Posts Tagged ‘influence mapping’
Thinking differently about word-of-mouth
Tuesday, June 30th, 2009
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:
- 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.
- “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.
(more…)
Tags: influence, influence mapping, marketing, network analysis, public relations
Posted in influence, networks | 8 Comments »
Relationships between “top 50″ UK PR twitterers
Tuesday, December 16th, 2008
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.
Tags: citation analysis, influence, influence mapping, network analysis, networks, twitter
Posted in influence, networks, twitter | 4 Comments »
Map of top 50 UK PR twitter people and their followers
Saturday, December 13th, 2008
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.
Tags: citation analysis, influence mapping, mapping, network analysis, networks, public relations, twitter
Posted in influence, networks, twitter | 2 Comments »
A sneak peek at our online influence mapping tool
Thursday, December 4th, 2008
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.
Tags: citation analysis, influence, influence mapping, network analysis, networks, porter novelli
Posted in influence, networks, porter novelli, rufus | Comments Off










