Category: onlinejournalismblog

Is it really only a couple years since the latest, widely quoted, iteration of the idea that “If you are not paying for it, you’re not the customer; you’re the product being sold” was first posted about web economics?

[Notes for folk visiting this site from a referral thread on metafilter]

Prompted by the recent release of new Google product that presents site visotrs with a paid for, and revenue generating, survey before they can see the site’s content, here are a few observations around that idea…

First, let’s just consider the paywall for a moment. Paywalls on the web prevent you from accessing content without payment or some other form of financial subscription. I’m guessing the term was originally coined as a corruption of the term “firewall”, which in a network sense is a component that either allows or prevents network traffic from passing from one device to another based on a set of rules. For example, a firewall might blog traffic from a .xxx domain or particular IP address. [OpenLearn: What are firewalls?]

If a user can be tracked across pageviews within a single visit to a site, or across multiple visits to the site, the paywall may be configured to allow the user to see so many items for free per visit, or per month, before they are required to pay.

Paywalls, can come in a literal form – you pays your money and you gets your content – or at one step remove: you hand over your data, and it’s used to charge an advertiser a premium rate for selling ads to you as a known entity, or by selling your data to a third party. This is the sense in which you are the product. So how does it work?

If you’ve watched an online video recently, whether on a site such as Youtube, or a (commercial) watch again TV service such as ITV Player or 4od, you may way have been exposed to a pre-roll advert before the video you want to watch begins. Many commercial media websites, too, load first with an ad containing lightbox that overlays the article you actually want to read, often with a “Skip Ad” action required if you want to bail out of the ad early.

These ads are one the ways these sites generate income, of course, income that at the end of the day helps pay to keep the site running.

The price paid for these ads typically depends on the size and “quality” or specificity, as well as the size, of the audience the site delivers to the advertiser (that is, the audience segment: [OpenLearn: Market segmentation and targeting]). Sites (and magazines, and TV programmes) all have audiences with a particular demographics and set of interests, and these specialist or well defined audience groups are what the publisher sells to the advertiser.

(From years ago, I remember a bid briefing for a science outreach funding programme where we were told we would be marked down severely if we said the intended audience for our particular projects was “the general public”. What they wanted to know was what audience we were specifically going to hit, and how we were going to tune our projects to engage and inform that particular audience. Same story.)

At the end of the day, adverts are used to persuade audiences to purchase product. So you give data to a publisher, they use that to charge an advertiser a higher rate for being able to put ads in front of particular audiences who are presumably likely to to buy the advertiser’s wares if nudged appropriately, and you buy the product. With cash that pays the advertiser who bought the ad from the publisher who sold your details to them. So you still paid to access that content. With a “free gift” in the form of the goods you bought from the advertiser who bought the ads from the publisher that were placed in front of a particular audience on the basis of the data you gave to the publisher.

Let’s reconsider the paywall mediated sites, for a moment, where for example you get 10 free articles a month, 20 if you register, unlimited if you pay. The second option requires that you register some personal information with the site, such as an email address, or date of birth. You get +x article views on the site “for free” in exchange for your giving the website y pieces of data. In exchange for those free views, you have had to give something in return. You have bought those extra “free” views with your data. The money the site would have got from you if you had paid with cash is replaced by income generated from your data. For example, if the publisher sells adverts at a high price to audiences in the 17-25 range, and you are in that age range, the disclosure of your birthdate allows you to be put into that audience group which is sold to advertisers as such. If you handed over your email address, that can also be sold on to email marketers; if you had to verify that address by clicking on a link emailed to it, it becomes more valuable because it’s more likely to be a legitimate email address. More value can be added to the email address if it is sold as a verified email address belonging to a 17-25 year old, and so on.

Under the assumption that by paying attention to an ad you become more likely to buy a product, or tell someone about the product who is likely to buy it, the paywall essentially becomes replaced by an “attention based, indirect paywall”.

