based on a customer’s watch history, it provides the watcher with relevant suggestions of similar content he/she might be interested in. I think this is a strong strategy to help customers explore new items in the long tail which truly suit their needs. Therfore, I am highly interested to see what Netflix’ analytical skills and algorithms can bring in the future.
Source: http://www.businessinsider.com/how-the-netflix-recommendation-algorithm-works-2016-2?international=true&r=US&IR=T
Data is key to make suggestions – no questions about that. And without doubt, suggestions are one of the main reason that keep users on Netflix’s platform and makes viewers continue to binge watch one series after the next. However, this is only one part of why viewers return to Netflix and don’t switch to TV or any other channel: They cannot. Some of the most popular content for which consumer return and return over again is simply shown exclusively on Netflix. Some analysts even estimate self-produced content to make up as much as 20% of its total content until the end of 2017. (Derrick, 2017) Netflix produces these series themselves and do not sell them to any broadcaster. This not only makes them competitive (i.e. the series are only available at Netflix) but crucially it also limits the until now very high bargaining power of movie studios. They would traditionally supply the content and then demand a large chunk of Netflix’s revenue as licence fee. Until now they were the gatekeepers and in my eyes one of the main reasons why streaming platforms only emerged so late despite the technical possibilities. By producing its own exclusive content, Netflix sidesteps the suppliers, creates a distinguishing feature that set it apart from its competitors and makes customer return.
Derrick, J. (28 April 2017). 20% Of Netflix Streaming Content Could Be Self-Produced By Year-End. Retrieved October 21, 2017, from https://www.benzinga.com/analyst-ratings/analyst-color/17/04/9365980/20-of-netflix-streaming-content-could-be-self-produced-b
To me, the most interesting part of this whole development is Netflix’ rationale for introducing the algorithms and recommendations. For the Long Tail phenomenon, the other side of the coin is the “rabbit hole problem” where users have access to so much content, they get lost. The recommendations simply serve to help users navigate among the incredible amount of options, and increase satisfaction as the service offers a better match.
Fun fact: Did you know that Netflix only has 90 seconds, on average, to convince the user that the content it provides is suitable, before they start doing something else? Apparently, (fast) personalization is really important to make people come back for more.
If you want to know more about the reasoning behind the algorithms, the source below might be of use:
http://www.businessinsider.com/how-the-netflix-recommendation-algorithm-works-2016-2?international=true&r=US&IR=T