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Synopsis - The Web 2.0 financial
model: user-generated content attracts visitors, which boosts search rank,
which attracts more visitors, which boosts advertising revenue.
- The
traffic-to-dollars business model resulted in unintended social
phenomenons: social-networks and blogs, which themselves spurred new web
design goals that focus on encouraging visitor participation and
contribution.
- The un-monetized "waste byproduct" from these websites
is a stockpile of user-generated content, such as photos, videos,
paintings, drawings, stories, commentary, and opinions.
- To make use
of all that flotsam, a suite of semantic analysis tools is being
developed to organize and structure them in ways that help search engines
produce better results. This is called Web 3.0—or, "the semantic web."
- The consequence of Web 3.0 will be the unanticipated financial
incentive for websites to monetize their content, rather than just
host it to attract visitors.
- The opportunity to make money from
user-generated content will give incentive to visitors to produce "better"
content, and for websites to be more discerning about the content they
receive, which affects both the social and economic landscape of the
internet.
- The ways this type of transformation may evolve is the
challenge for technologists and entrepreneurs, who must be both visionary
in how the future may appear, but cognizant of missed opportunities of the
past.
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In this article, I turn my attention to the economic effects
from new developments in search technology. As I'll make clear soon,
"search" is merely a spark that launches a much larger fire: the social
aspects of the web. And whenever the subject of "social networks" mixes
with economics, the spotlight focuses squarely on the consumer. I
presented a simple example of this in part one of this series by showing that more
consumers both buy and sell photos than do professional photographers or
stock photo agencies, which itself has caused a dramatic shift in how
licensing is done. In fact, the word 'consumer' is really a misnomer in
this era, because they don't just consume, they produce.
This phenomenon is not as readily visible to the undiscerning eye,
because the greatest proportion of these transactions is done on a
peer-to-peer basis (directly between the photographer and the buyer). In
fact, asymmetric analysis shows that approximately 80% of photo media
content is acquired directly from the photographer, and images are found
primarily from search engines. This mechanism creates economic incentive
for those on both sides of the transaction: the buyer uses search engines
to find what they want, so the seller tunes his content to conform to
the kind of information that search engines look for.
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The "missed opportunity" from this shift has been primarily the lack of
technical infrastructure to support broad peer-to-peer licensing. Only the
traditional licensing methods are available on a broad scale, which
requires photos being submitted to a company (a "stock photo agency"), who
then licenses them to buyers. Each individual agency operates entirely
independently from others, and none of them have prominent placement in
traditional search engine results, so only a small percentage of potential
buyers ever end up on those sites. The majority of them go directly to the
websites of the photographers themselves (because the search engines index
them), but most of these photographers are unaware that they could make
money licensing their content. Indeed, even social-networking sites are
unaware of the financial opportunities to license the content. The net
result is that few photos are actually monetized, and of those that are,
the pricing is arbitrary and spurious. It's estimated the $15-20B of
licensing is done on a peer-to-peer basis, and countless more dollars are
simply unrealized due to this inefficiency.
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I should clarify that even though my first article demonstrated this type
of media growth in the photo industry, the broader market of all types of
media is evolving similarly. In fact, events in online photography serves
as an excellent "base case" to help forecast economic effects for other
media types, such as music, video, line-art, books, and so on. What all
these have in common, and which everyone has known for years, is that
non-professionals create this content as well. And, people do so without
necessarily intending (or expecting) make money with it. As noted earlier,
I call this class of content creators, "consumers," even though they
also "produce."
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By examining the photo industry, we can extrapolate what might happen with
the industries of other media types. Accordingly, photography has these
important characteristics:
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- Everyone does photography on a regular basis in large
quantities.
- People upload their photos the internet more frequently
and in higher volumes than other media types.
- Economically, photos
are used more than any other media type in publishing of both commercial
and editorial content. (Thus, economic value.)
- More people can create
"salable content" with less expertise, less effort, and greater speed than
other media types.
- The high quantity and low price per unit of
licensed images equate to a lower barrier of entry for both buyers and
sellers.
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Forecasting the economic future of media on the internet is difficult
because it isn't clear whether the same lack of awareness will plague
other media types as it has with photography. There's already been a major
economic shift in the photo industry as a result of the internet, and
evidence suggests that similar economic changes are happening with video
and music as well. As technology for creating content of any type
improves, as does self-publishing of this content, the economics are
likely to follow the trend set forth by the photo industry, and consumers
will find themselves in a position to make money with their content.
