For many media companies, online distribution has been seen as a practical solution to audience fragmentation. Those who are not interested in primetime content can satisfy their needs by shows that are available online, on-demand. The problem with this “long tail” solution is finding the right content for these fragmented audiences. Going through an extensive catalogue of different tv and radio shows won’t bring you any closer to satisfaction than simply succumbing to the alluring yet numbing power of American Idol or Big Brother.
The solution to this particular problem is, naturally, personalization. In an interview for Wired, Netflix’s Neil Hunt stated that in the future, Netflix’s recommendation algorithm will be so accurate that it will be able to give users “one or two suggestions that perfectly fit what they want to watch now.”
Obviously, Netflix is not there yet:
Snide remarks aside, Hunt’s vision is probably true, but not because Netflix is about to find the golden piece of code that will make this prediction of the future reality, but simply because media consumption is very, very predictable. In a Harvard Business School study from 2008, Anita Elberse found that the top 10 % of songs on the music streaming service Rhapsody accounted for 78 % of all plays and that the top 1 % accounted for nearly one-third of all plays (cited in Misunderstanding the Internet, 2012). The tail had gotten longer, sure, but the big profits were still made where the tail was the thickest. A quick glance at YouTube statistics would confirm this.
“Predicting” that people will want to see Game of Thrones after seeing the Walking Dead isn’t difficult, it’s just … probable. Personal preferences play in, of course, but I don’t think I’m going out on a limb when I say that 10 viewing profiles with appropriate standard recommendations would fulfil 90 % of all viewers’ needs.
The thing with predictions is that they effectively make the tip of the long tail obsolete. It’s more likely that primetime shows will be predicted, since it is quite probable that a viewer will be content with what’s offered. Suggesting less-popular shows is riskier, as the prediction is more likely to go wrong. Instead of watching one primetime show we’ll watch nothing but primetime, as recommended by algorithms. At least with Netflix’s failed recommendations, it’s possible to find something completely unexpected.