The Long Tail Is Only As Good As The Recommendation System
from the that's-how-it's-supposed-to-work dept
It’s been amusing to watch folks like Andrew Orlowski continue to misinterpret Chris Anderson’s slight admission that things in “the long tail” aren’t exactly they way he’d predicted them to be. Of course, Orlowski entirely misses the point by assuming incorrectly (as many others have done) that the discussion of the long tail meant the death of “the blockbuster.” That’s not at all true. The idea of the long tail was that it both enabled more content to be produced by opening up more shelf space and then made it easier to find that content.
But, the fact remains that the finding of that content is entirely dependent on the filtering and recommendation systems, which is highlighted in the recent NY Times piece by Clive Thompson about attempts to improve Netflix’s recommendation engine (and, yes, this is the second post I’ve written on that article, but this is discussing an entirely different issue than the first, so it seemed worthwhile). In the article, Thompson notes:
Cinematch has, in fact, become a video-store roboclerk: its suggestions now drive a surprising 60 percent of Netflix?s rentals. It also often steers a customer?s attention away from big-grossing hits toward smaller, independent movies. Traditional video stores depend on hits; just-out-of-the-theaters blockbusters account for 80 percent of what they rent. At Netflix, by contrast, 70 percent of what it sends out is from the backlist ? older movies or small, independent ones. A good recommendation system, in other words, does not merely help people find new stuff. As Netflix has discovered, it also spurs them to consume more stuff.
Basically, that entire paragraph explains the issue. A good recommendation system does two things: it gets people to consumer more — and it introduces them to stuff they might not have heard about otherwise. But, that second part is not necessarily the same as the first part. Many people assumed, incorrectly, that the greatness of such “long tail filters” was that it would drive people to consumer more down the tail — but as Netflix is seeing, the good recommendation engine drives people to consume more content in both the head and the tail.
And, when you think about it, that makes an awful lot of sense. Popular stuff often is popular for a reason. While some may disagree, things are often popular because they really do appeal to a lot of people, so it should be no surprise that a good recommendation system would increase consumption in the head: it’s accurately noting that an awful lot of people will like that content. But that doesn’t exclude promoting some of the content from the tail. Since the recommendation system is driving more consumption overall, it’s “lifting all boats” as they say, even if (as is likely) it lifts the boats in the head more than in the tail. In the past, that content in the tail wouldn’t get any business at all, but these days it can at least make some money, if not a huge amount.
So, no the concept of the long tail is hardly dead or even in trouble (or, as Orlowski notes, downgraded). Instead, it’s just being understood better.