The Aggregator Blog Model: How a Blog leverages Long Tail Economics

Transcription

1 The Aggregator Blog Model: How a Blog leverages Long Tail Economics Michael P. Evans Computer Science and Informatics, University of Reading, Reading, UK Michael.Evans@reading.ac.uk +44(0)

2 The Aggregator Blog Model: How a Blog leverages Long Tail Economics Author biography: Mike Evans is a lecturer in Computer Science and Informatics at the University of Reading, where he researches the evolution of the Web and social networks, and their impact on society. In addition to his academic research in Web usage, technology and evolution, he has also worked in industry on a variety of Web-related projects and publishes a portfolio of successful Web sites that he uses to monitor the changing ways in which the Web is used.

3 The Aggregator Blog Model: How a Blog leverages Long Tail Economics 1 Abstract Long Tail Economics is a concept defined by Chris Anderson to describe the business model employed by the majority of Web sites and ecommerce stores, which effectively operate in an economy of abundance. Anderson s thesis has become well-known, but he focused largely on ecommerce stores such as Amazon, and Content Aggregators, such as YouTube and Flickr. In contrast, this paper seeks to identify the effects of Long Tail Economics on an individual blog. Blogs have become increasingly important in the dissemination of information for academics, organizations and individuals alike. By modelling the blog as an Aggregator of search terms and analyzing usage data from a live blog, the paper shows how Long Tail Economics can be applied to a blog to reveal the forces that both drive and constrain its growth. Keywords: Long Tail, Web 2.0, blog, search engine, Web 1 A reduced version of this paper was presented at ISOneWorld 2008, University of Nevada, Las Vegas, Nevada, USA, June

4 The Aggregator Blog Model: How a Blog leverages Long Tail Economics 1. INTRODUCTION Over the past few years, the Web has evolved away from its origins as a series of interconnected, but largely static, Web sites towards a much richer interactive medium popularly called Web 2.0 (O Reilly, 2005). Web 2.0 has brought a new social dimension to the Web, encouraging users to add their own content to a site as well as reading existing content. This User Generated Content (UGC) is principally found in large Web sites known as Aggregators, such as Flickr, YouTube, and Wikipedia, which aggregate the content generated by hundreds of thousands of users. 1.1 Long Tail Economics the Business Model of Web 2.0 The business model for Web 2.0 sites is based on Long Tail Economics, which sets out a model for revenue generation in an economy of abundance, which is what most Web sites, with virtually limitless disk space and bandwidth, now operate in. Long Tail Economics was popularised by Anderson (2006), who described how many Web companies operate in what he terms Long Tail markets, where distribution is cheap and inventory can be virtually unlimited. This is a fundamentally different business model from traditional retailers, who are constrained by the cost of distributing physical products and the shelf space they have in which to display their inventory. Anderson showed through various examples that a huge inventory of unpopular items (the tail ) can be just as profitable as an inventory of only the most popular items (the hits ). However, these examples focused primarily on how Aggregators exploited the Long Tail, ignoring the opportunities that may be present for other types of Web site, such as blogs.

5 This paper seeks to correct this omission by providing evidence of Long Tail Economics at work within a popular blog. 1.2 Applying Long Tail Economics to the Blog A blog, according to Wikipedia, is a web-based publication consisting primarily of periodic articles (normally in reverse chronological order) (Wikipedia, 2005). Blogs are an interesting case study to focus on because they are an increasingly important medium for the dissemination of information for both organizations and individuals alike. Some examples include: academics, who use blogs to bring their research to a wider audience; organizations, and particularly ecommerce stores, which use blogs to help build their brand, establish trust and improve their search engine rankings, thereby driving traffic to their store and away from their competitors. For example, is a leading UK mobile phone e-commerce store that drives traffic to its site through online advertising and through its blog, and individuals, who use blogs for all manner of reasons, from personal expression, to significant revenue generation ( Nick Denton s blog network Gawker.com, for example, is currently estimated to be worth $170 million (MacIntyre, 2009)). In all these examples, a blog s success is dependent upon the number of people it attracts: Disseminating information widely, whether for academic or organizational purposes, requires a large number of people to be exposed to that information. Much of the revenue generated from blogs comes from advertising, either through ad programs such as Google AdSense, which operates on a pay per click model, or direct

