Today Netflix announced a million-dollar contest for the team who can come up with the best improvement to its DVD recommendations. Aside for the obvious points that better recommendations are, well, better and the best thing of all is to get volunteers to do the work, why is this such a priority for Netflix, which is known for having pretty good recommendations already?
The simple answer is that search, recommendations and other filters tend to drive demand down the tail, from the hits down to the niches where minority tastes are often better satisfied. Aside from happier customers, this also has some clear economics benefits for Netflix. It so happens that older titles, well down the Long Tail of time, are both cheaper to acquire and tend to get higher ratings than new titles (mostly because they've passed the test of time and have moved beyond the fog of hype that accompanies new releases). Not only that, but Netflix can also buy fewer of them, since as you go further down the curve the demand is spread out over more titles and there's less of a need to buy stacks of expensive new blockbusters to satisfy the rush of rentals requests around the release date.
To explain why this is such a good business move for Netflix, let's start with the economic picture on the DVD acquisition side. The following chart was primarily meant to show how margins can get better in the tail, but the thing to notice in this discussion is that acquisition costs fall dramatically with age.
Furthermore, DVD renters are much happier with the DVDs they get from recommendations (which tend to be to older DVDs) as opposed to new releases, as shown by the following slide from a presentation that Netflix's Jim Bennett gave at the Recommenders06 conference in Bilbao last month. Note that once again, the most expensive DVDs for Netflix are the new releases ("NR"); the least expensive are the ones that were rented as a result of a recommendation (the left two)
(Each bar refers to a way that a customer found the video that they rented. Again, "NR" is New Releases; "Interest" refers to the recommendations you get when you add a DVD to your queue and you get a "Other films you might enjoy..." suggestion. "Recs" are recommendations based on rental history and ratings.)
Evidence of the power of Netflix's recommendations to drive demand down the tail can be seen in the following data comparison, which shows Netflix rentals versus the industry as a whole (mostly bricks-and-mortar rental stores such as Blockbuster, hence the labeling). I've plotted the demand curves on a log-log scale so the powerlaws look like relatively straight lines and I've indexed them to the same scale for comparison purposes. The difference in the slopes of the lines shows that Netflix is considerably more niche-centric than the traditional industry retailers such as Blockbuster.
Recall that there are two factors that create functioning Long Tail markets: 1) massive increase in product variety, and 2) massive improvement in findability. In the above chart, for the first 20,000 titles or so the inventory is the same between Netflix and the aggregate bricks-and-mortar retailers. But because Netflix has search and recommendations, two things that are hard to do in a traditional store, it wins the findability contest hands down. That's why its stats show that niche titles (rank 400 and below) are rented far more often: people are able to easily find them even after they've left the new release promotion shelves in physical stores.
So let's put this all together and bring it back to Netflix's recommendations contest. As shown above, the more Netflix can get customers to rent DVDs from recommendations, rather than new releases, the more money it makes and the more satisfied the customer. The prize will go to the team that can improve those recommendations by 10% (measured by correspondence to customer ratings). I don't know what the exact effect of a 10% improvement in accuracy would have on the demand curve, but I can illustrate the basic effect with the following conceptual chart.
Is that worth $1 million? Netflix clearly thinks so. If there were any doubt about the importance of filters to a functioning Long Tail, this should put it to rest.