The Long Tail! Chris Anderson

The Long Tail or long tail refers to the statistical property that a larger share of population rests within the tail of a probability distribution than observed under a 'normal' or Gaussian distribution. This has gained popularity in recent times as a retailing concept describing the niche strategy of selling a large number of unique items in relatively small quantities – usually in addition to selling fewer popular items in large quantities. The concept was popularised by Chris Anderson in an October 2004 Wired magazine article, in which he mentioned Amazon.com and Netflix as examples of businesses applying this strategy.[1][2] Anderson elaborated the Long Tail concept in his book The Long Tail: Why the Future of Business Is Selling Less of More (ISBN 1-4013-0237-8).[3]
The distribution and inventory costs of businesses successfully applying this strategy allow them to realize significant profit out of selling small volumes of hard-to-find items to many customers instead of only selling large volumes of a reduced number of popular items. The total sales of this large number of "non-hit items" is called the Long Tail.
Given a large enough availability of choice, a large population of customers, and negligible stocking and distribution costs, the selection and buying pattern of the population results in a power law distribution curve, or Pareto distribution. This suggests that a market with a high freedom of choice will create a certain degree of inequality by favoring the upper 20% of the items ("hits" or "head") against the other 80% ("non-hits" or "long tail").[4] This is known as the Pareto principle or 80–20 rule.
The Long Tail concept has found some ground for application, research, and experimentation. It is a term used in online business, mass media, micro-finance (Grameen Bank, for example), user-driven innovation (Eric von Hippel), and social network mechanisms (e.g., crowdsourcing, crowdcasting, peer-to-peer), economic models, and marketing (viral marketing).
A frequency distribution with a long tail has been studied by statisticians since at least 1946.[5] The term has also been used in the insurance business for many years.[1]
Statistical meaning

The tail becomes bigger and longer in new markets (depicted in red). In other words, whereas traditional retailers have focused on the area to the left of the chart, online bookstores derive more sales from the area to the right.
The long tail is the name for a long-known feature of some statistical distributions (such as Zipf, power laws, Pareto distributions and general Lévy distributions). The feature is also known as heavy tails, fat tails, power-law tails, or Pareto tails. In "long-tailed" distributions a high-frequency or high-amplitude population is followed by a low-frequency or low-amplitude population which gradually "tails off" asymptotically. The events at the far end of the tail have a very low probability of occurrence.
As a rule of thumb, for such population distributions the majority of occurrences (more than half, and where the Pareto principle applies, 80%) are accounted for by the first 20% of items in the distribution. What is unusual about a long-tailed distribution is that the most frequently-occurring 20% of items represent less than 50% of occurrences; or in other words, the least-frequently-occurring 80% of items are more important as a proportion of the total population.
Power law distributions or functions characterize an important number of behaviors from nature and human endeavor. This fact has given rise to a keen scientific and social interest in such distributions, and the relationships that create them. The observation of such a distribution often points to specific kinds of mechanisms, and can often indicate a deep connection with other, seemingly unrelated systems. Examples of behaviors that exhibit long-tailed distribution are the occurrence of certain words in a given language, the income distribution of a business or the intensity of earthquakes (see: Gutenberg-Richter law).
Chris Anderson's and Clay Shirky's articles highlight special cases in which we are able to modify the underlying relationships and evaluate the impact on the frequency of events. In those cases the infrequent, low-amplitude (or low-revenue) events — the long tail, represented here by the portion of the curve to the right of the 20th percentile — can become the largest area under the line. This suggests that a variation of one mechanism (internet access) or relationship (the cost of storage) can significantly shift the frequency of occurrence of certain events in the distribution. The shift has a crucial effect in probability and in the customer demographics of businesses like mass media and online sellers.