A couple of weeks ago, I asked a bunch of people to send me their OPML files (for those of you who aren’t aware, an OPML file is what tells your RSS reader what feeds you’ve subscribed to — it can act as a way of moving your subscriptions between readers.) Some of the more trusting among them agreed, and that gave me the raw material for the first bit of my experiment.

Some red herrings

Along the way I uncovered a couple of things that were interesting but not (entirely) relevant to the experiment.

1. Some people are cagey about sharing their list of feeds: whether they consider it intellectual property, or whether they think that it may be too revealing, I don’t know.
2. Lots of people said things like “oh — my RSS reader? Haven’t looked at that in a while. I get all my news off Twitter these days.”

So what’s the experiment about?

You’ll probably know that I’m interested in networks of people and how information flows through those networks. I’m also interested in things like influence and whether and how we can identify and track its effects.

I’d heard about a word of mouth marketing campaign which was set up along the following lines: a sample set of pupils at a given school were asked “who’s the coolest kid in the school?” Clearly some names came up more often than others, whereupon the researchers went to those kids and asked them the same thing. By the end of the process, they had a good idea of who might be the key influencers.

This seemed like a good sort of thing to be doing. It was a simple idea, and apparently easy to execute. Everything else I was working on (the citation analysis and the network analysis in particular) seemed to be complementary.

A little history

So last year we did a version of this experiment for a client. We emailed and phoned a whole load of journalists and analysts whose beats covered our client’s interests and asked them:

Who do you read on a regular basis?

Who (other than you) should we be talking to?

If you were looking for information, where would you start?

Then we approached the people that they’d recommended and asked them the same questions. The purpose (as I’m sure you’ll have guessed) was to create a network map of who was whose go-to guys and girls. We’d take the data that we collected, push it through the usual processes and hey presto! we’d know who was really influential.

But what we actually found was that the journalists and analysts we asked seemed not to have any specialist sources. The people whom they cited were (in no particular order) their colleagues, the companies they covered (and their public relations representatives and agencies). Oh, and Google. Google was cited by everyone.

There were two obvious explanations for this:

1. It’s the truth: they really didn’t have any better sources. We’d all read about the collapse of journalistic standards; perhaps we were encountering it at first hand?
2. The privacy and intellectual property argument: maybe the journalists and analysts were constitutionally loathe to reveal their sources?

All my training and experience, however, points to the following reason:

3. Poor questionnaire design led to an inability to think (when put on the spot) of names and sources whom they believed were their influencers.

This is less unlikely than it may seem. If I were to ask you who your big influencers were, would you be able to answer?

All in all, it was a deeply unsatisfying exercise. We had envisioned what wild success would look like and this wasn’t even close.

Where we are today

I thought that it might be easier to ask people for their OPML files than it had been to ask them who were their influences. This is the equivalent of — say — asking to see a musician’s CD collection, instead of asking them about their musical influences. It’s not necessarily more accurate, but it might help expose a different picture.

Seven of my friends, colleagues and acquaintances sent me their OPML files, and that was enough to get started.

Early results

Between them, the first seven people subscribed to just over 1.5K RSS feeds, which gave me a lot of data to process. Here’s a picture of the network before I started processing the data.

You can see (I think) pretty clearly that there are several blogs (or RSS feeds — I’m using them interchangeably here) in the middle of the map that are linked to by several of the respondents. And there are some (around the edges) that are linked to by a couple of respondents. And there are lots that are linked to by only one respondent.

At this stage we’re interested in “indegree” — or “the number of OPML files in which we find RSS feed X.” Clearly the great majority (just over 1.3K) of the RSS feeds are single hits — that is, they appear in only one respondent’s OPML file. But that still leaves 180 RSS feeds with multiple hits:

Those 180 RSS feeds with multiple hits left us with a nice (and fairly predictable) distribution that looks like this:

Clearly the first-generation results aren’t very meaningful or accurate. Most often cited were author Seth Godin, ex-blogger-turned-lifestreamer Steve Rubel, and PR blogger Stephen Davies who were each read by five of the seven respondents. But more than half the respondents read my blog. Despite what I’d like to think, this is clearly an artefact thrown up by the sampling frame (my friends, colleagues and acquaintances).

