The Digital Economy. Tim Jordan
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– and existentially, in the sense that without a successful search engine advertising is irrelevant since no company will survive for very long. Once Google had learned how to read the community of the WWW and ally this to the data flow derived from its searchers, it could then make the profitable leap to advertising, while still offering its core ‘value’ of search as a free service. At Google, the addition of monetisation is materialised in new practices of analysing users, as well as in auctions, money transfers and bookkeeping. Fundamentally, Google’s digital economic practice is based on its ability to read a community and then pass that reading through recursions that both identify better search results and deliver targeted advertising.
Search as an Economic Practice
If Google is not the only search company, and not the only digital economy company, then is this understanding of its economic practices being dependent on and deriving their primary value from communities, groups or collectives specific to Google or to search companies? In relation to digital economic practices generally, subsequent chapters will examine cases other than search in order to both complicate and confirm how far Google is an exemplar of more general practices. But here it will be worth looking briefly at a number of other search engines.
Bing is the next in line for the most search queries in many parts of the world, with around 10 per cent of all search queries worldwide, though this varies according to region, from around 30 per cent in North America to 3 per cent in the Asia Pacific (Statista 2017). Bing is different to Google in a number of ways, though it retains the element of reading the World Wide Web, with somewhat different strategies for achieving a good read. Primarily, Bing aims to be a semantic search engine by building rankings related to keywords. Once a complex set of rankings is created it is supplemented with a measurement of links to deliver an answer to a query. While not a great deal of detail is available, it seems Bing’s keyword table is generated by looking at the nature of websites and how many links there are to their semantic content, creating a hierarchy based on a particular kind of web linking. Indicative of this is Bing’s advice to websites about how to make themselves more visible to its search, which includes providing clear and discrete keywords that are strongly related to the site’s content (Moffit 2014). Though implemented differently, Bing maintains a strategy of reading the WWW. Once read through keywords the semantic content is supplemented with further backlink searches, not entirely unlike Google’s processes, to generate answers to search queries. Bing notably uses this system to provide search that takes on non-text content, attempting to provide effective search results for images, video, sound and so on.
Bing’s monetisation broadly follows the targeted advertising model. It uses its search abilities to feed advertisements related to the content of a search. Again, while advertising appears to be the primary economic practice, it is so only in terms of generating income, with the full economic practice relying on the reading of the WWW and its various commercial and community networks. Microsoft’s early 2018 financial report established that Bing revenue rose by 15 per cent in 2017 to a total of $1.8 billion (Javed 2018). While a significant income, available figures make it hard to judge if Bing is profitable or a loss-leader that Microsoft is subsidising out of the profits made from commodities like its operating system and Office software.
Another major search engine is Baidu, which in 2017 had around 70 per cent of all searches in China, though only around 1 per cent of searches worldwide (CIW Team 2017). One of Baidu’s founders, Robin Li, had a similar idea to Page and Brin’s of basing search on an academic-citation-like search of WWW links. Li first worked on this idea with a few companies in the United States, but these efforts led nowhere. He returned to his native China to start a number of ventures, eventually setting up Baidu as a search engine (Levy 2011: 24–6). After an initial period of development, Baidu had to compete with Google in China during its establishment (later Google withdrew from a China-based search engine, though access to it can still be gained through virtual private networks and other cloaking devices2). While the details remain secret, it is likely Baidu used a similar overall strategy to Google in reading the WWW through backlinks, though it also used its knowledge of China’s languages to develop searches more useful to Chinese users (Fung 2008: 145–7).
To establish itself in competition with Google, Baidu had to ensure it generated enough traffic to achieve the secondary recursions of data needed to supplement the reading of the WWW. It also had to achieve sufficient traffic to gain enough advertising to gain enough income to, in turn, gain enough investment (and then profit) to grow. While this appeared uncertain for some time, Baidu adopted a tactic not available to Google by providing listings of mp3 music files, many of which were pirated. With some implicit protection both from China’s then lax approach to protecting Western copyright claims, and from the Chinese government, who may well have been seeking a Chinese search engine, Baidu was able to build up a significant following in China (Fung 2008: 145–7; So and Westland 2010: 41–59). After Google withdrew from the country entirely, Baidu’s dominance was secured.
Monetisation proceeds similarly on Baidu as it does on Google and Bing, through selling keywords that lead to targeted advertising driven by a reading of the WWW community and the records of searchers, but with a focus on China and South-East Asia. One notable difference is that at times Baidu has mixed paid-for advertising with ‘normal’ search results. This can lead to it being difficult to tell whether a search result is paid for or not. Similar issues have appeared on Google and other search engines, raising the key issue of trust. As we have seen, Google’s strategy has been to mark and separate out paid-for search results, whereas Baidu has succeeded with a more obscure presentation; but within their practices each search engine has to manage the trust of its users in their search results (So and Westland 2010: 55).
There are a range of other search engines, some with specialist purposes. For example, DuckDuckGo aims to protect privacy. It does not follow users and their searches to record them and build a profile of use. To answer a search query it primarily acts as an aggregator, building on over 400 other existing forms of answering online queries. These 400 sites include many wikis and other collections of data (such as game Digimon’s wiki, many ‘cheatsheet’ sites, and sports sites, for example using Sportsradar for some game scores). The major search engines Bing, Yahoo and Yandex are also mixed in among the 400 sources. Though little is known of how it works, DuckDuckGo states that it has its own web crawler that automatically searches the Web collecting links and information on which to base search results (DuckDuckGo 2018). Monetisation is achieved through perhaps the simplest form of targeting, as explained by DuckDuckGo founder and CEO Gabriel Weinberg: ‘If you type in “car” you get a car ad, if you type in “mortgage” you get a mortgage ad … We don’t need to know about you or your search history to deliver a lucrative ad’ (cited in Burgess and Woollaston 2017). These ads are drawn from the Bing and Yahoo Search Alliance.
DuckDuckGo represents a minimum in reading a community, but it still has to do some reading to generate its results, both through its own crawler and by relying on community-created resources in its 400 sources and on the readings Bing, Yahoo and Yandex make of the WWW. Stripping money to its barebones of connecting a search term to an advertisement makes clear the fundamental relationship: search first, ads second. DuckDuckGo does not involve the complexities of Google, Bing or Baidu, but strips search back to serve a specific ethical purpose, emphasising that along with community and trust, digital economic practices raise issues of privacy.
Community, Trust and Privacy
Search engine corporations have connected two distinct practices in search and advertising. Search existed before digital advertising practices (though, of course, not before advertising) and could exist without them; search was the magnet to be subsequently monetised. As we have seen throughout this chapter, search is based on automated readings of communities and collectives allied to further exploration of sociality among searchers once enough users have been attracted whose behaviour can be recorded. Advertising is then reliant on the search.
This also reflects a wider societal change that Turrow has called the transformation of the advertising industry into a surveillance industry (2011: 1–12).