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The next phase of internet search technologies and standardized
communication protocols may inadvertently help. As we've seen with the
evolution of the internet to date, whose economic growth came from
unintentional consequences, we can learn from how those circumstances came
about, and use them to forecast how the future economics might also take
shape.
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The goal and challenge for media-oriented industries of all types is to
build a more structured, formal, internet-wide framework that handles
content licensing in general, regardless of the media type, or who the
buyers and sellers are, and establish these foundations before consumers
set precedents that are harder to unravel, which could deflate a content's
value before it has a chance to flourish.
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The good news is that some developments are already under way. But, to put
them into context, we need to understand today's business model, and how
it evolved into what we currently work with. What we'll find is a very
tight sequence that starts with technical innovation, followed by social
adaptation, which affects financial incentives, which comes full circle to
innovation again. This feedback mechanism of constant reinforcement and
revision is an economic truism. Since the technology is already underway,
and some of the social fabric is similar taking shape, the only question
that remains is how the business environment evolves with it.
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Web 2.0 Business Models Setting the
Stage
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Most people are familiar with MySpace, FaceBook, and Flickr as common and
well-known examples of social-networking sites. They are essentially
places where people sign up and contribute "content" in the form of
information about themselves, while also contributing photos, music,
poetry, writing, and ideas of various sorts. In return, they get to
socialize—learn, teach, meet, and access.
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Getting people to participate and contribute content is what is commonly
called, "Web 2.0", and most sites on the internet are so enabled. You can
visit most any blog, news organization, movie review site, shopping site,
or cooking site, and you'll probably find a way to contribute something,
whether it's as simple as voting on how much you liked a book, or as
involved as contributing your own recipes, movies, photographs, short
stories, or politically biased nonsense that you hope others will agree
with.
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While this kind of activity has always been technically possible to
program into websites, these features of the web were largely ignored
until there was a business incentive to use them. That incentive came in
the form of Google's advertising network. If a site had good information,
and it was indexed well by search engines, advertisers paid more dollars
to have ads there. Consequently, for a site to get those advertising
dollars (or to sell its own products or services), it needed to be indexed
well, which means it needed more content. The easiest and cheapest way to
get content is to encourage people to contribute their content. The
incentive that sites give to consumers to contribute is the "social
rewards."
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By being smarter, funnier, cuter, or more ridiculous than others, people
get attention, and people love that. So, websites used the "social" carrot
to get people to participate, and in return, the site got its free
content, and of course, traffic. These both raise the site's raking and
boosts advertising revenue (or sales of their own stuff). Today, it's
almost unheard of that sites don't have some way for users to contribute.
Everyone wins.
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What's notable about this development is that it was unintentional.
Social websites had been around in earlier days of the net, but didn't
really gather much attention or traffic. Even of those that did, it wasn't
easy to make any money from those users. No one bought anything, and they
wouldn't pay subscription fees. So, having millions of users did nothing
but cost the company money in technical infrastructure (which itself was
vastly more expensive than it is today). Companies that had stuff to sell
typically didn't garner much traffic, except for dating sites, like
match.com.
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It wasn't until Google introduced its auction-based advertising model that
inadvertently rewarded highly-trafficked sites with unanticipated revenue
did a financial incentive exist. There was then an instant awareness that
social-networks is where the money is.
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Yet, it was never Google's intention to create social networks or any
other kind. In fact, it didn't intend to affect the nature of the internet
at all. It just wanted to create a model of analyzing the internet as it
is (or was) for purposes of setting auction-based advertising rates. They
did not anticipate that their very act of analyzing data actually changed
the very data itself. (This is a perfect example of Heisenberg's
Uncertainty Principle.) Indeed, not only has the data changed because
Google observes it, they created a feedback mechanism where the more they
looked at data (and thus, reported their observations by way of search
rankings), the more the data itself morphed into to the kind that people
thought Google wanted to see. This, in turn forced Google to change how it
looked at the data, because people were manipulating the content on their
sites to artificially bump their rankings higher.
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And so it goes to this day: websites and search engines are in an endless
cat and mouse game, where sites try to get higher search rankings, and for
Google to maintain a plausible ranking system that users can trust to be
objective when they search. This credibility is required for advertisers
to trust it.
