Testing the Long Tail's First Test

Web entrepreneurs have been relying on the Chris Anderson’s long tail hypothesis even before he published his ideas, so it’s good to see that the academic community has begun to study the effect and test for its existence. The results of the first major study looking for proof of the long tail were announced on Harvard Business Online and were not the endorsement of the long tail that proponents were looking for. Specifically, the author, Prof. Elberse, found a growing preference for hits rather than niche products based on an increasing portion of all sales coming from the top 10% of items sold on Rhapsody and Quickflix. This leads her to recommend that companies focus on cultivating popular products, rather than long tail products, generally ignoring long-tail business models.

While these results are important and shouldn’t be taken lightly, the analysis falls short of what I’d like to see on three fronts. First, I’d like to see more depth to the statistical analysis, focusing on analyzing the falloff of sales volume – rather than just sales volume in the head of the distribution – to avoid arbitrary definitions of “hit” and “long tail” (such as whether to use the top 5%, 10% or 20%). Second, I’d like to see greater attention paid to the types of goods being sold and whether they are even appropriate for analyzing long-tail effects. Finally, I’d like to see managerial recommendations that are focused on profitability, rather than top-line revenue – a more helpful metric for designing business models.

Elberse’s study focuses on the head of the sales distribution, but the head doesn’t tell us how the tail behaves. To do that, we need to look at how the volume of the tail falls off. Fortunately, power-law distributions (the statistical distribution that describes the long-tail theory) are easily described with a single number, k, which tells us how fast popularity dies out. Long-tail proponents would expect this number to slowly increase over time, pushing more of the sales toward the tail. Moreover, looking at deviations from a power-law distribution – lower than predicted volume in the head and greater than predicted volume in the tail – would provide a stronger indication of the long tail. (For more on some of the statistical problems in measuring the long-tail see Kurt Cagle’s nice write up.)

In addition, by focusing on music and film distribution services, she ignores the fact that high production costs within these industries make finding evidence of the long tail difficult. By focusing on other industries where every part has low marginal costs, she would have had a much better opportunity for finding long-tail effects. Since much of the music and movie industries have not adjusted to long-tail forces – the bulk of the less popular titles in existing catalogs are failed hits, rather than niche products – her results aren’t surprising, as she was looking for long tail at the distribution end of industries that are just beginning to support it in the production end. Until long-tail production houses like RCRDLBL and Normative take off, Rhapsody and QuickFlix can only sell the movies and albums that are produced – and since these industries have low marginal-cost distribution, but not production, they aren’t ideal choices.

If you are developing a new long-tail business model these results can be disconcerting, especially given Elberse’s recommendation that you ignore the long tail entirely. However, even when you set aside whether her results are correct to begin with, her recommendations are only instructive for driving top-line revenues. They fail to address what’s really important: becoming profitable. Creating hits can be an expensive business – it’s risky and error prone. In contrast, one of the most attractive features of a long-tail business is its focus on selling products with very low costs of production. Ignoring the opportunity for making profits with very low costs through volume means not just ignoring the entire idea of the long-tail approach, but also the importance of profits over revenue. With Elberse’s single-minded approach, while there is the potential for taking in more money overall, there is also the potential of operating at an overall loss through high expenditures. You would be better off to think of your business holistically, considering both top-line revenue and profitability, for the well-being of your own business, as well as that of your suppliers and partners, so every part of your industry is working to take advantage of the long tail.

Whether or not you agree with the results shouldn’t change that fact that it’s important that Elberse has given us our first results in this market. Her results, made possible only through privileged access to proprietary data, are the first steps towards understanding if the web can alter the way we consume products. I’d like to see more of this research put forward and a clean dataset made public for others to examine (the likelihood of this is very low given how valuable this data is to companies – but one can hope). The publication of these results have already started a debate online and are forcing proponents of the theory to make the arguments more robust and, therefore, more helpful to entrepreneurs.