A contenda sobre cloud computing parece que continua. Nicholas Carr retomou sua crítica ao Tim O´Reilly ontem no seu blog com o post abaixo!
This week’s pissing match – I mean, spirited conversation – between Tim O’Reilly and me regarding the influence of the network effect on online businesses may have at times seemed like a full-of-sound-and-fury-signifying-nothing academic to-and-fro. (Next topic: How many avatars can dance on the head of a pin?) But, beyond the semantics, I think the discussion has substantial practical importance. O’Reilly is absolutely right to push entrepreneurs, managers, and investors to think clearly about the underlying forces that are shaping the structure of online industries and influencing the revenue and profit potential of the companies competing in those industries. But clarity demands definitional precision: the more precise we are in distinguishing among the forces at work in online markets, the more valuable the analysis of those forces becomes. And my problem with O’Reilly’s argument is that I think he tries to cram a lot of very different forces into the category “network effect,” thereby sowing as much confusion as clarity.
Ten years ago, we saw a lot of fast-and-loose discussions of the network effect. Expectations of powerful network effects in online markets were used to justify outrageous valuations of dotcoms and other Internet companies. Disaster ensued, as the expectations were almost always faulty. Either they exaggerated the power of the network effect or they mistook other forces for the network effect. So defining the network effect and other related and unrelated market-shaping forces clearly does matter – for the people running online businesses and the people investing in them.
With that in mind, I’ve taken a crack at creating a typology of what I’ll call “network strategies.” By that, I mean the various ways a company may seek to benefit from the expanded use of a network, in particular on the Internet. The network may be its own network of users or buyers, or it may be a broader network, of which its users form a subset, or even the entire Net. I don’t pretend that this list is either definitive or comprehensive. I offer it as a starting point for discussion.
Network effect. The network effect is a consumption-side phenomenon. It exists when the value of a product or service to an individual user increases as the overall number of users increases. (That’s a very general definition; there has been much debate about the rate of increase in value as the network of users grows, which, while interesting, is peripheral to my purpose.) The Internet as a whole displays the network effect, as do many sites and services supplied through the Net, both generic (email) and proprietary (Twitter, Facebook, Skype, Salesforce.com). The effect has also heavily shaped the software business in general, since the ability to share the files created by a program is often very important to the program’s usefulness.
When you look at a product or service subject to the network effect, you can typically divide the value it provides to consumers into two categories: the intrinsic value of the product or service (when consumed in isolation) and the network-effect value (the benefit derived from the other users of the product or service). The photo site Flickr has, for example, an intrinsic value (a person can store, categorize, and touch up his own photos) and a network-effect value (related to searching, tagging, and using other people’s photos stored at Flickr). Sometimes, there is only a network-effect value (a fax machine or an email account in isolation is pretty much useless), but usually there’s both an intrinsic value and a network-effect value. Because of its value to individual users, the network effect typically increases the switching costs a user would incur in moving to a competing product or service or to a substitute product or service, hence creating a “lock-in” effect of some degree. Standards can dampen or eliminate the network-effect switching costs, and resulting lock-in effect, by transforming a proprietary network into part of a larger, open network. The once-strong network effect that locked customers into the Microsoft Windows PC operating system, for instance, has diminished as file standards and other interopability protocols have spread, though the Windows network effect has by no means been eliminated.
Data mining. Many of the strategies that O’Reilly lumps under “network effect” are actually instances of data mining, which I’ll define (fairly narrowly) as “the automated collection and analysis of information stored in the network as a byproduct of people’s use of that network.” The network in question can be the network of a company’s customers or it can be the wider Internet. Google’s PageRank algorithm, which gauges the value of a web page through an analysis of the links to that page that exist throughout the Net, is an example of data mining. Most ad-distribution systems also rely on data mining (of people’s clickstreams, for instance). Obviously, as the use of a network increases, the value of the data stored in that network grows as well, but the nature of that value is very different from the nature of the value provided by the network effect.
Digital sharecropping, or “user-generated content.” A sharecropping strategy involves harvesting the creative work of Internet users (or a subset of users) and incorporating it into a product or service. In essence, users become a pool of free or discount labor for a company or other producer. The line between data-mining and sharecropping can be blurry, since it could be argued that, say, the formulation of links is a form of creative work and hence the PageRank system is a form of sharecropping. For this typology, though, I’m distinguishing between the deliberate products of users’ work (sharecropping) and the byproducts of users’ activities (data mining). Sharecropping can be seen in Amazon’s harvesting of users’ product reviews, YouTube’s harvesting of users’ videos, Wikipedia’s harvesting of users’ writings and edits, Digg’s harvesting of users’ votes about the value of news stories, and so forth. It should be noted that while sharecropping involves an element of economic exploitation (with a company substituting unpaid labor for paid labor), the users themselves may not experience any sense of exploitation, since they may receive nonmonetary rewards for their work (YouTube users get a free medium for broadcasting their work, Wikipedia volunteers enjoy the satisfaction of contributing to what they see as a noble cause, etc.). Here again, the benefits of the strategy tend to increase as the use of the network increases.
Complements. A complements strategy becomes possible when the use of one product or service increases as the use of another product or service increases. As more people store their photographs online, for instance, the use of online photo-editing services will also increase. As more blogs are published, the use of blog search engines and feed readers will tend to increase as well. The iPhone app store encourages purchases of the iPhone (and purchases of the iPhone increase purchases at the app store). While Google pursues many strategies (in fact, all of the ones I’ll list here), its uber-strategy, I’ve argued, is a complements strategy. Google makes more money as all forms of Internet use increase.
Two-sided markets. Ebay makes money by operating a two-sided market, serving both buyers and sellers and earning money through transactional fees imposed on the sellers. Amazon, in addition to its central business of running a traditional one-sided retail store (buying goods from producers and selling them to customers), runs a two-sided market, charging other companies to use its site to sell their goods to customers. Google’s ad auction is a two-sided market, serving both advertisers and web publishers. There are a lot of more subtle manifestations of two-sided markets online as well. A blog network like the Huffington Post, for instance, has some characteristics of a two-sided market, as it profits by connecting, on the one hand, independent bloggers and, on the other, readers. Google News and even Mint also have attributes of two-sided markets. (Note that the network effect applies on both sides of two-sided markets, but it seems to me useful to give this strategy its own category since it’s unique and well-defined.)
Economies of scale, economies of scope, and experience. These three strategies are also tied to usage. The more customers or users a company has, the bigger its opportunity to reap the benefits of scale, scope, and experience. Because these strategies are so well established (and because I’m getting tired), I won’t bother to go into them. But I will point out that, because they strengthen with increases in usage, they are sometimes confused for the network effect in online businesses.
None of these strategies is new. All of them are available offline as well as online. But because of the scale of the Net, they often take new or stronger forms when harnessed online. Although the success of the strategies will vary depending on the particular market in which they’re applied, and on the way they’re combined to form a broader strategy, it may be possible to make some generalizations about their relative power in producing competitive advantage or increasing revenues or widening profit margins in online businesses. I’ll leave those generalizations for others to propose. In any case, it’s important to realize that they are all different strategies with different requirements and different consequences. Whether an entrepreneur or a manager (or an investor) is running a Web 2.0 business (whatever that is) or a cloud computing business (whatever that is), or an old-fashioned dotcom (whatever that is), the more clearly he or she distinguishes among the strategies and their effects, the higher the odds that he or she will achieve success – or at least avoid a costly failure.