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This underscores these basic, fundamental socio-economic principles:
- Financial incentives promote user behaviors.
- Examining user
behaviors to calculate financial incentives causes sites to filter those
behaviors that optimize financial returns.
- The constant feedback
mechanism and subtle refinement of behaviors and economics creates a state
of unpredictability.
- The unpredictability invokes the Law of
Unintended Consequences, which yields a new economic model.
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This begs questions: What's next after Web 2.0? And what are the potential
byproducts from whatever that is? More social networks? Or
something else? Our objective here is to anticipate future financial
opportunities without forgetting that the feedback mechanism produces
unpredictable results. Also, the Law of Unintended Consequences suggests
that examining user behavior changes those very behaviors, which itself
often forms the basis for new developments and incentives. (Knowing the
future will affect your behavior, thereby changing the future.) But, as
any good entrepreneur and venture capitalist know (er, should
know), the goal isn't to predict or (worse) to control or shape the
future, but rather, to anticipate the parameters that are most likely
to frame that future.
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The answer to "What's next?" is easy: Web 3.0. In inner circles, this is
called, "The Semantic Web." That is, the content on the web that was
generated during the Web 2.0 era will be more intelligently
analyzed than before. In essence, the data is not just indexed as it is
today, but is being better understood for its semantic meaning.
This very core nugget of change sparks a feedback mechanism on a very
large scale, which will transform the economic foundations of the
internet, much the same way the web itself changed the world.
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For example, let's say you have a weird and embarrassing rash. Today,
searching relies on brute-force matching of search terms, such as "weird
rash" or "red rash". Type that into Google, and the results you get are
pages that happen to contain both words. Though the pages themselves may
be ranked according to popularity (the Mayo Clinic's site may rank higher
in search results than some guy's blog), you still have to sift though
more than just the first set of results to find what you're really looking
for.
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You could do an image search for "red rash," but search engines don't
really know what's in the content of photos. If you were to do such
a search on images.google.com, you'll get photos of red rashes, but this
isn't because Google analyzed the photos. They match because the photos
happen to have the words "red" and "rash" in the image's filename. E.g.,
red-rash.jpg. (Google also looks at the file's pathname as well
as the filename.) If you try it, you'll see there are photos of every
possible kind of red rash you can get, most of them having nothing in
common with the others, nor are they sorted or ranked in any intelligible
way. The results are merely arbitrary listings of all matches for images
with properly-named files. Google relies on the fortunate-but-useful
naming convention that some people happen to use when naming their photos:
that they name their files according to their content. In practicality,
this technique is spurious and nearly useless, but it's the only thing
they can go on for the moment. As it is today, you have to examine each
photo and see whether that looks like your rash, and then examine
each of the pages that the came from to determine what the rash is.
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That google relies on photos' filenames to match their content is not just
unreliable, it's not even complete. Statistically, most people don't
change the filenames of their photos; they leave them as they were from
the camera, such as DSC1004.JPG. Photos with those names
are never going to come up as a search result for any search, let
alone those that could be potential (and valuable) matches for relevant
searches, were the search engine to genuinely know what it was searching
for. So, there's a lot of photos out there that may be of red rashes, but
they aren't found because there's nothing about them that indicates that's
what they are.
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Pro photographers may be asking, "what about metadata? What about the
keywords that I apply to photos? Why doesn't Google look at that?" They
don't for the same reason Google no longer looks at the "keywords" tag on
html web-pages themselves when compiling results for general searches:
websites learned they can game the system by stuffing these attributes
with unreliable data.
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People can still game the system to some degree by naming their image
files accordingly, but for the moment, doing so doesn't really reward the
behavior very much. It would only yield sporadic results because Google
doesn't sort or prioritize results according to any sensical pattern that
matters to searchers. And there's currently no other benefit to naming a
photo improperly. After all, why would someone name a photo,
sexy-woman.jpg if it's just a red rash?
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One reason to do so would be if there were financial incentive, say,
advertising revenue if your site were to get more traffic. This would
provide incentive to name photos sexy-woman.jpg, even if it's a
photo of a rash. As you can imagine, this would completely ruin Google's
current image search feature, which is why all search engines are sort of
stuck in a corner with image search results: unless they can assure some
degree of reliability of results that cannot be gamed once there was a
financial incentive to do so, it's best to leave the system as it is—nearly useless, but not so much so that people don't tinker with it. Yet,
there's the very paradox: because it's still the only game in town, people
tinker a lot, and it's the source of most image searching on the
internet today.