A new initiative by Google ramps up the data-exchange based paywall even further: Google Consumer Surveys. Marketing magazine describes it as follows (Google’s new survey tool: DIY research tool and pay wall alternative):

‘Google Consumer Surveys’ is a survey tool which blocks sections of webpages or articles until the reader answers a question, paying the website owner five cents per response when they do. The service is being billed as an alternative revenue model for publishers considering a pay wall strategy, launching with a handful of news partners last week.

The service works as a DIY research tool, charging users 10 cents per response to questions of the their choice. Buyers of the research have the option to pay an extra 40 cents per response to target sub-populations based on gender, age and location and can target more specific audiences, such as dog owners, with a screening and follow-up question option that costs an additional 50 cents per response.

So let’s unpick that: rather than running ads, the publisher runs a survey. They essentially get paid (via Google) for running the survey by someone who pays Google to run the survey. You hand over your data to the survey company who pays Google who pays the publisher for delivering you, the survey subject. Rather than targeting ads at you, Google targets you as a survey subject, mediated by the publisher who delivers a particular audience demographic; (rather than using sites to target particular audiences, I guess Google will end up using knowledge about audiences to ensure that surveys are displayed to a wide range of subjects, thus ensuring a fair sample. Which means, as Marketing mag suggests, “the questions [will] potentially having nothing to do with the site’s content…”). Rather trying to influence you as a purchaser by presenting you with an ad, in the hope that you will return cash to the person who orginally paid for the ad by buying their wares, disclosure about your beliefs is now the currency. (I need to check about the extent to which: a) Google can in principle and in fact reconcile survey results with a user ID; b) the extent to which Google provides detailed information back to the survey commissioner about the demographics and identity of the survey subjects. Marketing mag suggests “[t]o pre-empt any privacy fears, the search giant is emphasising that all surveys will be completely anonymous and that Google will not use any data collected for its own ad targeting.” So that’s all right then. But Google will presumably know that it has served you x ads and y surveys, if not what answers you gave to survey qustions.).

As well as productising yourself, as sold by publishers to advertisers, by virtue of handing over your data, you’ve also paid in a couple of other senses too – with your attention and with your time. Your attention and your demographic details (that is, your propensity to buy and, at the end of the day, your purchasing power (i.e. your cash) are what you exchange for the “free” content; if your time represents your ability to use that time generating your own income, there may also be an opportunity cost to you (that is, you have not generated 1 hour’s income doing paid for work because you have spent 1 hour watching ads). The cost to you is a loss of income you may otherwise have earned by using that time for paid work.

A couple of the missing links in advertising, of course, are reliable feedback about: 1) whether anyone actually pays attention to your ad; 2) whether they act on it. Google cracked part of action puzzle, at least in terms of ad payments, by coming up with an advertising charging model that got advertisers to bid for ad placements and then only pay if someone clicked through on the ad (Pay-per-click, PPC advertsing) rather than using the original display oriented, “impression based” advertising, where advertisers would pay for so many impressions of their advert (CPM, cost per mille (i.e. cost per thousand impressions).

It seems that Google are now trying to put CPM based metrics on a firmer footing with a newly announced metric, Active View (Making the Web Work for Brand Marketers).

Advertisers have long looked for insight into whether consumers saw an ad on page 145 of a magazine, or switched the channel during a TV commercial break. It’s similar online, so we’re rolling out a technology [Active View], … that can count “viewed” impressions (as defined by the IAB’s proposed standard, this is a display ad that is at least 50% viewable on the screen for at least one second).

… Active View data will be immediately actionable — advertisers will be able to pay only for for viewed impressions.

