This article is written by contributor Mario Gavira, Managing Director at liligo.com
What if I tell you that Netflix is NOT in the business of media entertainment? Yes, keep reading.
Netflix not only has the largest worldwide subscriber base of any business but has also managed to grow it by +25% last year. Its market capitalisation competes head-to-head with Disney, the most-valued entertainment company in the world.x`
Netflix’s success story, however, cannot be explained without understanding their granular knowledge of their subscriber base and their AI-driven focus on personalisation. Netflix not only looks at millions of ratings, searches and “plays” a day, but the entire viewing history of billions of hours of content streamed per month. It took them six years to collect enough viewer data to engineer a show that became a worldwide success: House of Cards. Since then, Netflix has increasingly used this formula for content creation, achieving success rates of 80% compared to 30%-40% by traditional TV shows.
Let me pull back the curtain for you on some of Netflix’s key AI and data insights.
Make the experience personal by testing, testing and testing
Back in 2013, Netflix claimed that there are 33 million different versions of Netflix. At that time, the company had 33 million subscribers.
Or as Ted Sarandos, Netflix’s Chief Content Officer puts it:
There’s no such thing as a ‘Netflix show’. Our brand is all about personalisation.
Netflix algorithmically adapts the entire user experience to each individual subscriber, including the rows selected for the homepage, the titles selected for those rows, the visuals for each movie, the recommendations of other movies etc.
This ongoing personalisation process is driven by what Netflix defines as “consumer science”.
Ignore traditional segmentation criteria
Traditional TV networks use standard demographic ratings such as age, race or location for their market segmentation. Netflix instead tracked viewing habits of its subscriber base from the beginning and created almost 2000 clusters of so-called “taste communities”.
These segments are not seen as static silos:
“Most people are usually members of a few different communities. We’re complex beings, we’re in different moods at different times.”
The power of recommendations
Netflix’s Senior Data Scientist, Mohammad Sabah stated in 2014:
“75 per cent of users select movies based on the company’s recommendations, and Netflix wants to make that number even higher.”
These recommendations are powered by algorithms that are based on the assumption that similar viewing patterns represent similar user tastes.
The taste communities play an instrumental role in these recommendation algorithms.
We didn’t come out of the gate and say, ‘We think Black Mirror is for this audience or not for that audience’. But after we launched the show, we were able to see the patterns. The chart showed how folks who liked Black Mirror were also fans of Lost and Groundhog Day. On the surface, if you thought about Groundhog Day with Black Mirror, you might not find an obvious similarity.”
But the recommendation algorithms go beyond the “taste” criterion. Netflix also includes contextual criterion to find the perfect recommendation for each user in each moment.
We have data that suggests that there the viewing behaviour differs depending on the day of the week, the time of day, the device, and sometimes even the location.
Most internet companies use batch processing for personalisation use cases such as recommendations, but Netflix realised that this was not quick enough for time-sensitive scenarios such as new title launch campaigns or strong trending popularity cases. They moved to a near real-time (NRT) recommendation process to accelerate the learning process and roll out test results.
A picture is worth more than a thousand words
Netflix sets themselves apart from traditional media companies not only by what they recommend but how they recommend it to their members. A key feature is an image they use to promote each movie or TV show, or the so-called artworks.
Netflix aims to provide the artwork for each show that highlights the specific visual clue that is relevant for each individual member. For each new title, different images are randomly assigned to different subscribers, using the taste communities as an initial guideline. This translates into hundreds of millions of personalised images continuously being tested among its subscriber base.
For the creation of the artwork, machine learning also plays a critical role; thanks to a computer vision algorithm that scans the shows and picks the best images that will be tested among the taste communities.
Go beyond standard industry metrics
Netflix does not limit the success or failure of a show to the size of its audience. Shows with a smaller audience but low production costs can also remain profitable.
John Ciancutti, former VP of Product Engineering summarised the key criteria for content selection as follows:
Netflix seeks the most efficient content. Efficient here means content that will achieve the maximum happiness per dollar spent. There are various complicated metrics used, but what they are intended to measure is happiness among Netflix members.
Does Netflix entirely rely on machine decisions across the organisation?
Netflix´s wealth of data and sophisticated algorithms may lead to think that decisions such as investing or not in a new show is purely driven by machines.
It is not.
Viewing habits combined with smart algorithms are used for predicting consumer behaviour.
“We have projection models that help us understand, for a given idea or area, how large we think an audience size might be, given certain attributes about it.”
But their various projection models and cost analyses don’t dictate their decisions.
“You have to be very cautious not to get caught in the math because you’ll end up making the same thing over and over again. And the data just tells you what happened in the past. It doesn’t tell you anything that will happen in the future.”
Even in a data-obsessed company like Netflix, humans are still in command of key investment decisions and data; and smart algorithms only support the final decision-making process.
We are witnessing the transformation of the media industry with the rise of technology giants such as Netflix rewriting the rules of the game.
It will be fascinating to see how the media landscape will reshape in the coming years, but it seems pretty clear that Netflix’s combination of data, algorithmic personalisation and massive content investment are likely to keep us glued to the screen watching their shows.
So is Netflix a media company? It certainly competes in this industry, but you might argue that Netflix really is in the business of personalisation and recommendation.
Mario has over 15 years experience in a range of senior management roles with a solid background in e-commerce and online marketing and a proven sales record in multicultural environments. Currently heading liligo.com, the leading travel meta search player in France expanding strongly at worldwide scale.