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So, unless one can actually, reliably determine the content of a photo,
image searches will have to remain circumstantial, unscientific, and
without reward.
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We can envision what the economic effects would be in a Web 3.0 world,
where there was a better semantic understanding of media content beyond
just text. Using new algorithms that can determine the content of photos,
for example, you may one day search for "red rash" and get a lot more
relevant search results than before, simply because the existing content
on the web is better understood.
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Image-recognition algorithms are evolving in many ways, and look for very
different aspects of images to determine characteristics, genres,
attributes and, ultimately, content. A couple examples that I often cite
in my blogs are tineye.com and picscout.com, which examine photos and find
"similars" on the net based on pattern recognition by identifying photos
(and portions of them) by assigning a unique "fingerprint ID." Another
site, xcavator.net finds photos based on conceptual elements and can do so
using specific keywords, like "train" or "window." Using this technique is
even better than a text search for "red rash" or "weird rash" because the
image recognition algorithm can do a better job of analysis and ordering
results accordingly to proximity. Here, you could just take a picture of
your rash, upload it to the web, and you'll get search results that are
not only more relevant, but they can see similarities and differences that
humans simply can't, or would overlook by an untrained eye.
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And image-recognition is only one example—there is also
music-recognition technologies that do the same sort of thing. Shazam (www.shazam.com) is a site based on
music-recogition technology that is evolving its own economic model by
itself. Sing a portion of a song you like into the phone (connected to the
company), and it'll tell you what song it is. That's just one application
of its technology, and the company is partnering with many different,
diverse businesses, many having nothing to do with the web or "search
engines" directly, but it is nonetheless a search technology with
widespread economic effects that are, so far, unanticipated by industry
watchers. A similar-but-different development from Widisoft (www.widisoft.com) does more
detailed analysis of music for conversion between music file formats that
assist musicians and sound engineers to better compile and arrange musical
components of a song.
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The social (and, by extension, economic) ramifications of all these
developments should be self-evident to anyone that works in an internet or
media company. Economic predictions, on the other hand, would be premature
without understanding how these technologies would evolve both technically
and socially.
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The first question most people ask is why aren't these websites (or their
technologies) more widely deployed, or even more usefully employed,
especially by larger search engines? Several reasons.
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First, these algorithms are still pretty young; recognizing patterns is a
difficult and imprecise science. Of course, so is traditional text search,
but the difference between the two is rather substantial. (Just matching a
photo with another photo—or a song with another song—isn't yet
sufficient for a "semantic" search.)
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Second, pattern-recognition of content is only the beginning. Information
about what those patterns are and what they mean still need to be seeded,
and that information needs to start from humans. This isn't a major
barrier, as the current Web 2.0 content on the internet has a great deal
of that info already. But, its vast size and disorganization means that
time is required to harness it properly. During this process, major search
engines are left to guess at semantic meaning from the text on the same
page as a photo, for example, which is similarly error-prone and
unreliable as their current method of file-naming, though a notch better.
The lesson we learned about examining data altering user behavior must be
heeded strongly here: if there's incentive to "lie" or manipulate data,
search engines will lose credibility.
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Thus, the third problem: trustworthy information about content. Because
there will eventually be a financial incentive to provide trusted and
controlled data feeds about various content types, new methods need to be
established to "search and rank" the sources of information. That
sounds similar to traditional text search and rankings of websites, but in
this case, it's not the site's credibility at stake; it's the credibility
of the data found on the site. Or rather, the information about the
content. (That is, the description of the rash in the photo may need to be
ranked, which may be independent of the site that hosts the photo.) Unlike
the text on a site that can be interpreted and ranked—which is
closely linked to the site—photos and other media types on that same
website can be sourced from anywhere, and the data about that media may
have well come from an entirely different source. In the Web 3.0 world, it
will be much more common for crowd-sourced content to be annotated by
someone other than the content's creator. (Wiki-based sites are good
examples of this today.)