They’re also looking to improve feedback on the demographics of users who actually view an advert:

Active GRP: GRP, or a gross rating point, is at the heart of offline media measurement. For example, when a fashion brand wants their TV campaign to reach 2 million women with two ads each, they use GRP to measure that. We’re introducing a new version of this for the web: Active GRP. …

… Active GRP is calculated by a statistical model that combines aggregated panel data and anonymous user data (either inferred or user-provided), and will work in conjunction with Active View to measure viewed impressions. This approach overcomes problems of potential panel skewing and reliance on a single data source. This approach also has the advantage of never using personally identifiable information, not sharing user data with third parties, and enabling users, through Google’s Ads Preferences Manager, to opt-out.

Both these announcements were made in the context of Google’s Brand Activate initiative.

Facebook, too, is looking to improve it’s reporting – and maybe its ad targeting? – to advertisers. Although I can’t offhand find an original Facebook source, TechCrunch (Facebook Ads Can Now Be Optimized To Drive Any On-Facebook Action, Such As In-App Purchases, Shares, Offer Claims), Mashable (Facebook’s Analytics Tool for Ads Will Soon Measure Actions Other Than ‘Likes’) et al are reporting on a Facebook briefing that described how advertisers will be able to view reports describing the downstream actions taken by people who have viewed a particular advert. The Facebook article also suggests that the likelihood of a user performing a particular action might form part of the targeting criteria (“today Facebook begins allowing advertisers using its API to ask it to show their ads to people most likely to take any specific post-click action on the social network, such as sharing a brand’s content to the news feed, buying virtual goods in their apps, or redeeming one of the new Facebook Offers at a local brick-and-mortar store”).

So now, it seems that the you that is the product may well soon include your (likely) actions…

See also: Expectations Matter, Even If You’re Not ‘A Customer’ which links in to a discussion about what reasonable expectations you might have as a user of a “free” service.

And this: Contextual Content Delivery on Higher Ed Websites Using Ad Servers, on something of Google’s ad targeting capacity as of a couple of years ago…

[Notes: I would reply in the thread but I don’t want to have to pay cash for the, erm, privilege of doing so… I also appreciate that none of these ideas are necessarily original, and I recognise that the model applies to TV, radio, print or whatever other content carrier and container you care to talk about… I suspect that Blue Beetle isn’t actually the source of the “you are the product” slogan this time round, anyway, (in recent months, Wired probably is) although many search engines lead that way. (So for example, it’s easy to find earlier, similarly pithy, expressions of the same sentiment in the web context all over the place… For example, this 2009 post; or this one). And not that you’ll care, this blog is my notebook, and these notes are just me scribbling down some context around the Google survey product (the post construction/writing style reflects that) #trollFeeding PS Since everybody knows that 1+1=2, I figure we probably don’t need to teach it anymore #deadHorseFlogging #gettingChildishNow #justLikeAMetaFilterThread]

It seems that Reuters has a new product out – Reuters Social Pulse. As well as highlighting “the stories being talked about by the newsmakers we follow”, there is an area highlighting “the Reuters & Klout 50 where we rank America’s most social CEOs.” Of note here is that this list is ordered by Klout score. Reuters don’t own Klout (yet?!) do they?!

The offering also includes a view of the world through the tweets of Reuters own staff. Apparently, “Reuters has over 3,000 journalists around the world, many of whom are doing amazing work on Twitter. That is too many to keep up with on a Twitter list, so we created a directory Reuters Twitter Directory] that shows you our best tweeters by topic. It let’s you find our reporters, bloggers and editors by category and location so you can drill down to business journalists in India, if you so choose, or tech writers in the UK.”