6 sponsorship where revenue is earned per thousand ad impressions. In both cases, revenue is directly proportional to the number of people who visit the blog, making revenue generation a function of visitor numbers. Understanding the forces that both drive and constrain a blog s visitor numbers can therefore bring substantial benefits to a wide range of individuals and organizations. This paper aims to achieve this understanding through the development of a new model of the blog called the Aggregator Blog Model (ABM). The ABM views a blog s content as its inventory, and was developed to show how the long tail of search terms contained within that content is governed by the same Long Tail Economics that drive the growth of Aggregators. The practical significance of the ABM is that it enables blog publishers to focus their limited resources on the areas of their blog that maximize its ability to attract visitors, whether for the dissemination of information or for revenue generation. The paper presents the ABM as a novel model of the blog, and validates the model using three years worth of Web Analytics data taken from the blog MobileMentalism.com. The characteristic Long Tail of search terms is shown, and the effects of the three forces of Long Tail Economics on the blog are revealed as they shape the blog s popularity over time. The result is a fully validated model of the blog that shed new light on the dynamics that both drive and constrain its growth. The paper is presented as follows: section 2 provides the background to the research by describing Long Tail Economics and blogging, respectively. Section 3 defines the

7 Aggregator Blog Model (ABM), a model of the blog to which Long Tail Economics can be applied. Section 4 uses three years worth of Web analytics data to show how the ABM can leverage Long Tail Economics. Finally, section 5 concludes with a discussion on the limit of Long Tail Economics for the individual blog that this research has discovered. 2. WEB 2.0 AND LONG TAIL ECONOMICS Web 2.0 is the term first coined by Tim O Reilly to describe the new business models and technologies that have emerged on the Web in the 21st Century since the ending of the DotCom bubble in According to O Reilly, Web 2.0 can be defined as the network as platform, with Web 2.0 applications consuming and remixing data from multiple sources, including individual users, while providing their own data and services in a form that allows remixing by others and creating network effects through an architecture of participation. (O Reilly, 2005). It is this last part of O Reilly s definition that refers to the Long Tail; that is, a curve that occurs with near ubiquity on the Web, and whose characteristics have recently been successfully harnessed in the pursuit of new Web business models. 2.1 The Long Tail and Power Laws The Long Tail is a term used by Anderson (2006) to describe the curve that results when plotting a power law, a scale invariant function evident throughout much of science and nature. The most well-known power laws include the Pareto Distribution (popularised by the term the 80/20 rule ) and Zipf s Law, which is particularly evident through much of the Web.

8 The term Long Tail comes from the shape of a power law curve, which exhibits a long tail as the amplitude approaches, but never reaches, zero. Power laws accurately model the popularity of a range of products, such as music or videos, with a small percentage of the product (the hits ) making up a large proportion of the sales. In the case of Amazon, for example, Zipf s Law provides an accurate model of the popularity of the products sold (Brisco et al., 2006), but whereas a traditional retailer such as Wal-Mart would only have shelf space for the top sellers, Amazon, with its virtual store front, can stock millions of different products. Amazon, and other Aggregators like it, such as YouTube, itunes, NetFlix, etc., is therefore able to sell products all the way down the vanishing point of the tail (the niche products ), even those that sell only once a year. What Anderson discovered was that the total sales value of products in the tail is worth as much as the sales value of the hits. This insight led to the idea of the Long Tail, which he describes as...a power law that isn t cruelly cut off by bottlenecks in distribution such as limited shelf space and available channels (Anderson, 2006, p126). 2.2 The three forces underlying Long Tail Economics Anderson calls this economics of abundance Long Tail Economics, which comprises three principal forces that collectively act to reduce the cost of accessing niche products: The first force: Democratize the tools of production Long Tail Economics relies on the provision of an enormous range of items (both physical products and virtual items, such as text, images or videos, etc.). As such, the first force of the Long Tail is the democratization of the tools of production that is, make the cost of

