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“Forget squeezing millions from a few megahits at the top of the charts. The future of entertainment is in the millions of niche markets at the shallow end of the bitstream.” -Chris Anderson. ![]() The long tail is the colloquial name for a long-known feature of statistical distributions (Zipf, Power laws, Pareto distributions and/or general Lévy distributions ). The feature is also known as “heavy tails”, “power-law tails” or “Pareto tails”. Such distributions resemble the accompanying graph — where the long tail is the area below the right-hand part of the curve. In these distributions a high-frequency or high-amplitude population is followed by a low-frequency or low-amplitude population which gradually “tails off”. In many cases the infrequent or low-amplitude events—the long tail, represented here by the yellow portion of the graph—can cumulatively outnumber or outweigh the initial portion of the graph, such that in aggregate they comprise the majority. Chris Anderson’s application of this principle to internet phenomena, drew on an influential essay by Clay Shirky, “Power Laws, Weblogs and Inequality” that noted that a relative handful of weblogs have many links going into them but “the long tail” of millions of weblogs have only a handful of links going into them. Anderson described the effects of the long tail on current and future business models. Anderson argued that products that are in low demand or have low sales volume can collectively make up a market share that rivals or exceeds the relatively few current bestsellers and blockbusters, if the store or distribution channel is large enough. Examples of such mega-stores include Amazon.com and Netflix. The Long Tail is a potential market and, as the examples illustrate, successfully tapping in to that long tail market is often enabled by the distribution and sales channel opportunities the Internet creates. http://en.wikipedia.org/wiki/The_long_tail (Tuesday, 2 May 2006) |
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