Where next?

I’m hoping that we can extend this out a generation — and then keep iterating. I’m going to approach everyone with an indegree of 3 or above, and see how many of them will send me their OPML files.

I don’t hold out much hope of getting OPML files from people like Messrs Godin and Rubel, but if I don’t ask, I’ll never know, will I?

The current approach to WOM is to try to stimulate positive WOM while addressing or countering negative WOM. A sort of “accentuate the positive, eliminate the negative and don’t mess with Mr In-Between” strategy.

But what if we could do it a different way?

This idea stems from a conversation I had back in February with Martin Kelly and Andy Cocker of Infectious Media. Since that time I’ve chatted it through a couple of times with various interesting people. It’s not properly thought through yet, but following a chat a couple of weeks ago with Ketchum London’s new Head of Digital, the excellent Fernando Rizo, I’ve decided to put the idea out into the public domain to gauge what (if any) interest there is and whether I should continue to work on it.

“Word of Mouth” is hard to do well

I’ve read lots of word of mouth marketing case studies (there’s a great list over at WOMMA) and it strikes me that WOM is hard to do well for a few reasons. I don’t want to go into these in too much detail, but here are a couple of the structural issues:

1. Unless I’m a journalist, an A-list blogger or media personality or have some kind of platform, I probably have a very low reach.

   Despite everything pointing towards personal contact being the best impetus for positive word of mouth, most word of mouth campaigns compensate for my low reach by trying to get me to self-service my relationship with the brand and the campaign.

2. “Viral” distribution just doesn’t work the way most people seem to think it does; and this is particularly true when it comes to WOM.

   While I’m quite likely to tell stories about my personal experience of a brand and fairly likely to tell stories that involve a mutual friend, I’m much less likely to tell stories about other friends’ experience, and not likely at all to tell stories about friends-of-friends.

   Furthermore because of the ‘clumpiness’ of most people’s social graphs, geometric progression (the “I tell two people and they each tell two people and so on” effect) just doesn’t happen.

Homophily

One of the many reasons that WOM works is a thing called homophily — which roughly translates to “birds of a feather flock together”, or “you can tell a man by the company he keeps.”

I’ve written about examples of this before: for example, my analyses of twittering US Congresspersons and Westminster MPs which showed that one can predict with some reasonable degree of accuracy the political colouration of any given twitter account based on their mutual friends and follows (if you want to know more about the methodology, it’s worth reading Robert Hanneman’s chapter on cliques and subgroups.)

But there’s another side to the homophily coin; the social pressure to conform to the group’s norms.

Why I bought an iPhone

Let’s take a concrete example of this: it often seems to me that everyone I know has an iPhone. I made a conscious decision a few years ago not to buy an iPhone, but I’ve finally succumbed to the pressure.

While it’s true that several people have take time out of their busy schedules to tell me exactly how good the iPhone really is, I’m far more affected by what I perceive as the omnipresence of the iPhone. Everyone, it sometimes seems, has one except me.

Of course, on those occasions when I head down to visit my family in rural Hampshire, I’m reminded of the obvious truth: most people don’t have an iPhone. But that’s not the way the world appears to me.

How about turning it on its head?

What if we could identify people who are under social pressure to buy our products — who are being influenced by what their friends are doing and saying?

Let’s say for example that Apple and O2 (iPhone’s carrier partner in the UK) could work out that a significantly higher proportion than average of my Facebook friends access the service using the iPhone client, and could target me with special offers and rates to push me over the edge.

Instead of looking for WOM influencers, why don’t we look for areas of high potential — and target those people who are likely to be receiving lots of WOM stimuli?

Public service message: please don't push strangers in front of oncoming trains (by freshelectrons on Flickr)

When we’re using push channels like display ads, direct marketing or pull channels like websites or search marketing — numbers are what count, and numbers are enough. But when we are talking about social media channels, we shouldn’t target strangers. Instead, we should look at our existing relationships and learn how to make the most of these to our mutual benefit.

I don’t know whether you’ve had the experience of meeting someone famous in an ordinary context (in the street, say, or in a supermarket queue). I have.