9 producing the item so low that an enormous variety of it is produced by users in their thousands (i.e. the tool is made widely available, rather than being available to a select few). Examples of the effects of the first force include the explosion of digital photographs that have emerged since the introduction of cheap digital cameras and camera-phones; and the enormous increase in Web content that has been produced (Technorati currently indexes 37,500 blog posts an hour (Technorati, 2008)) since blogging platforms made it easy even for the layman to create professional looking Web sites The second force: Democratize the tools of distribution The second force of the Long Tail focuses on the consumption of the content that is produced as an effect of the first force. Aggregators such as Flickr or YouTube have a key role in the second force, as they significantly lower the cost of consumption by providing cheap and easy access to the content being produced. Anderson gives the example of retailing. Wal- Mart has spent billions setting up a huge and expensive supply chain that offers consumers access to the products it sells. This supply chain acts as a distribution channel, giving Wal- Mart the power to decide what will and what will not be distributed to the market it has created. Such is the power of this distribution channel that an entire category of product can be killed off if Wal-Mart chooses not to stock it. However, on the Web, ebay provides a market just as big as Wal-Mart s, but crucially, one that everyone has access to. Wal-Mart s market is controlled by Wal-Mart, which decides who can distribute what to its customers; ebay s market in contrast is available to all.

10 ebay is just one of many Aggregators on the Web, each of which provides a virtual distribution channel that enables millions of users to consume the millions of items in its inventory; Flickr is another, providing free access to over 2 billion digital photographs (Flickr, 2007), a library of free content the scale of which has never been seen before. Increased access to the goods on offer causes more niche goods to be available to users who would simply not have access to those goods from traditional distribution channels. With more goods being accessed throughout the length of the tail, the second force s effect is to fatten the Long Tail The third force: Connect Supply and Demand The third force of the Long Tail focuses on the filters that enable people to find the content they want. By connecting supply with demand through these filters, demand is driven to products further down the tail, ensuring it is not just the hits that are being accessed. The filter is the real innovation that the Web 2.0 paradigm has brought to Aggregators, as it connects supply and demand through the use of social data: that is, the emerging patterns inferred from the previous choices made by millions of users (the so-called Wisdom of Crowds effect (Surowiecki, 2005)). For example, Amazon s Recommendation Engine uses choices made by previous customers to suggest products to new customers (Jacobi and Benson, 1998); and Google s PageRank algorithm (Brin and Page, 1998) filters the results of Web pages returned from a user s query according to the number of links each Web page has from other Web pages (among other factors).

11 By driving demand further down the tail, the third force increases demand for niche products that traditional distribution channels cannot supply. The effect of the third force, therefore, is to flatten the curve of the Long Tail, shifting its centre of gravity to the right (see Anderson, 2006, p57). 2.3 Long Tail Economics in action Power laws and their attendant long tails are common phenomena in networked environments, and so their appearance throughout the Web is no surprise. With the Web and the technologies that make up Web 2.0, though, the forces that underlie such power laws have been harnessed for competitive advantage, leading to a new business model termed Long Tail Economics. The following lists just some of the ways in which different companies have successfully leveraged Long Tail Economics to their own commercial benefit Long Tail Economics and Physical Products Both Amazon and EBay are perfect examples of the Long Tail in action for physical products. Amazon sells brand new physical goods, but unlike a physical retailer, it has only a virtual (online) storefront. With no physical stores to manage, it can significantly reduce its overheads while also aggregating its stock in huge distribution warehouses that are not tied to the location of its stores. In addition, a virtual storefront does not incur extra management costs when new items are added (unlike physical stores where new buildings must be built once inventory reaches the existing building s limits), and so the company is free to stock as many products as it can, irrespective of their popularity. Thus, it is not driven by the Hits and can successfully sell products all the way down the tail (Dempsey, L., 2006).

12 EBay s business model is even more efficient. By acting as a middleman between buyer and seller, it can list literally millions of physical products for sale without needing to stock any of them. Again, the virtual storefront concept means it can sell all the way down the tail, as each new product on sale does not add to the inventory costs of the company Long Tail Economics and Virtual Products Perhaps the best example of the advantages of a Long Tail business model can be found with the distribution of virtual goods, such as music or video distributed through the likes of Netflix or Apple s itunes stores. In a physical store, music is sold via a physical distribution system: the CD. Online, however, music is simply a series of bits that can be transferred simply from machine to machine. Adding one new track to an online store costs nothing, and so it is in the online store s interests to stock as many tracks as possible. In doing so, it can offer a much wider range of tunes than its physical competitors, all the way down the Long Tail of musical niches. The key challenge for an online store is thus connecting supply with demand, as aggregation and distribution, each of which presents significant cost and logistical issues for physical stores, are virtually free Search Terms and the Long Tail Search Engines are fundamentally Long Tail systems. If each search term is ranked according to the number of times it is used, the resultant graph shows an unmistakable power law with an extremely long tail. The search engine Excite, for example, observed that its top ten search terms, each of which was used thousands of times more often than the average search, represented just 3% of the total number of search terms used (Krauss, 2005).

