mediaczar

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.”

Continue reading ‘The #interestingOPMLexperiment (stage 1)’

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.
Continue reading ‘Thinking differently about word-of-mouth’

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.”
Continue reading ‘Social marketing, and why we shouldn’t talk to strangers’

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.
Continue reading ‘Some Twitter Social Network Analysis’

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.

Continue reading ‘A sneak peek at our online influence mapping tool’

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: Continue reading ‘What we can learn from the real evangelists?’