Thursday, 20 May 2010
Summary!
Below is a define meaning of what the long tail, involves, it gives statistical meanings and sums up more of an accurate meaning of what the long tail involves, everything that is beiong sold on the internet, will be bought at least once, which still creates a profit, the long tail expresses, the niche market and the mainstream marketing, involving products that are sold well and others that may not be sold well but are still sold in some way. The 80% 20% rule, how the consumers purchase the products, portraying each product within different markets.
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.
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.
A Vary Of Internet Television!
There are many different varies of sites that offer many different shows to the public after they have been screened on television, The ineternet is offering more and more catch up sites, making almost everything that is displayed on the tele accesible, the bbc channels, itv channels, e4 channels and also sky catch up even though sky also offers recording, which again makes it more accesible for the public to be able to watch shows whcih they have missed or forgot to re
cord.
cord.Sky also offers live streaming, watching live shows on the internet as they are being played, creating more accesability, all these catch up shows are making it easier and easier for and audience to be able to watch any show they like, whenever they like, Chris Anderson also contributes to this idea, as he explains hopw a television can present one show to millions of people, whereas the internet can present millions of shows to one person.
BBC iPlayer
BBC i Player is the equivalent to a television, but presented through the internet, at the comfort of being accesible, when and where you feel like watching it, BBC iPlayer went live on the 25th December 2007. The site tagline is "Catch up on the last 7 days of BBC TV & Radio", Making television more accesible and relevant on the internet, it has made television more suitable for people, who may work all day and miss ceratin things in which they want to watch. it has the importance of not having to worry about recording, and the comfort of knowing it will still be there when you come home. They reflect that programmes are made unavailable on iPlayer after this time (with some exceptions). The BBC state on their website this is due to copyright reasons, however programmes are removed from iPlayer regardless of whether they have in fact been funded partly or entirely by public money.
Focusing On Television!
I am mainly going to look at television, gain information on how online data has effected television, and how televeision has changed.
i am going to look at websites which play programmes normally watched on the television, how it has effected the ratings of how many people watch normal everyday televsion compare to catching up on the internet, and how it is still rapidly changing.
i am going to look at websites which play programmes normally watched on the television, how it has effected the ratings of how many people watch normal everyday televsion compare to catching up on the internet, and how it is still rapidly changing.
New Blog!
I have started up a new blog to upload information, to help me revise for my exam, im going to upload important facts and information which will help me prepare for my exam.
Subscribe to:
Posts (Atom)