13 Google, too, has confirmed this observation (Manber, 2007), with Google s chief search technologist Udi Manber reporting that 20 25% of the search terms it sees each month have never been seen before (though see the discussion at Lenssen (2007) for clarification of the term never ) Long Tail Economics and Online Advertising In the days prior to the DotCom bust of 2000, online advertising simply required people to see an ad in order for a publisher to be paid. In contrast, the Web 2.0 world employs contextual advertising, which ensures that the ad matches the content of the publisher s site while also only paying out whenever a user clicks on the ad. Where contextual advertising really benefits both publishers and advertisers alike is that anyone can become an advertiser for as little as $10, and any Web site can publish an ad (subject to appropriate terms and conditions). The result is that the long tail of advertisers, from those few who spend tens of millions to the millions more who spends just tens of dollars, can now advertise their products across the Web; the long tail of publishers, from the few media giants to the millions of individual blogs, can publish and earn money from ads; and the supply of products is matched to the demand from users as relevance ranking algorithms such as those used by Google s AdSense program successfully match adverts to content Long Tail Economics and the individual blog Each of these examples has focused on large Aggregators that rely on content generated by thousands of users, or on ecommerce stores that supply a vast array of inventory. However,

14 individual blogs can also be seen as Aggregators, albeit on a much smaller scale. Specifically, a blog can be seen as an aggregator of search terms, and so has the potential to benefit from Long Tail Economics just as much as the other examples given in this section. The following section shows how this can be achieved by describing the Aggregator Blog Model (ABM), which is a model of the blog as an Aggregator of search terms. Section 4 then applies this model to Web analytics data from a live blog to show the effects that the three forces of Long Tail Economics have on growth of a blog. 3. THE AGGREGATOR BLOG MODEL In order to apply Long Tail Economics to a blog, a model of the blog as an Aggregator must be constructed. The model has been called the Aggregator Blog Model rather than the Blog Aggregator Model, as it reflects the fact that it is the blog aggregating content of some form, rather than an Aggregator aggregating the blog (e.g. Technorati is a blog Aggregator because it aggregates many blogs; in contrast, the model proposed here is not of an Aggregator aggregating blogs, but of a blog aggregating search terms). The Aggregator Blog Model (ABM) comprises the following components: Search Terms as the unit of aggregation Although a blog aggregates the posts written by its author, an individual post is too crude a unit of aggregation to apply Long Tail Economics to an individual blog. As such, for the ABM, the search term is used as the unit of aggregation. A search term is simply some combination of words found within the post. As such each post

15 carries orders of magnitude more search terms, providing the granularity required to successfully apply Long Tail Economics. Potential Search Term (PST) In order to refine the model further, search terms have been classified as Potential, Active and Inactive. A potential search term is one that may or may not have been used by a user to locate the post, but which would return the post if it were to be used. The set of all potential search terms for a blog is therefore the set of all combinations of words within all the blog s posts. Inactive Search Term (IST) An Inactive Search Term is a word or combination of words that appear in a blog s post, but which has not been used in a search engine to find the post. Active Search Terms (AST) An Active Search Term is one that has been used in a search engine to find a post. The set of all ASTs and ISTs collectively form the set of PSTs. Blog Distribution Channel The distribution of a blog s content is dependent upon the set of hypertext links that refer to its content. This set effectively acts as a distribution channel for the blog, directing readers to its content. It is the combination of the number of links referring to the blog and the number of visitors coming from those links that dictates the size of this distribution channel.

16 The distribution channel comprises a set of links from two main sources: links from other Web pages, and links from the results returned from search engine queries. In MobileMentalism s case, roughly 60% of people visiting have come from the links returned by search queries from Google. The set of Active Search Terms can therefore be seen as part of the blog s distribution channel.

