Netflix: How Big Brother is watching what you’re watching

big brother is watching you darth vader

It certainly seems that online streaming services are the future of home entertainment; with super-fast fibre optic broadband becoming the norm, this form of ad-hoc pop-culture consumption is becoming more accessible and fundamentally, more convenient, than traditional methods of accessing new films, TV-series and video games.

Gone are the days when you’d spend £25 on a box-set or trawl through dodgy sites closing endless numbers of porn and gambling pop-up ads to illegally download. Why bother? When these streaming websites open up a world of entertainment for a low monthly fee.

It is estimated that 10.2% of British homes subscribe to Netflix and 4.5% to Amazon Prime – however one would imagine that these are not unique subsets, with each service providing a distinct offering. While we are all glued to our screens, equally Netflix and Amazon Prime are paying attention to what we’re watching too.

Netflix US tweet: Going out tonight? LOL! No you're not. Come join us, loser.

It is also widely documented that Netflix has one of the most sophisticated big data analysis teams in the world, but what exactly is the purpose of all this number crunching?

Keeping Us Happy

Ultimately, subscription based services want to keep us happy with the content available so we continue to fork out the subscription fee. By tracking what we view, when we view it and what we considered viewing but rejected, they can ensure that we are subtly nudged towards other programmes of interest to keep us engaged. Broadly, this is done through the use of a propensity score; you may have noticed Netflix estimates how many stars you are likely to give a title you have yet to watch. This is your estimated propensity score for the title; calculated using your past viewing habits and any additional data Netflix may hold on you (gender, region, etc.).

Am I the only one around here who browses Netfix for an hour and then doesn't watch a damn thing?

But, of course, as a business, these companies want to provide appealing content in the most cost-efficient way possible. One way they do this is making it near-impossible to view a full list of available titles on their services; for fear that we may realise their offering isn’t as comprehensive as they would have us believe. Big data allows these services to ensure they provide sufficient titles of interest to different sections of their customer base and update when necessary. Also, fundamentally, big data helps make decisions about when to de-list titles (thus saving money) from the offering.

For example: once 60% of people who have been scored with a high propensity to enjoy the 2009 spin-off Bring it on: Fight to the Finish had either viewed or rejected the title, Amazon or Netflix can make a decision to de-list the title and replace it with a fresh option – tailored to the customer base.

Then, when they have to find something just as fantastic to replace Bring it on: Fight to the Finish to satisfy their audience, they can simply check out what sort of titles people have been searching for and unable to find – suggestions are literally handed right to them.

But I'm A Cheerleader poster

Preventing Lapsing

From the point of view of these companies, hopefully all this nosiness should have helped kept you on as a long-term subscriber, but with the subscription service market being more and more crowded nobody can afford to become complacent. This is where models to identify lapsing households come in. From past experience, service providers can recognise behaviour typical of households who are about to lapse:

  • Lack of engagement with the service over a period of time
  • Scanning titles but not choosing to view anything
  • Starting titles but not finishing

If these service providers are able to identify your likelihood to cancel in the future, possibly even before the thought has actively crossed your mind, they are given a head-start at wooing you back into their warm bubble of pop-culture entertainment.

Production of New Titles

Big data is the big advantage service providers have over traditional production companies; they know exactly who their audience are and what they may like. Netflix didn’t sink $63 million on the first season of the House of Cards remake with Keven Spacey without knowing for sure that it would be a hit. They had all their subscribers viewing preferences, so that they could be sure they would produce something to please their audience.

House of Cards Kevin Spacey poster

Once the show was produced they could additionally use data to create the most appealing cover title, choosing colours and designs found on titles with high click through rates.

So next time you settle down to your favourite titles on an internet based subscription service; just remember you’re not the only one doing the watching!


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