If you view the source of Reuters Twitter directory page, you can find a Javascript object that lists all(?) the folk in the Reuters Twitter directory and the tags they are associated with… Hmm, I thought… Hmmm…

If we grab that object, and pop it into Python, it’s easy enough to create a bipartite network that links journalists to the categories they are associated with:

import simplejson
import networkx as nx
#http://mlg.ucd.ie/files/summer/tutorial.pdf
from networkx.algorithms import bipartite

g = nx.Graph()

#need to bring in reutersJournalistList
users=simplejson.loads(reutersJournalistList)

#I had some 'issues' with the parsing for some reason? Required this hack in the end...
for user in users:
	for x in user:
		if x=='users':
			u=user[x][0]['twitter_screen_name']
			print 'user:',user[x][0]['twitter_screen_name']
			for topic in user[x][0]['topics']:
				print '- topic:',topic
				#Add edges from journalist name to each tag they are associated with
				g.add_edge(u,topic)
#print bipartite.is_bipartite(g)
#print bipartite.sets(g)

#Save a graph file we can visualise in Gephi corresponding to bipartite graph
nx.write_graphml(g, "usertags.graphml")

#We can find the sets of names/tags associated with the disjoint sets in the graph
users,tags=bipartite.sets(g)

#Collapse the bipartite graph to a graph of journalists connected via a common tag
ugraph= bipartite.projected_graph(g, users)
nx.write_graphml(ugraph, "users.graphml")

#Collapse the bipartite graph to a set of tags connected via a common journalist
tgraph= bipartite.projected_graph(g, tags)
nx.write_graphml(tgraph, "tags.graphml")

#Dump a list of the journalists Twitter IDs
f=open("users.txt","w+")
for uo in users: f.write(uo+'\n')
f.close()

Having generated graph files, we can then look to see how the tags cluster as a result of how they were applied to journalists associated with several tags:

Alternatively, we can look to see which journalists are connected by virtue of being associated with similar tags (hmm, I wonder if edge weight carries information about how many tags each connected pair may be associated through? [UPDATE: there is a projection that will calculate this – bipartite.projection.weighted_projected_graph]). In this case, I size the nodes by betweenness centrality to try to highlight journalists that bridge topic areas:

Association through shared tags (as applied by Reuters) is one thing, but there is also structure arising from friendship networks…So to what extent do the Reuters Twitter List journalists follow each other (again, sizing by betweenness centrality):

Finally, here’s a quick look at folk followed by 15 or more of the folk in the Reuters Twitter journalists list: this is the common source area on Twitter for the journalists on the list. This time, I size nodes by eigenvector centrality.

So why bother with this? Because journalists provide a filter onto the way the world is reported to us through the media, and as a result the perspective we have of the world as portrayed through the media. If we see journalists as providing independent fairwitness services, then having some sort of idea about the extent to which they are sourcing their information severally, or from a common pool, can be handy. In the above diagram, for example, I try to highlight common sources (folk followed by at least 15 of the journalists on the Twitter list). But I could equally have got a feeling for the range of sources by producing a much larger and sparser graph, such as all the folk followed by journalists on the list, or folk followed by only 1 person on the list (40,000 people or so in all – see below), or by 2 to 5 people on the list…

Friends lists are one sort of filter every Twitter user has onto the content been shared on Twitter, and something that’s easy to map. There are other views of course – the list of people mentioning a user is readily available to every Twitter user, and it’s easy enough to set up views around particular hashtags or search terms. Grabbing the journalists associated with one or more particular tags, and then mapping their friends (or, indeed, followers) is also possible, as is grabbing the follower lists for one or more journalists and then looking to see who the friends of the followers are, thus positioning the the journalist in the social media environment as perceived by their followers.

I’m not sure that value Reuters sees in the stream of tweets from the folk on its Twitter journalists lists, or the Twitter networks they have built up, but the friend lenses at least we can try to map out. And via the bipartite user/tag graph, it also becomes trivial for us to find journalists with interests in Facebook and advertising, for example…

PS for associated techniques related to the emergent social positioning of hashtags and shared links on Twitter, see Socially Positioning #Sherlock and Dr John Watson’s Blog… and Social Media Interest Maps of Newsnight and BBCQT Twitterers. For a view over @skynews Twitter friends, and how they connect, see Visualising How @skynews’ Twitter Friends Connect.