It is a profoundly disturbing experience. For a split second your brain tells you that this is someone familiar but not why. Since you’re not expecting to meet David Bowie in your video store, your brain leaps to the most probable conclusion — this is clearly an old acquaintance or a friend-of-a-friend. By the time you realize who it is, you’ve already been staring at them too long, possibly waving and beginning to say hello.

What’s unnerving about this experience of course, is the asymmetry of the relationship; you know who they are (and possibly even some intimate details of their private lives) but they have no idea who you are. For all that you think you know them, you are in fact complete bloody strangers.

The circle of complete bloody strangers

At Porter Novelli, we’ve been trying out a new way of helping people think about the targets for our social media activities. Targeting in social media is one of the many places where conventional marketing experience fails to help; and indeed, generally hinders. For want of a better name, I’m calling it the “circle of complete bloody strangers.”

This may not be the most elegant name, but it does help simplify the problem. Here’s the synopsis: in traditional on and off-line marketing disciplines, planners like me tend to play a numbers game. We’re looking to reach the greatest number of people who will be receptive to our message, and (whether behavioural or demographic) we define our targets accordingly. And so, we spend nearly all our time crafting our messages and campaigns for complete bloody strangers.

Targeting strangers

Agencies do everything we can to get to know our target audience. At Porter Novelli, for example, we follow a process that has us (among other things) conducting desk research, running focus groups and interviews with representatives from the audience, and doing mystery shopping. At one of the wackier ad agencies where I used to work, there were workshops where hypnosis was used to break down barriers and aid rôle-playing exercises.

But however well we get under the skin of our target (say “C2DE mothers with young families”), they remain complete bloody strangers.

You might recognize them in the street, say, or in a supermarket queue – but like the celebrities whose lives you follow in the news – they have no idea who you are.

Social media marketing and social currency

There is (to my knowledge) no single precise definition of social media marketing. Many, I think would agree that it involves something like “using blogs and podcasts and social networks and media sharing sites and stuff to promote a product or service,” but that’s hardly precise.

Some would argue that simply buying advertising on a blog/podcast/social network meets the requirements, others would say that it doesn’t.

As a broad digital comms planner, I tend not to fall one way or the other, but suspect that if buying media or sponsorship was all that it took, we should all have stopped worrying and gone home by now.

I suspect that when we talk about social media marketing, we mean something like “generating word-of-mouth buzz and recommendations – whether for a product or a campaign promoting that product.”

I believe that we’re trying to use existing social relationships between colleagues, friends, tribes, and niche interest groups as the pathways along which we disseminate information. This isn’t as sinister as it sounds; we know that people use brands and branded content as what Douglas Rushkoff calls “social currency.” Content, he says

…only matters in an interactive space or even the real world … because it gives us an excuse to interact with one another. When I was a kid, we’d buy records not solely because we wanted to hear whoever was on them; we wanted an excuse for someone else to come over! “What are you doing after school? I got the new Stones’ album…”

In this sense, our content choices are just means to an end – social currency through which we can make connections with others.

So — in Rushkoff’s terms — what we’re doing when we do “social media marketing” is stamping new coins that can be released into the general circulation.

At Porter Novelli, my mantra is that “people want to promote themselves, and may be prepared to let you help.” This helps me remember that, however important my client’s campaign appears to me, my audience’s motivation is markedly different.

Why we shouldn’t talk to strangers

Take a look at the target diagram again. Normally I scrawl this on a flip chart and tell the story as I’m going along. The circles aren’t precise, and I often draw them differently depending on context, but these things are consistent: you are always at the centre of the target, and the outer circle is always defined as “complete strangers”.

Now ask yourself – who’s most interested in what you have to say, and who is most likely to repeat it in subsequent conversations? I’d say that the progression goes something like:

1. You (naturally.)
2. People you’re close to (friends, family, and colleagues for example.) If you can’t get these people interested in what you have to say, what hope do you have?
3. Your “ecosystem”; people who depend on you economically – whether suppliers or employees or retailers who carry your product or developers who develop for your platform. Trade and specialist bloggers and journalists fit in somewhere around here.
4. People who have a strong relationship with your product, tailing off from advocates to simple repertoire purchasers (people who regularly — but not exclusively — buy your product.)
5. Complete bloody strangers.

