The phrase The Long Tail (as a proper noun with capitalized letters) was first coined by Chris Anderson in a 2004 article in Wired magazine [1] to describe certain business and economic models such as Amazon.com or Netflix. The term long tail is also generally used in statistics, often applied in relation to wealth distributions or vocabulary use. 数据挖掘研究院
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The long tail in probability theory statistics
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. 数据挖掘研究院
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.
Such distributions are surprisingly common. In standard English, the word "the" is the most common word and other short words such as "of", "is" and "have" are also quite common. These common words are vastly more common than most other words. For example, about 12% of all words are "the" (while "barracks" occurs less than 1 out of 50,000 words), but cumulatively, words roughly as rare as "barracks" make up about a third of all text. These rare words are the long tail in English vocabulary. [2] 数据挖掘研究院
The Long Tail by Chris Anderson
The phrase The Long Tail, as a proper noun, was first coined1 by Chris Anderson, drawing 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. Beginning in a series of speeches in early 2004 and culminating with the publication of a Wired magazine article in October 2004, 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. 数据挖掘研究院
A former Amazon employee described the Long Tail as follows: "We sold more books today that didn′t sell at all yesterday than we sold today of all the books that did sell yesterday." In the same sense, Wikipedia has many low popularity articles that, collectively, create a higher quantity of demand than a limited number of mainstream articles found on a professional site such as Britannica.
The term is derived from the XY graph that is created when charting popularity to inventory. For example, in the graph shown on this page the total inventory of Wikipedia articles is along the bottom line, while the popularity rating (web page hit statistics) is along the vertical axis. So, for example, the Wikipedia homepage would receive the most views and be on the far left in the red, while this page might be on the far right in the yellow, as would most of Wikipedia′s articles. The same could be said for Amazon′s book inventory or Netflix′s movie inventory. The total volume of low popularity items exceeds the volume of high popularity items.
Relationship with storage and distribution costs
The key factor that determines whether a sales distribution has a Long Tail is the cost of inventory storage and distribution. 4 Where inventory storage and distribution costs are insignificant, it becomes economically viable to sell relatively unpopular products; however when storage and distribution costs are high only the most popular products can be sold. Take movie rentals as an example: A traditional movie rental store has limited shelf space, which it pays for in the form of building overhead; to maximize its profits, it must stock only the most popular movies to ensure that no shelf space is wasted. Because Netflix stocks movies in centralized warehouses, its storage costs are far lower and its distribution costs are the same for a popular or unpopular movie. Netflix is therefore able to build a viable business stocking a far wider range of movies than a traditional movie rental store. Those economics of storage and distribution then enable the Long Tail as advantageous: Netflix finds that in aggregate "unpopular" movies are rented more than popular movies. 数据挖掘实验室
Cultural and political impact
The Long Tail has possible implications for culture and politics. Where the opportunity cost of inventory storage and distribution is high, only the most popular products are sold. But where the Long Tail works, minority tastes are catered to, and individuals are offered greater choice. In situations where popularity is currently determined by the lowest common denominator, a Long Tail model may lead to improvement in a society′s level of culture. Television is a good example of this: TV stations have limited time slots, so the opportunity cost of each time slot is high; stations therefore choose programs that have the broadest appeal. But as the number of TV stations grows or TV programming is distributed through other digital channels, the choice of TV programs grows and the cultural diversity rises. 数据挖掘实验室
Some of the most successful Internet businesses have leveraged the Long Tail as part of their businesses. Examples include eBay, Yahoo!, Google, and Amazon amongst the majors along with smaller Internet companies like Audible and Netflix (cited above). 数据挖掘研究院
Competition and the Long Tail
The Long Tail may threaten established businesses.3 Before a Long Tail works, the only products on offer may be the most popular, but when the costs of inventory storage and distribution fall, then a wide range of products becomes available; that can, in turn, have the effect of reducing demand for the most popular products. For example, Web content businesses with broad coverage like Yahoo or CNET may be threatened by the rise of smaller Web sites that focus on niches of content, and cover that content better than the larger sites. The competitive threat from these niche sites is related to the cost of establishing and maintaining them and the bother required for readers to track multiple small Web sites. These factors have been transformed by easy and cheap Web site software and the spread of RSS.
Brynjolfsson, Hu, and Smith
In his Wired article, Anderson cites earlier research by Brynjolfsson, Hu, and Smith, who quantified the potential value of the long tail to consumers. In an article published in 20032 these authors show that, while most of the discussion about the value of the internet to consumers has revolved around lower prices, consumer benefit (a.k.a. consumer surplus) from access to increased product variety in online book stores is ten times larger than their benefit from access to lower prices online. Thus, the primary value of the internet to consumers comes from releasing new sources of value by providing access to products in the long tail.
The Long Tail and Scale-Free Networks
The long-tail distribution (a few things rated many times, many things rated a few times) may be modeled by Scale-free networks. In particular, its shape may be a "power-law" curve. Also, one of the "generative models" for a scale-free network ("the rich get richer") may be a sensible way to explain the long-tail distribution. For example, a book that has been bought many times will tend to be bought more, because more people will hear about it.
See also
- Professional amateurs
- Pareto principle
- Network effect
- Collaborative Filtering
- Matthew effect
- Gini coefficient
- Lorenz curve
- Narrowcasting
Probability distributions with "long tails"
- Lévy distribution
- Pareto distribution
- Zeta distribution
- Zipf′s law
- Zipf-Mandelbrot law
Notes
- Note 1: See The origins of "The Long Tail" by Chris Anderson
- Note 2: See Brynjolfsson, Erik, Yu (Jeffrey) Hu, and Michael D. Smith, "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers" Management Science, Vol. 49, No. 11, November 2003, and the April 2003 working paper version available via the Social Science Research Network.
- Note 3: "Competition and The Long Tail" by David Jackson.
- Note 4: "Competition and The Long Tail" by David Jackson.
External links
- "The Long Tail" by Chris Anderson, Wired, Oct. 2004
- "The Long Tail" as a Change This Manifesto. Same as the Wired article; includes additional graphics and Adobe Reader interface
- "Power Laws, Weblogs and Inequality" by Clay Shirky
- "Zipf, Power-laws, and Pareto - a ranking tutorial" by Lada A. Adamic
- The Long Tail Blog by Chris Anderson
- "Personalization, The Long Tail, And The Charge Against The Customer Monoculture" by Christopher Carfi
- "The long tail of software. Millions of Markets of Dozens"
- "Search′s Long Tail" by Danny Sullivan
- Profiting from obscurity from The Economist
- "The Economics of Digitality and the long tail" by Stirling Newberry
- "Consumer Surplus in the Digital Economy" by Erik Brynjolfsson, Jeffrey Hu, and Michael D. Smith, November 2003

