Netflix – Big data and new technologies

It is quite impressive to think that Netflix, a company nowadays worth 19 billions in total assets, wouldn’t be even conceivable only 15-20 years ago.
The proof of this is that Netflix itself appeared on the market in 1997 as a DVD rental and sales company (though, almost immediately, started focusing exclusively into the DVD rental by mail business).
It was not until 2007 that it started providing media streaming as a product, and didn’t produce any series before 2012.

What made possible to create a business (in its actual form), able not only to wipe out a company like Blockbuster from the market, but as well to compete with TV channels and movie productions as a content creator?

Of course, the maturity of web technologies (in the form of interactive and quick responding websites) and telecommunications (in the form of speedy internet connections) were absolutely necessary to achieve some success, but do not explain (nor probably would be sufficient to reach) THIS kind of success.

The decisive factor to see Netflix operating at the level it does today, with countless series parts of the popular culture (House of cards, Stranger Things, Orange is the new black – just to name few), with a brand recognition of 65% (and a stable position among the top 100) in the US market and an ubiquous presence around the planet, was the ability to translate the massive amount of data available about their users’ behaviour and preferences into a better offer and a better user experience (and also, continously improved)

In other words, Netflix is a prime example of data-driven company making use of big data.

The predictive algorithm used by Netflix to suggest users the next content to watch (partly based on “association rules”, for example) is quite important (it gets to suggest about 80% of the content to viewers), but it’s only a part of many multi-faceted processes, and it depends on the data and metadata it is fed with, which brings us the the next two aspects.

First and foremost, a big part of the data used to select the content (type of series/movies offered),¬† and the general form of the offer (interface, technical specs) comes from simply analyzing customers’ behaviour (gathered in a somehow “passive” way).

Beside the above though, there is quite a bit going on within Netflix itself, in order to create data (or maybe, more appropriately “metadata”), as internal “taggers” are in charge to watch every minute of the series, marking with precision the actual content of each series (genre, presence of an ensemble cast, main themes and much more) allowing capture the actual nature of the content in the most possibly nuanced fashion.
This side of the process can somehow seen as a more “active” way of generate useful data, by Netflix, and it is just as fundamental.

But, how does all that translate in an actual better, more succesful product, able to improve customer retention and to obtain better revenues, by offering something that the average customer is more keen to pay for or to keep paying for?

Let’s see some practical examples.

A first, high-level example is the concept of “micro-genre“, (something largely created by Netflix itself) which is simply the result of machine-learning processes creating “buckets” of shows, by¬†discovering some commonalities among them and their viewers.
Put simply, the algorithms working behind the scenes for Netflix created a countless number of micro-categories, allowing Netflix to do 2 things:
– to tailor the offer of existing shows to users
– to produce content that is very likely to be succesful among at least some demography of their audience.

Another example is the interface (cyclically reviewed) which is optimized to maximise the success rate of the series prompted.

Netflix interface 2014

Netflix interface 2018

We can see above how the interface changed in the last 4 years.
It’s fair to assume that data suggested a less dispersive visualization in the menu (fewer shows prompted at first), better interactivity (shows are now easily browsable, horizontally, by category), a more cinematic layout (darker tones, one single color throughout the webpage), and a better focus on the show selected (bigger prompt, more visible description and image, rating visible at a glance).

A third example is something that many (or all) Netflix users might have noticed, especially at the beginning of their experience as customers.
It is not uncommon that when you launch a series/movie, the first few seconds are lower-quality in terms of image, but they quickly adapt to then offer a stable and high-quality image throughout the show vision.
This is another decision relying on data analysis. Netflix noticed 2 things:
users would switch off within very few seconds, if the show doesn’t start streaming (hence, offering a lower quality at first ensures that this doesn’t happen, improving retention and user experience)
– it allows to optimize the streaming to ensure there is no buffering for the duration of the show (another factor that users could find extremely irritating and could lead to drop the vision, or even Netflix services in general)

Deciding (in particular) to trade lower quality for better response times was a choice deriving from looking at customers’ behaviour data.

A last example is how Netflix can (thanks to the use of big data) micro-target advertisement depending on the precise demography the latter is aimed at (something that a TV channel cannot do, with such precision).
For example, for the first season of House of Cards it created 10 different trailers, each aimed at a specific segment of viewers, to maximise the potential interest of customers, when launching the series.

It should be clear by now how Netflix is a textbook example of how data analytics, big data and data driven decision should be run.

It is surely not easy task to create the infrastructure, the internal know-how and the culture to make the best use of big data, but it is a fact that, when done properly, such an approach allows to optmize resources, offering at the same time the best possible product to customers, ensuring success and growth of a business under any perspective.

Leave a Reply

Your email address will not be published. Required fields are marked *