Let’s take an example.

1. The people who will be most interested in the launch of your new FMCG ad campaign will be people who work with you and at the agency who created it.
2. People at other agencies will also be interested in the campaign, so it’s a good thing that there are lots of inter-agency relationships. And so will your friends and family — they like seeing what you’ve been up to and will tell their friends.
3. The retailers who stock your product will naturally be excited to hear that there’s a campaign coming. And Campaign and Ad Age are always hungry for content.
4. If you’re smart, you’ll have run test campaigns among your customer database of people who have asked to receive information. Asking their opinion makes them feel involved, and means that they’ll feel a little pride-of-ownership when the campaign does launch. “I helped with that,” they’ll say to themselves, and maybe to their friends.
5. If your media plan and creative are any good, you’ll create a stir among the complete strangers.

Do you see what I mean? As you move further and further out, the more tenuous the connection, and the less certain the response.

Another thing to bear in mind; the further away you are from the middle of the target, the more noise you encounter. By the time you’re out in the complete strangers circle, you’re just another voice making demands on their time.

So why do so many of the campaign ideas I come across require the active involvement of complete bloody strangers for them to be successful?

Let’s host a competition on YouTube where people send in films about our product! Wheeee!

Let’s create a community! Wheeee!

Let’s send out a short film and hope that people will send the link to each other! Wheeee!

Let’s make a Facebook app! Wheeee!

None of these ideas is inherently wrong from the traditional marketing point of view. As an industry, we are (rightly perhaps) judged by total exposure to our campaigns. For most channels, numbers are what count, and numbers are enough.

But how on earth can we hope to influence the social networks of complete bloody strangers when we aren’t even making proper use of our own?

On November 10th, Stephen Davies collected together a list of “UK PR people on Twitter” According to PostRank, this (and his earlier post, “UK Journalists on Twitter“) are the most popular posts on his blog.

Then a couple of days later, Stephen Waddington pushed that list through TwitterGrader to come up with his list of “Top 50 UK PR people by Twitter influence”

A couple of weeks ago, I was looking for a seed list with which I could test our “whitelist” and “canonify exception” rules on Rufus (the network analysis tool that Porter Novelli has been working on for the past six months.) This isn’t the right place to go into it, but to put it simply, the whitelist restricts the search to domains that are on the list (like a guest list), and the canonify exception list stops Rufus from chopping the subdomains or directories off the list (without this, a site like sethgodin.typepad.com would just show up as typepad.com or en.wikipedia.org/wiki/Social_network_analysis would show up as wikipedia.org. Rufus, by the way, is named after the George Carlin character in Bill & Ted’s Excellent Adventure.

My colleague, Tim Hoang used to work with Stephen W., so he sent him the image. Wadds then posted “the map on his blog“. My flickr page has never had so much activity.

Here’s the original graph:

Lots of people started drawing conclusions about the nature of PR, or the nature of Twitter from the graphs. There was lots of interesting speculation. Some people thought that this demonstrated how introverted the twitter crowd is. Others thought that it showed how introverted the PR/Social media crowd is. Others seemed to think that it didn’t matter.

But the truth is, Rufus isn’t the right tool for Social Network Analysis. Designed to look at how different blogs, forums and news sites contribute to the “conversation” around a particular topic, it’s not specialized enough to look closely at one site. But our interest was piqued. Among other things (and this isn’t false modesty) I never really believed that I belonged in the top 50. After all, I’ve been working in public relations for about eighteen months. I may be influential (get me!) outside the PR-sphere, but inside? Less so.

The Twitter Spider

By now we know just about enough with regards to social network analysis that we can throw up low-cost tools with only a very short planning stage. So that’s what we started to do. We built a twitter spider (which seems to be legitimate under Twitter’s terms and conditions) and started testing it. The spider is designed to find friends (i.e. people that a given Twitter user follows) and followers (vice versa). We’re only really using friend data in the rest of our analysis, but if you’re going to build one, you may as well build the other.

After some early setbacks:

We finally got it right. That’s when we ended up with the “cranberry hedgehog” map.

