mediaczar

For those of you who are wondering what a favicon is, here’s a quick explanation.

Some websites have a little picture that is displayed in your browser’s address bar next to the URL. If you look at your bookmarks list, you’ll probably see a whole collection of these. These are favicons (pronounced fav-eyecons.) The intention behind them is partly ambient branding, but mostly improved usability — your eye will spot the icon in a list of browser bookmarks (called “favorites” by Internet Explorer – hence “favicon”) much faster than it will a string of text.

Victoria was so irritated by my homemade favicon (she tells me that she “cannot keep looking at your head cropped that way…”) that she has just sent me two new ones. I’ve installed one of them, but can’t be sure I’ve chosen the better of the two. So please take a look at the following and tell me what you think.

Sunday February 1, 2009: I’d say it looks like it’s going the way of the initial — while I’ll keep the poll open for the rest of the week and make appropriate changes — I’ve now switched to the “m”

Last weekend I posted a chart of Porter Novelli Twitter folk and their followers. If you read it, you’ll recall that I was dissatisfied by what it implied about the collective reach of Porter Novelli twitterers.
Well, thanks to a long-ish train journey to Bolton and back, I was able to fudge a little perl script together to look through the data to find and remove everything other than the first instance of a follower. Let’s make that a little clearer. Let’s say that we’re looking at three Twitter people, Alice, Bob, and Carol. The first thing to do is to see who follows them:

Now we need to rank them in order of “who has the most followers” (also known as “popularity” as it happens). Here I’ve done that from left to right. Bob has the most followers and Carol the fewest.

And finally we go through from left to right removing all followers who have already shown up on someone else’s list.

Bob, being at the top of the list gets to keep all his followers which may seem unfair. But it’s not unfair if the question we’re trying to answer is “how do I reach as many people as possible by speaking to as few people as possible?” That is, I’m looking for reach (marketing people often express themselves in terms of “reach” — or the number of people who are exposed to a message — and “frequency” — or the number of times the average person is exposed to that message.)

Looking at the example above, we can see that Alice really delivers an incremental benefit of two new people, and Carol only reaches one new person. That gives us a much better idea of how valuable the most popular person (Bob) really is.

Applying this to the Porter Novelli data set

Clearly it would be extraordinarily boring to perform the process described above for the 205 people in the Porter Novelli data set that I want to analyse. But the analysis script that I wrote (with plenty of help from the perl monks) goes through exactly these steps. It’s a pretty straightforward job, ranking and deduping. Here’s what we get.

This makes much more sense than the last run. According to the Pareto principle, roughly 80% of the effects should come from 20% of the causes. Here we see that 20% of the Porter Novelli Twitter users (marked in black) account for slightly more than 80% of the reach (marked in red.) It’s pretty much a text-book example. Things are as they should be, I suppose.

More to the point, we can now assign appropriate value to coverage at the head of the graph. This is of great value when thinking about our media planning and engagement

By the way — if you’d like a copy of either the Twitter follower API query engine (it’s a well-behaved command-line thing that was developed by the excellent Joachim Larsen) or the slightly shonky perl script that I wrote on the train, you have only to ask: I’ll be pleased to share. Send me a tweet at @mediaczar and I’ll send you the scripts.

Using digital channels in tight association with others helps get the highest value from campaigns. All too often though integration is at best an afterthought and at worst ignored.

This is the triangle I draw when I’m trying to explain how to integrate digital comms into a client’s other activities. It provides one way of thinking about the challenges and opportunities that face us, and can stimulate better ideas.

In the interests of keeping it short, this post is going to be pretty theoretical. In future posts I’ll cover some practical case studies and refer back to this post. Think of this as laying the groundwork.

Here — in brief review — is some of what we know about the three corners.
Continue reading ‘5 straightforward ways to integrate your communications activities’

I’m taking a trip to New York between February 3rd and February 5th.