17 4. APPLYING LONG TAIL ECONOMICS TO THE AGGREGATOR BLOG MODEL In order to test the model, analytics data from a live blog have been obtained and subsequently used to determine the effects each of the three forces of the Long Tail have on the blog when seen from the perspective of the model (that is, viewing the blog as an aggregator of search terms). The blog used for this paper is MobileMentalism.com, details of which follow. 4.1 Testing the ABM with MobileMentalism.com MobileMentalism.com is a successful Blog that has been continuously published by the author since August MobileMentalism s niche is mobile phones, with the posts (i.e. individual articles published on the blog) encompassing a wide range of news, reviews and opinions on the mobile phone market. At the time of writing, some 1,242 posts have been published, comprising 564,346 words. The blog has grown significantly since it was first published, and now receives a significant amount of traffic. As of February 2009, its site statistics include: 179,898 visitors per month 719,423 page views per month GB of data transferred per month In addition, it is ranked 103,322 out of 1 million websites tracked by Compete.com, has an Alexa traffic rank (see Alexa.com) of 66,107, a Google PageRank of 4, and is linked to by 72,100 different Web pages from across the Web. Given the emphasis search engines place

18 on links to a site (Brin and Page, 1998), this is one reason for its success. Indeed, the importance of the search engines cannot be overstated; of the 2,942,379 visitors the blog has received in its life to date, 63.1% came from search engines (with the vast majority coming from Google). Using data from Google Analytics, figure 1 shows the frequency with which individual posts have been accessed across the lifetime of MobileMentalism, ranked according to popularity. Figure 1 Long Tail of posts, ranked by popularity As can be seen, the result shows a perfect Long Tail: the Long Tail of content for MobileMentalism.com. As such, the same power laws that describe the popularity of content within large Aggregators are evident in the content offered by MobileMentalism. This is to be expected, however, as many previous studies have shown Zipf s Law to be apparent in text of all kinds (see Mitzenmahcer, 2004).

19 Of more interest is the Long Tail that appears in the set of Active Search Terms. Figure 2 shows the frequency with which the ASTs were used to find individual posts in MobileMentalism, again ranked according to popularity. Again, we see a classic Long Tail, with the top 10 search terms appearing 12% of the time, and the long tail of search terms (a staggering 26,345 different ones) comprising the remaining 88%. Figure 2 Long Tail of active search terms, ranked by frequency As is evident in both figures, there is a distinctive power law in both curves, implying that the same forces that shape the Long Tails seen in Aggregators also apply to blogs. The following section therefore focuses on each of these forces, and determines its impact in the growth of MobileMentalism.

20 4.2 Applying the three forces of the Long Tail to MobileMentalism As discussed in section 2.1, the Long Tail is an emergent phenomenon that is pervasive throughout much of science and nature. Accordingly, the fact that Long Tail curves have been found within MobileMentalism is not in itself evidence that the same forces that shape the Long Tails within Aggregators are also at work within a blog. Long Tails are evident within traditional distribution channels such as Wal-Mart s, for example. It is the shape of the Long Tail of an Aggregator s content that the three forces affect Applying the first force to MobileMentalism For MobileMentalism, WordPress, a free blogging platform, provided the tool to produce a blog at virtually zero cost. However, according to Anderson, WordPress acts as a tool of the first force (Democratizing the tools of production) only in its use in producing millions of posts across hundreds of thousands of blogs. Recall that the first force helps lengthen the tail by enabling the creation of a large amount of content from thousands of users. Accordingly, WordPress as a tool for democratizing production is applicable to blog Aggregators, such as BlogLines, Technorati, etc., but not to an individual blog. For Long Tail Economics to apply to an individual blog, it is not the individual posts that act as the content being aggregated; rather, it is the set of Active Search Terms contained within the posts. Prior to the concept of the Web search engine, there was no way for an ordinary document to be indexed by an Information Retrieval system, and so the set of ASTs was zero. Since the development of the Web search engine, however, this set has exploded for every type of document. As such, it is the combination of publishing tools, such as WordPress, and search engines that have democratized the tools of producing Active Search Terms for individual blogs.