The hedgehog is actually a map of twitter users who are followed by at least two of Waddington’s top 50. The more followers a user has, the larger their “cranberry”, and the closer they are to the centre of the map. What we see here is a very, very dense network. But it’s too hard to read. There’s too much peripheral data.

Analysis

At this stage, we thought it might be interesting to see who (other than themselves) the top 50 were following. Here’s the list of all those people who are followed by at least 10 of the top 50:

Nice to see Robin Grant, Jemima Kiss, and Mike Butcher leading the list. And – surprise surprise – wordy smugbucket Stephen Fry is there in joint second place.

Over the weekend, I wrote a script to process the data a little more (I’ll share it when I’ve tidied it up a little). Now we can look at links between the top 50 only. If Mr Waddington’s seedlist is really representative (bear with me here) then what this will show is their relative status (or prestige) as assessed by their peer group. It doesn’t matter whether a bunch of credulous know-nothings think they’re worth listening to; their peers (who one would assume know a lot more about the topic area) think they’re worth it.

So here it is: Waddington’s list of Top 50 UK PR people by Twitter influence ranked by degree centrality within their peer group.

Interesting? Well — there’s been a lot of movement. Here’s a comparison of where they rank among their group as opposed to among the whole twittersphere. Notable winners are JedHallam and Wadds who leap up into leader board. Notable losers are Jangles (Neville Hobson) and Mediaczar (me, of course). It is with great relief that I feel the mantle of greatness sliding from my shoulders.

Now, there’s a metric that we haven’t really used much (and I’m not sure how meaningful it is) but I thought I’d share it nonetheless to see what others thought. It’s the ratio of Friends to Followers — or how many of the top 50 a user follows compared to the number of the top 50 who follow them.

I’ve been a bit intrigued by this metric since I did some social network analysis (SNA) on Facebook late year and in the first quarter of this (you can see some of the results on Facebook social graphs Flickr set.

What you should note in the chart below are the extreme outliers around the 400 to 460 mark (marked with a bright red circle).

I happen to know that one of these people is so widely admired that people seek to be their friend, and that another is an aggressive networker who strives to befriend vast swathes of the general population.

Because Facebook is an undirected network (i.e. if I’m your friend, you are ipso facto my friend) the data won’t tell you which is which. Looking at this, it seems obvious that the ratio between the people I ask to be my friend and the people who ask to be my friend should be a useful indicator in cases like this. When we’re dealing with websites (using Rufus) links only go one way. I may link to Wikipedia a lot, but Wikipedia is unlikely to link to me. That gradient is important.

So here – neatly marked in green and red are the Friend-to-Follow ratios for the top 50.

This is a 300dpi map of the top 50 PR twitterers (as per Stephen Waddington’s analysis) and the interrelationships between them.

To generate this:

We first crawled all the accounts for “friends” (accounts that they follow) and “followers” (accounts that follow them). This is a profligate use of resources because we were always going to throw away a massive load of that data. But it’s always more interesting to start with a large data set. You don’t know what you’re going to find.

Then I wrote a quick-and-dirty perl script to process the data looking only for those instances where one of the top 50 followed another.

Then we dropped everything into NetDraw (if you are at all interested in this stuff, you really should get hold of a copy and start reading around the subject.) We laid out the chart so that the people who have the most peer-group followers are in the centre of the chart – and to make it even more obvious, we sized their nodes according to the number of peer-group followers that they have.

So people on the peripheries (like me – mediaczar) are peripheral to the community, and those in the middle are central. Obvious, huh?

This chart already shows a massive difference between our analysis (as it progresses) and the raw data from Wadds’s list. There are some really good reasons for this, which I’ll go into on the blog.

Map of top 50 UK PR twitter people and their followers

Originally uploaded by matmorrison

This is not a hedgehog in a cranberry field. It is a network map, but a particularly tightly-knit one.

Spurred on by some of the comments we’ve received about the Rufus map we made of the top 50 UK PR twitter people (as measured by Stephen Waddington) I thought it’d be a good idea to look at this in a bit more depth. Rufus isn’t really the right tool for looking at this kind of thing, so we’ve built something else to do it better. Looking at one site or service is a lot easier than looking at lots of sites — so this took hours, not months to create.