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And lastly, the three problems noted above will be difficult to do by any
one entity, since the ingredients in this recipe require
participation from many different organizations. Getting that
participation is difficult, especially since each is intimately focused on
their own small views of the world. (This is the very problem that every
player in the photo licensing industry exhibited, which is the primary
cause for its slow demise.) Having an entity that sits on top of the trees
and sees the broader economic opportunities that it can use to direct
which direction the tribe goes in hatching through the forest is not easy,
and no one is currently poised to accept such a role. It will unlikely be
a small organization, and larger companies tend not to have
entrepreneurial spirit or vision.
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Yet, these technologies continue to develop. Just as Web 2.0 evolved
relatively slowly, so too are Web 3.0 capabilities, and with them will be
leading industries that pave the way for the others by establishing
standards and protocols that set the economic wheel in motion. The
economic wheel for Web 2.0 was advertising dollars and traffic, so people
looked to Google for the parameters to design sites and user experiences
that lead to traffic, so money can be made. In the 3.0 world, there is
currently no such leader, since it isn't yet clear where the incentives
are. Nor will such a model exist without having experienced the iterative
social feedback mechanisms that are part of every economic development.
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What might that social environment look like? Let's consider your rash
again: You take a picture of it, upload it to an image-recognition site,
which matches it to a set of potential candidates, and each one checked
against a medical website that has information about the rashes, which are
then fed into a pharmaceutical website, which may list potential remedies.
If it turns out you just went camping over the weekend, you can assume
it's likely to be poison ivy and choose the appropriate remedy. If, on the
other hand, you recently visited the red light district in Bangkok, then
your spouse will be alerted, and your lawyer will be notified to accept
the divorce papers being prepared for you.
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Such possibilities would be objectionable to many, so limitations would be
naturally put into place to protect privacy. And that's just one example.
As technologies develop and new capabilities are evident, people react,
and social acceptance or rejection alters future developments. These must
take place before effective and long-standing economic models can form.
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A critical component of this social evolution of Web 3.0 is found in a
very old technology that hasn't been exploited to its potential: that of
"predictive preferences." That is, search results being ordered according
to what might be appropriate or relevant to the searcher. While many may
not be aware of it, the vast amount of raw content from the Web 2.0 world
is being analyzed by "crowd-analysis algorithms", which look at data that
people have voted on or expressed some kind of opinion about. This data is
then examined for patterns to predict how individuals might like
something, which can then be used to determine whether any given search
results should rise or fall in "relevance" ranking.
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In the music industry, there are two applications of this technology that
you may be aware of. Amazon.com has been using predictive preferences for
years, and I rely on it almost exclusively when I buy music. I let amazon
choose new albums for me based on what it knows about me: the things that
I've bought in the past, searched for, and/or rated my preferences for as
it tracks my behaviors on its site. It even looks at preferences that
aren't music related. When it offers suggestions for what I'd like, I'm
always shockingly surprised at its accuracy. (Porcupine Tree is my latest
miraculous find.)
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Another example is Pandora (www.pandora.com). People with an
iPhone were recently introduced to it this way: name a song, a band, or a
genre, and the site will stream music to you in radio-station format, all
comprised of songs that you are likely to enjoy.
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Part of how pandora does this is by applying conceptual attributes to
songs, such as "acoustic guitar solo." There are over 400 such attributes,
which the site calls "the music genome project." This requires humans to
assign such attributes to songs manually at the moment, but music analysis
isn't that hard. It isn't a stretch to envision combining these two
technologies, so that an algorithm determines attributes based on
digitized sound waves, which it can then assess and assign to other songs
in real-time.
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Hypothetically, I could rent a car in a city I've never been to and
program the radio's stations by singing a song that I happen to like. The
radio can be instantly programmed to assign stations to the preset
buttons. No more "scan button!"
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A similar "genome sequence" of photography or video has never been done
(or proposed as far as I know), but it seems as one would be inevitable,
and would lead to another step in the semantic understanding of media on
the web. When you combine the semantic understanding of content with
predictive preferences, you have a readily monetized network of resources
that, currently, no one is capitalizing on.
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Web 3.0 Business Model: It's the
Content, Stupid
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This scenario presents the potential for a pivotal economic shift of focus
for where value is: from the website to content. As my
earlier research in the photography realm revealed, the less time it takes
for a searcher to find a relevant photo from a search, the more likely it
is that the searcher will license it. There's every reason to believe that
photos are not unique to this human behavior and economic need. The
semantic web will make it easier to find content of any sort, and if the
searcher's results are also tuned to their particular preferences, it
raises the likelihood that such content would be purchased beyond the
ratio we see today. Thus, the value of content on a site goes up because
it has a higher chance of being monetized.
