“House Of Cards” And The Problem With Big Data

March 4, 2013

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Consumers like what consumers like.

That’s the very simplest way of explaining how the political thriller “House of Cards,” the latest Netflix original series, has become the most revered TV show never to air on TV. Ask anyone who has seen the first season and they will cop to having spent huge chunks of time viewing it — some, in just one 13-hour helping. It’s that good.

The success of “House of Cards” can’t really be considered a creative one, though. Rather, its success is almost entirely mathematical. Last week, New York Times media critic David Carr explained how Netflix made entertainment the latest touch point in our culture to leverage the power of Big Data:

Netflix, which has 27 million subscribers in the nation and 33 million worldwide, ran the numbers. It already knew that a healthy share had streamed the work of Mr. Fincher, the director of ‘The Social Network,’ from beginning to end. And films featuring Mr. Spacey had always done well, as had the British version of ‘House of Cards.’ With those three circles of interest, Netflix was able to find a Venn diagram intersection that suggested that buying the series would be a very good bet on original programming.

In addition, movies and TV shows on the service are annotated with hundreds of tags — metadata descriptors — inserted by viewers commissioned to describe the talent, the action, the tone and the genre, among many, many other things. In the past, those tags were used to recommend other shows from the long tail of content on the service, essentially building profiles based on the preferences of individual subscribers. But now Netflix is commissioning original content because it knows what people want before they do.

In other words, “House of Cards” was hardly a big gamble, but rather, Netflix executives playing with “a stacked deck,” as Carr writes.

Consider: We have finally reached a point in humanity where the technology we have created can process, synthesize and – in some cases – predict outcomes before they happen. Why do I love “House of Cards?” Because I enjoy many of the same pieces — Kevin Spacey, Robin Wright, politics, Scotch, video games, David Fincher, first-person narratives — that make “House of Cards” precisely what it was designed to be.

Alfred Hitchcock shrugs.

There is a downside here, of course. Envision “House of Cards” as an advanced algorithm scrawled on a whiteboard rather than the product of a few painstakingly brilliant writers and you can’t help but feel, I don’t know, predictable? No consumer wants to be predictable.

That’s not what marketers want, anyway.

Here’s why: Big Data suggests people/consumers will do as they have done in the past and that there is much smaller probability they will enjoy something they have not enjoyed in the past. But what if that something is something they have never tried before?

I had not tasted Thai food until a year ago. Now? I can’t go a week without some red curry chicken. I cringe at the sound of a banjo, but I love the Punch Brothers. I hated the idea of distance running my entire life until I experienced my first runner’s high last year. This fall, I’m running the Twin Cities Marathon. Big Data never would have predicted I would become a bluegrass-loving runner with deadly curry breath.

Don’t get me wrong, Big Data has been on one hell of a run. (I’m talking to you, Nate Silver.) But I’m a marketer and part of my job is to push consumers away from their predisposed tastes and preferences. So much of the beauty of the human experience is found in trying and enjoying things that are new and different.

All along, “House of Cards” was nothing more than an amalgamation; all of my old friends in new clothing.

So, did I like it?

Probably.