After a little debugging we were ready to test on a seedlist of 50. The crawl took about an hour to run.

This is a visualization of the data set we got (correct as at December 12, 2008) after very little processing.

The size of the blobs relates roughly to “how many people in the group follow you.”

We’ve removed anyone who is only followed by one person in the group. So everyone here is followed by at least two others (obviously.)

There are just too many people in this graph to show labels. And a lot of the top 50 people are hidden by other top 50 people. Maybe I should do a graph rotating in 3D. (Later, having tried this: if I had a SGI workstation, maybe I would.)

What I’ll do over the weekend is process the data files I’ve got (one’s got around 30K records, and the other 40K records) to see if we can tease a little more information out of them.

Then we’ll run this on other twitter communities, and random twitter seedlists to see how (if at all) the networks differ. Are PR people more introspective than the rest of the twitterverse?

This is a very high def image, so it will blow up nicely.

High network density in twitter UK PR community

Originally uploaded by matmorrison

For this graph we took a list of the top 50 PR twitterers as measured by Stephen Waddington (Nov 2008). We added “twitter*” to the canonify exception list to identify individual twitterers (this isn’t perfect — the regex may need some tweaking) and further limited the crawl to domains that contained the word “twitter” using the Whitelist function.

Again – look at how dense the network is here.

Reading RUFUS data with yEd

Originally uploaded by matmorrison

Most of the time we use UCINET and NetDraw to analyze the data from Rufus. Rufus exports crawl data to a Pajek .net file by default. But we can also export GraphML and read the data into other tools that handle that format. This is a test we ran of this feature using yEd

It’s not working beautifully yet, but it is working.

Porter Novelli has been working on its own “online influencer mapping” tool for about six months now. Recently, I’ve started posting screen grabs on our Flickr page to see what people think about it. I thought it was probably time to share some of the images here.

Version 3.5.4 (Always in Beta)

The project is named Rufus after the character George Carlin played in “Bill & Ted’s Excellent Adventure”.

For those of you who know how network analysis works and what it’s used for, this is revolutionary only in that it’s fast and accurate enough to use as an exploratory tool.

For those of you who have no idea what network analysis is or how it’s used in many, many situations, 2009 would be a really good year to start finding out.

For this graph (which took around 5 mins to generate), we took as a seed list the first 50 back links as generated by Yahoo Site Explorer (http://siteexplorer.search.yahoo.com/.) We’ve tested this up to 100 seeds, but there’s plenty of room to go further.

Taking the data from the previous crawl, I’ve used AnalyticTech’s NetDraw to remove the pendant data and one or two obvious data blips (web services like Feedburner that have a high background presence in most maps).

I’ve also set NetDraw to calculate centrality measures, then size nodes by indegree (citation frequency) and colour them by betweenness (I needed to do a few adjustments to make it look pretty, too…)

We use indegree as a measurement of “authority” within any given data sample. What we can say is that more sites in the sample link to the big circles than link to the small circles. Since they’re all talking about the same subject, it’s generally safe to say that the big circles are more authoritative. But in practice, we have to manually remove a few “noisy” sites that have a high probability of being linked to no matter what the subject matter — generally advertising networks like DoubleClick, measurement sites like Google Analytics or Quantcast, and bookmarking sites like Digg, Delicious, and Reddit)

Betweenness is a useful proxy for “influence” — you can see that (in this particular data set) the big media aggregators like Engadget, the BBC, ZDNet etc. show up much brighter than the other nodes.

The GNU foundation is startlingly bright, but the SourceForge datum will probably change when we get the canonification process working in v3.5.5

Version 3.5.5 (Still in Beta)

The canonify function seems to work now, but I was startled to see that the clump of sites that I thought were SourceForge were, in fact, the output of what is probably a grey-hat search engine site. Oops. Well, we’ll take that faction out of the calculation…

Rufus has a pretty good blacklist and whitelist function: the blacklist excludes sites that we don’t want to crawl, and the whitelist is like the guestlist: it restricts the crawler to a certain set of sites.