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Remember all those photos named, DSC1004.JPG? That's
content that is currently next to useless because it carries no semantic
meaning, and is therefore not seen or understood by current search
engines. The semantic web will eventually find all those abstract media
objects and make sense of them, adding them to the set of possible search
results. Such data exists in all media types, not just photos, making the
economic possibilities far greater than anyone has anticipated. So, once
all the "useless raw content" from Web 2.0 is semantically analyzed, it is
likely to emerge in the Web 3.0 world as "invaluable data assets" that
contribute even more to the Long Tail of internet economics.
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As the perception of content's value continues to increase, Web 3.0 sites
will have more incentive to attract those who create quality
content.
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A key indicator of this is reflected by the following quote from
this article in the New York Times:
All Web sites that rely on ads struggle to a greater or lesser extent
to convert traffic, even high traffic, into meaningful revenue. Ads
that run on Google and other search engines are a profitable exception
because their visitors are often in a buying mood. (...) Google's own
YouTube, which relies heavily, like Facebook, on user-generated content,
remains a costly experiment in the high-traffic, low-revenue ad business.
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The emphasis shown above are mine so as to underscore the pivotal facts:
visitors to search engines are in a buying mood, and while they do
click on ads on search pages, more people click on content than
they click on ads because it's the content they want. (They only
click on ads if the search results don't offer what appear to be
immediately actionable purchasing landing pages.)
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An example illustrating this socio-economic development can be found in this
story from the New York Times. Joel Moss Levinson, "a college
dropout with dozens of failed jobs on his resume," has earned more than
$200,000 by creating homemade movies that major corporations are now using
in their mainstream commercials. Where'd they find them? YouTube. The
article goes on to mention many companies getting content directly from
common consumers, rather than through traditional ad agencies, and how
this trend is reshaping many aspects of the Marketing and Advertising
industries.
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The beneficiaries of this are obviously not just limited to individuals.
While Levinson created his own content, there's quite a bit of mainstream
content from traditional media companies that can be applied to the same
business model that benefits them: make the content available for a fee.
More and more consumers are actually paying for movies and videos over the
web, which is a trend that was once predicted, but failed to materialize
for years. In fact, there was doubt that it would ever happen, because
users just got used to the net being "free." Many early sites that tried
to charge for membership found they couldn't make the numbers work. But as
users are learning that good content is harder to come by, this model is
finally becoming economically stable. And, it has a twist: users are not
just getting content for pay, but are also given further incentives to
contribute as well, such as feedback or other information about the
content they're paying for. These incentives come in the form of reduced
fees, or in the reduction of advertisements the visitor otherwise has to
see before getting to see the content. An example is found in this
article in the New York Times, where Hulu (www.hulu.com) allows
visitors to view programming with fewer ads, and encourages visitors to
vote on shows with thumbs-up and thumbs-down buttons.
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As new and different kinds of websites are built to respond to that
economic incentive, websites will continue to reinforce this behavior by
adjusting compensation and other reward systems. They'll also want
"semantic information" about that content, not just raw data, which
changes the user experience, which changes the nature of how and why
people go to websites in the first place.
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Evidence of this is already making headlines: YouTube's recent
announcement that they will now begin to enable users to purchase songs
and other content found on the site—whereas, before, such content was
only used to attract more visitors. Similarly, Flickr's relationship with
Getty Images is one where a company is cherry-picking user-generated
content from a social network and selling that very same content on a
professional photography site. Still another example is the growing basket
of online discussion forums that are converting from "free access" to
paid-for access, which is the most overt illustration where a site is
changing its business model from using user-generated content to attract
visitors, to one where that same content is used to generate subscription
fees.
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All this is part of the feedback mechanism that perpetuates unpredictable
change. As users themselves are ranked and "scored" for the various
content types they create and contribute, a phenomenon that already exists
in many forms on social networks and discussion boards, there would be an
amplification of this if there were financial incentive to raise your
rankings. Or, to lower others' rankings. This type of human behavior has
not yet been put to the test in a broad scale on the internet yet, so its
economic effects cannot be predicted.