21 4.2.2 Assessing the effects of the First Force on a blog Figure 3a Number of posts per month Figure 3b Cumulative Total of posts per month The effects of the first force on a blog s set of ASTs can be seen graphically by analyzing the content produced over time and the ASTs that were used to locate that content. Using data gathered from the database used by MobileMentalism to store its content, figures 3a and 3b show the number of posts published on MobileMentalism each month, and the cumulative total of posts published, respectively. The effect this has on the set of Active Search Terms is shown in figure 4 (below), which shows the number of unique Active Search Terms used to find the blog from Google each month (blue and left scale) overlaid with the cumulative total of posts that were written during the same period (red and right scale). The correlation between the two figures is strong, at Figure 4 therefore shows that as more content is written, so the set of Active Search Terms grows, which in turn acts to lengthen the tail of this set, as is evident from figure 2. The first

22 force of Long Tail Economics therefore acts to lengthen the tail of ASTs within an individual blog. Figure 4 - Number of unique Active Search Terms over time, and cumulative total of posts, showing how the tail has lengthened as posts were created Applying the second force to a blog The second force of Long Tail Economics applies to the blog s distribution channel. Recall that for a blog, this is the set of links coming into the blog, either from other Web pages, or from search engine queries. Developing a good distribution channel relies on a combination of strong content, to attract a human reader s attention, and Search Engine Optimization (i.e. optimizing HTML to make the Web page appear more relevant to a particular search term), to attract a search engine s attention. MobileMentalism employs both strategies, with high quality content having attracted 72,100 links from other Web sites (Yahoo, 2008), and a site structure optimized for

23 search engines that ensures it ranks highly for search terms specific to mobile phones (such as best camera phone and Sony Ericsson W810i, which are each currently ranked fifth in Google). The position of a result from a search engine directly affects the probability of that result being clicked on by the user (Chaffey, 2006); as such, the higher in the search engine rankings a blog is placed, the more people will come to the site using that search term. This has the effect of increasing the size of the distribution channel, as more people visit the blog for each AST. Figure 5 shows the number of visitors to the blog each month from Google divided by the number of ASTs used each month. The resultant graph shows how many times individual search terms were used each month in total. As can be seen, this ranges from one visitor per search term in the early days of the blog, rising to 2.5 visitors per search term more recently. In other words, more people are using the same search term to find the blog over time. The effect of the second force of Long Tail Economics is therefore to fatten the tail of ASTs, as shown in figure 5.

24 Figure 5 - Number of visitors per keyword over time, showing how the tail has fattened as the blog rose higher in Google Applying the third force to a blog A key component of the third force of the Long Tail is the filter, which drives people further down the tail, ensuring that people locate the item they are interested in. Without the filter, their niche item of interest becomes lost in a sea of irrelevance. Large Aggregators employ their own filters, but these employ complex sophisticated algorithms beyond the reach of the ordinary blogger. However, search engines act as the blog s filter, helping to connect the supply of posts from the blog with the information demand from users, represented by the set of ASTs. Search engines, then, are external filters. Clearly, relying on an external filter runs the risk of the user finding the information they seek from another site should they have cause to return to the search engine if they did not find what they were looking for internally (hence the need for good information architecture).

25 However, without the ability of search engines to drive people to the niche posts they wish to view, MobileMentalism and other blogs like it would literally be half the blog they are today. The effect of the third force of the tail should therefore be to drive demand further down the tail of ASTs than would exist without an effective search engine ranking algorithm. Work on providing evidence for this is ongoing and will be published shortly. 4.3 Summarizing the effects of the three forces on a blog To summarize, the effects of the three forces of Long Tail Economics on a blog are as follows: Force 1: Search engines democratize the tools of producing ASTs The first force of Long Tail Economics lengthens the tail of ASTs within an individual blog. Force 2: SEO and high quality content democratize the tools for distributing content through higher search engine rankings. The second force of Long Tail Economic fattens the tail of ASTs Force 3: Search engine algorithms connect the supply of Potential Search Terms with the demand in ASTs The effect of the third force of the tail is to drive demand further down the tail of Active Search Terms.