When we process the data to remove pendants, sites that are eye-catching here (like the spam site) shrink into nothingness. It’s not the outbound links, but the inbound links that count. Since the grey-hatter has no inbound links, he’ll vanish in a puff of smoke.

We just ran a crawl on a bunch of green-issue bloggers. Because this bunch are serious cross-linkers, it highlighted an interesting new bug.

The canonify-exception list function works for the nodes (so we can trap information about subdomains when we choose to (*.wordpress.com, *typepad.com, *blogspot.com for example) but doesn’t appear to store edge information ("edges" are the links between two nodes)

The result? We’re seeing a load of isolated nodes (isolates).

Normally the only way you’d see an isolate is when a site on our seed list doesn’t link out to any external pages. This happens more than you might think when we’re looking at corporate and product marketing sites, but there’s a theoretical maximum number of isolates N, where N is the number of sites on the seed list.

So we’re back to the drawing board on this particular function… Roll on version 3.5.6

Version 3.5.6 (The Eternal Beta)

Hooray! The problem with the isolates has been fixed in this version, and we’re cooking on gas once more.

Or perhaps – because this is a map of green bloggers and their influence landscape – we’re cooking with a low carbon-emission renewable fuel.

(Lots of credit and thanks to: Darrell for invaluable advice, Nick and Stuart for tireless development, to Kerry & Andy B for beta testing, and to Jean and Gary for funding and vision)

This is a description of Billy Graham crusades from an academic study I’ve been reading. I’m interested in how real evangelists work (after all, I use the term often enough when talking to colleagues and clients):

Counselors begin their work after the singing, testimonials, collection and Billy Graham’s sermon, which culminates in the altar call. At the moment of Graham’s invitation to “come forward to Christ.” counselors and choir members begin moving forward to an area usually in front of the speaker’s platform or rostrum. To a naive member of the audience or a television viewer, this movement creates an illusion of a spontaneous and mass response to the invitation. Having been assigned seating in strategic areas of the auditorium or arena and given instructions on the staggered time-sequencing for coming forward, the counselors move forward in such a fashion so as to create the illusion of individuals “flowing” into the center of the arena from all quarters, in a steady outpouring of individual decision. Unless an outsider or observer of these events has been instructed to look for the name tags and ribbons worn by those moving forward it is all too easy to infer from these appearances the “charismatic” impact of Graham and his invitation. These strategies promote the respectability of making a public commitment and represent methods calculated to manipulate the consent of the passive, the uncertain, the wary, and the indecisive.

(from: David L. Altheide and John M. Johnson, Counting Souls: A Study of Counseling at Evangelical Crusades, The Pacific Sociological Review, Vol. 20, No. 3, (Jul., 1977), pp. 323-348)

Momentum

A recent (and criticised) study by Tubemogul on the short shelf life of online video reminded me of some research into views on YouTube videos I did back in 2006. I only looked at about 130 random YouTube videos for the first 20 days of their life cycle, while TubeMogul’s methodology was somewhat more sound (they tracked more than 10K videos for around three months, among other things.)

Here’s the chart from my analysis:

And here’s the chart from theirs:

You can see that it was pretty easy for me to accept their findings.

What does this have to do with evangelicals?

It’s a point I haven’t quite got my head around (that’s what this blog’s for, I’m afraid – to share what I’m thinking about, not to share what I’ve already thought out.)

I’m interested in the idea of momentum (something that my colleague Marian Salzman first brought to my attention.)

By creating the appearance of acceleration (using pre-briefed volunteers and “staggered time-sequencing”) the evangelicals put on a better show. They pull people with them.

Does the gradual build ultimately deliver more volume than the fanfare? What are the timelines?

Apologies: some very poorly put-together thoughts here. More – perhaps – later.

Later

You know the way, when a new social networking tool appears, first one email, then a few, then a dozen of emails from your friends arrive asking you to join them? Or videos always used to be on YouTube, but these days you’re seeing more Vimeo in your mix? Or (for the geekier among you) open source projects were always on SourceForge, but now you’re seeing more on Google Code.

It’s that sense of movement, of a growing surge that we notice, I think, and that can foreground a new entrant in a market heavily dominated by another player.