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New Frontier for Web Design and User
Participation
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The economic models I described above have all been on insular sites that
host content. That is, people realize that content has value, so they are
using Web 2.0 world to find it. However, in the Web 3.0 world, content may
very well exist on websites that don't yet have an ecommerce
infrastructure. It's not just about taking credit cards or other forms of
payment, it's about pricing models and legal licensing terms. This is the
very inefficiency of peer-to-peer licensing that I focused on in part one
of this series.
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New internet-wide methods and protocols must be established to enable any
website that carries licensible content. As more and better content
is produced, and as search engines are better able to analyze it
semantically and produce search results sorted by personalized
preferences, more of the content must be licensed through a universally
available infrastructure, thereby transforming the way websites are
designed, further affecting the visitor behaviors and incentives.
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So what about that licensing mechanism? One such development in this area
is ACAP, which is found at http://www.the-acap.org/. ACAP
stands for the Automated Content Access Protocol, and its main initial
purpose is for communicating access and usage permissions (about the
content on any given site) to web crawlers (also known as 'spiders' or
'robots'). Just as you currently accept (and need) Google and other search
engines to crawl your site to index it so it will come up in search
results, an ACAP search engine will do the same thing, but it looks for
other details about your content besides its semantic meaning. It is used
to specify license terms and conditions that the owner stipulates, should
someone want to license something from your site.
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Of course, this a huge and complicated effort, since mechanisms need to be
put into place to track and verify content ownership. But, waving the
magic wand about that for the moment, if it were to exist, this then paves
the way for a content licensing protocol to sit on top of the
entire stack of media and search data about it, to complete the puzzle:
any content crawler could assess market conditions for any given type of
media type and estimate a market value. Plug that into an existing
auction-based system like Google's adwords program, and the financial
models are in place for the new economic model where a series of automated
analytical robots crawl the web, analyze content, rate and rank its
information and its creators, and come up with a high/low range for
pricing, which can be used to see a more fine-tuned auction-based
mechanism.
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As futuristic as this may sounds, all of the technologies that do
these tasks exist today in one form or another. It's merely applying them
in a generalized way to arbitrary and abstract data types that makes it an
inevitable development. What's more, it's self-regulating and
self-perpetuating. Taken out of the equation is the inefficiencies of
peer-to-peer licensing models, where prices are arbitrary, and the
transaction itself is costly and time-consuming.
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Just as Web 2.0 created a feedback mechanism (where social networks
yielded financial returns, which stimulated the growth of social
networks), the Web 3.0 world will have a similar feedback mechanism, where
content creators are given incentives to create good content, describe it
well, and allow third-party, automated market-makers to handle
transactions. Though the content itself may still be exchanged between
creators and publishers, the transaction will more likely be
officiated through market-makers.
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It's also a more efficient system in that incentives to cheat are reduced.
This comes in two forms. First, because everyone's search may not
necessarily yield the same results, attempting to manipulate content to
match what someone might think search engines are looking for may actually
diminish the content's value. Searchers looking for a photo of a "woman"
aren't always looking for porn—they may genuinely be looking for a
photo to be used in legitimate mainstream media. If the content creator
tries to "lie" to manipulate search engine results for the photo, he may
inadvertently eliminate as many buyers as he would attract if he were just
honest about the content in the first place. That's not to say that all
content is equally valued, but that brings up the second aspect to
semantic awareness by search engines: the content itself would be ranked,
not just the site it came from. If a particular set of photos were
manipulated with "keyword pollution" (where the photographer adds a huge
amount of keywords in the hopes of being indexed to match a large number
of search parameters), then that image would be reduced in its credibility
ranking, irrespective of what the photo's content actually depicted, or
what site the photo came from. Being a bad actor in the economic game has
penalties, and being a good actor has rewards.
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So, what can disrupt this potential future? The elephant in the middle of
the room that I haven't mentioned is Copyright. That is, user-generated
content is copyrighted material, owned by the creators of content, and
that creator has rights. The ability for a website to sell content that
visitors submit is restricted in ways that aren't entirely easy for
everyone to quickly understand, and navigating around this restriction
involves an exercise in skills in three disciplines: political, legal and
socio-economic. I'll tackle all that and more in part three of this
series. Stay Tuned.
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