26 The combination of the three forces acts to increase the amount of visitors from a search engine over time. However, there is a caveat, as discussed in the next section. 5. THE LIMIT OF THE LONG TAIL OF ASTS FOR THE INDIVIDUAL BLOG As can be seen, the number of ASTs used to find MobileMentalism in figure 4 has actually tailed off, and is currently in decline. The reason for this is that the posting schedule for the blog has slowed down recently; thus, with fewer posts being written, there is a slow-down in the growth of the Active Search Terms. However, this does not explain why the number of Active Search Terms is actually shrinking. Further analysis reveals it is the finite lifetime of an AST that is to blame. An Active Search Term only remains active while users are still using that term to locate the blog. Once the term is no longer used, it reverts back to being an Inactive Search Term. One reason why an AST reverts back is because the product or information that the AST relates to has become obsolete, a situation that is all too common in the mobile phone market. Consequently, the content that many users were searching for when the blog started in 2005 is no longer being searched for today, as the majority of mobile phones that were written about then are no longer sold. The corresponding ASTs that were used back then are therefore not being used today, and given that the posting schedule has slowed down, the ASTs that have reverted back to ISTs are not being replaced by new ASTs fast enough to maintain the length of the tail. Thus, the blog has reached its current limit of ASTs from Google, and with fewer posts being written, the active distribution channel is shrinking, as fewer people are coming to it from ageing links and ageing search terms.

27 This shows that although the set of ASTs and links provides a distribution channel, it is one that must be regularly maintained, as it ages in terms of activity before eventually sliding into inactivity through irrelevance to the user rather than the search engine. Long Tail Economics may describe itself as the economics of a market without shelves, but for the individual blogger, it is still a market with a finite shelf-life. The lesson from this to the blogger is to write a consistent regular stream of posts, and pay attention to the longevity of the content being published. Too much content that will soon be yesterday s news constrains the size of the active distribution channel, as its older boundary is being constantly eroded by irrelevance even as its newer boundary is extended. As such, a balance is required between news-type articles with a limited shelf-life, and more general topics, such as how to articles, which will remain relevant for a longer period of time. A painfully clear example of this can be seen in the mobile phone blog MobileTracker.net, which stopped publishing on March 28 th 2007 (apart from one post on June 29 th when the iphone was launched). As the Alexa chart in figure 6 shows (from Alexa.org s Traffic Rank feature), its daily PageViews have been steadily decreasing ever since. However, it is not that the blog is seen as irrelevant by the search engines Google, for example, still gives it a PageRank of 6 it is just that the distribution channel has decayed as the blog s users have seen it as irrelevant, causing the ASTs to slowly turn back into ISTs.

28 Figure 6: Alexa traffic history of MobileTracker.net over the past three years. 6. CONCLUSION The paper has presented the Aggregator Blog Model, a new model of the blog as an aggregator of search terms that can be used with Long Tail Economics to understand the forces that drive and constrain a blog s growth. By applying 3 years worth of Web analytics data to the ABM, the research has validated the ABM model and shown the effects that the three forces of Long Tail Economics have on a blog. The research has also shown how the Long Tail of search terms has a limit, defined by the interest in the set of ASTs that users show. This limit constrains the growth of the blog, as over 50% of the blog s traffic will come from ASTs. Accordingly, ASTs need to be kept fresh in order to create an efficient distribution channel that is optimized according to the time that an individual blogger can devote to their blog.

29 Future work is currently being carried out aimed at a more comprehensive study of ASTs and ISTs from across several blogs, with the aim of creating a definitive business model for blogs that harnesses the three forces of the Long Tail. 8. REFERENCES Anderson, C. (2006). The Long Tail: How endless choice is creating unlimited demand. Random House Business Books. Brisco, B., Odlyzk, A. & Tilly, B. (2006). Metcalfe s Law is wrong. IEEE Spectrum, 43, pp22-31, Chaffey, D. (2006, August). SEO learning / lessons from the AOL data leak. Dempsey, L. (2006, April). Libraries and the Long Tail Some thoughts about libraries in a network age. D-Lib Magazine, 12(4), doi: /april2006-dempsey Flickr (2007, November), Krauss, J. (2005, March). The long tail of software. Millions of markets of dozens. Bnoopy, Lenssen, P. (2007) % of searches are new everyday. Google Blogoscoped, MacIntyre, D. A. (2009, February). The twenty five most valuable blogs. 24/7 Wall St., Manber, U. (2007, July). Search is a hard problem. Supernova 2007, July 17 th 2007, O Reilly, T. (2005, September). What is Web 2.0 Design patterns and business models for the next generation of software. O Reilly,

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