• The Future of Information Architecture– from Hypertext to Hypercontext
  A thought-provoking article from Agency.com's Kai Gani Turner on how the job of the IA has changed in light of recent developments in both technology and user expectations.
  (tags: usability IA user experience)
• 10 Things You Can Do Today to Kick-Start Your Blog’s Growth
  Abdylas Tynyshov's linkbait article succeeds: I've been trying out one or two of these techniques on my own blog, and recognize the behaviour of a few friends as well. I'm actually about to try the StumbleUpon advertising approach.
  (tags: blogging pneo001 prca001 blogs traffic)

• How to avoid being “that annoying PR person”
  Adam Westbrook's short-and-sweet advice to PRs wanting to sell stories in to Radio Journalists. One of the many nice things about blogging is that it exposes conversations that previously were only heard down the pub to new (and interested) audiences. Adam writes a particularly polite version. [via Matt Rebeiro]
  (tags: prca001 pr journalism radio)
• Visualising the Open University's Twitter Network
  Tony Hirst's record of his experiments with with visualizing the network of Open University twitter people that (for me) provide an interesting counterpoint to my own. He's experienced the same problems ( for example, how do you identify members of a network in the first place?) And he's used many of the same tools. But there's some interesting use of treemaps that I've not even begun to think about. Really excited to find this.
  (tags: visualization networks twitter analysis sna)
• I’m a social media specialist…get me out of here
  There's an increasing trend of frustration with the plague of soi disant media experts. It's hardly a surprise – all anyone seems to need is a laptop a few moo cards and enough money for a coffee at Starbucks. This has led both to a brave new world of pioneering marketing thinking, and exactly the amount of below-average me-too hypercaffeinated cluetards you'd expect to find in a new market. Asi Sharabi has had enough. Here's his suggestion for a new TV format I'd love to watch.
  (tags: humour social media)
• Corporate comms have radically changed
  We Are Social's Robin Grant's suggests that — while US Corporate Comms have been changed for ever by the advent of social media, European companies are slow to adapt.
  (tags: pneo001 prca001 social media corporate communications ford motrin)

Yesterday I did a little work with the TwitterCounter API. Today I’ve gone a little further and (purely as an experiment) ranked a list of Twitter people in Porter Novelli by the number of their followers.

What happens if we chart this? Here’s a kind of Pareto chart showing users ranked in order of followers and the total reach that we get at each stage.

If you’ve seen this kind of thing before, it looks wrong, doesn’t it? That red curve should be steeper at the beginning and have longer flatter asymptote. If you’ve ever heard of the 80/20 rule this is one of the graphs that describes it. Normally the head of the graph (the first 20% of the x-axis) controls around 80% of the value while the tail (the remaining 80% of the x-axis) controls around 20% of the value. If you’ve ever heard about the long tail, it’s this tail that Chris Anderson et al. are talking about.

What’s wrong with the data?

It’s not so much the data as what I’ve not done with it. There must be many, many duplicated connections here. So now I need to write something that will go through the followers of all the Porter Novelli Twitter usernames in ranked order, and only count unique (or unduplicated) followers.

I’m hoping that when I re-do the chart, it will look something more like this:

TwitterCounter, the service that tells you how many people followed a given Twitter user on a given date (among other things) has an API – so I thought I’d take a look at it to see whether I could create a quick automated table of rankings.

Here’s the simplest way to query the API:

[code]

http://twittercounter.com/api/?username=mediaczar&output=xml

[/code]

Just cut and paste that into the address bar of your browser for example. Fairly simple. Change the username and you’ll get the data for a different user. Here’s what you get back from the API — an XML file with lots of rich meaty data:
Continue reading ‘Counting Twitter followers’

Partly due to a comment I left on Amelia Torode’s blog, I’m re-reading Neal Stephenson’s 1999 monograph In the Beginning…Was the Command Line. I get a lot of perspective from reading old books. I tend to be too caught up in the zeitgeist to read new cultural commentary with anything like the distance that’s required to draw sensible conclusions.

This particular book concerns itself with the history and nature of operating systems, how they are shaped by and reflect our wider culture, and how (in turn) they shape our society. It makes fascinating reading, but it’s been almost ten years since I last read it and much has changed.

For one thing, the future didn’t work out the way Stephenson (or anyone at the time) expected. Stephenson forecast doom for Apple, for example:

They have had GOING OUT OF BUSINESS! signs taped up in their windows for so long that they have gotten all yellow and curly.

But in 1999 when he wrote this, Apple stock was in fact just beginning to recover from its disastrous performance in the mid nineties.
Continue reading ‘Review: “In the Beginning… Was the Command Line”’

Dan Zarella’s ReTweet mapper “indexes ReTweet streams into hierarchical structures that can be displayed visually”. I wouldn’t have known about this if it hadn’t been for the Retweetist — a project that tweets out the most retweeted URLs, and the Tweetbacks plugin from Joost de Valk that traps Tweets that mention your blog post. That’s a lot of pinging around the web. As if by magic, the views on the US congress Twitter map post have shot up again, even though it’s almost ten days old.

Really quite flattered to have one of my posts tweeted by Jack Schofield. I rather feel like I’ve arrived.