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Influence Mapping: The Maverick Cop Way (Part 2)

The story so far: In the last episode of Influence Mapping: The Maverick Cop Way, we discussed a simple process for organizing what you know about influencers. We discussed briefly the decision making unit model we were using, our (very broad) definition of “influencer”, and showed how we can score them quickly for the three key variables reach, authority, and “ease-of-use”. At the end of the process, we found ourselves with something like this:

Now we’re going to go a little further, and show how we can map the relationships between the various stakeholders. This is the second and final post, and it may introduce a lot more that’s new to you. But stick with it – it might be worth it.

What you’ll need before you start

You’re going to need UCINET, a programme that analyses matrices and networks. It comes along with another programme from the same publishers (Analytictech) called NetDraw that draws networks. You’re going to want both. UCINET costs $250 for a corporate license, but the first 30 days are free. NetDraw is a free download.

They only work on Windows, but I’ve not experienced any real problems running them on a Mac using Parallels.

1. Create a matrix

Take your list of influencers (as per the table above), and add in the four key players from the decision making unit; the initiator, the decision maker, the purchaser and the end user.

Paste them down the left hand side of your table, and along the top edge, as in the illustration below. Excel’s Edit > Paste Special > Transpose command is useful, not to say essential, when you’re doing this. At the end of the process, you should have something that looks like this.

The rows show the influencers, the columns show those being influenced, the targets.

Because we don’t think that an influencer can exert influence themselves we drop a line of zeroes down the diagonal. This is more to help us navigate than anything else. Don’t feel you have to do this.

2. Fill in the matrix

Go through the rows one by one, deciding if a given influencer has any effect on the targets (the column headings). So for example, we know that the initiator influences the decision maker, and so on. But knowledge of the end user may well influence the decision maker, too. So – for example – when I’m choosing my dad a computer, I take into account the fact that he’s not so au fait with technology, and that if he can’t use it, he’s going to call me to ask for tech support. So I’d better choose something that will limit these calls.

At the end of the process, you’ll end up with something like this (although probably much bigger). It can take quite a lot of time to go through this process – this may be one of those times when you want to work with a partner to bounce the ideas around.

3. Import the matrix into UCINET

You may need to fiddle with the settings here. But you should end up with something like this. Forgive my rather antiquated looking Windows interface – I turn off lots of stuff to make sure my emulated PC runs as fast as it can. Yours will obviously look much nicer.

That’s all you’re going to do in UCINET — we’re just using it as a conduit between Excel and NetDraw (a woeful misuse of its many talents)

You’ll export it as a UCINET .##h file – one of the formats that can be read by NetDraw.

4. Import the matrix into NetDraw

This is pretty straightforward.

You should see something like a messy cobweb. What this map shows you is how information (advice, opinion, editorial, brand messages) flow around your audience’s sphere of influence. But let’s tidy it up, by using the Layout > Circle command. Now you’re seeing something like this:

You can play around with the node and label shape, size and colour and so on to make it look nicer than this.

You can export the diagrams as Enhanced Metafiles (.emf files) that can be opened by things like Adobe Illustrator for tweaking.

5. Add your “Opportunity to Influence” scores

Remember the scores from Part 1? Been wondering what they’re for? Well here’s where you use them. Pull up the Node Attribute Editor (Transform > Node attribute editor), and add a new column. Enter your scores.

OK. Now go back to the diagram and use Properties > Nodes > Symbols > Size to change the size of the individual nodes to reflect the scores. You should now see something like this:

You can play around with the relative sizes, but it’s pretty clear who the important opportunities are in this network.

5. Establish who has influence in the network

NetDraw can do some analysis for you, to establish which influencer has most influence within the local sphere of influence. Using Analysis > Centrality Measures you can calculate and add scores for things like Eigenvector Centrality. If you’ve bothered to read this far, you probably have some kind of idea of what this is, but it’s probably enough to know that Google uses a version to rank pages for authority. It’s a useful measure.

NB: You want the directed kind: information is flowing in one direction through these links.

In Eigenvector

Change the node sizes to reflect In Eigenvector, to see who’s receiving most information and you should have something like this:

Out Eigenvector

Go back and change the node sizes to reflect Out Eigenvector, to see who’s receiving broadcasting most information and you should have something like this:

Betweenness

Change the node sizes to reflect Betweenness – Directed and you’ll see yet another version of the map, one that gives you an interesting picture of who channels the most information:

Compare the Fingerprints

Oh – alright. Maverick cops don’t really care about fingerprints, I suppose. They leave that up to the SOCO or the CSi people or Forensics. No doubt they slightly patronize them, too.

But my Maverick Cop metaphor has really begun to break down, and I don’t think I’m going to get it back.

Look: you’ve now got four maps, all telling you slightly different things, giving you slightly different perspectives. It’s taken less than a couple of hours. Put them all together, and you can see how different roles exert influence in different ways.

The maps above are based on flawed data (as I said in the last article, it’s more important that the examples help you catch my drift than it is that they convince you of the accuracy of the approach. Accuracy, after all, is up to you. And as I say, you can always improve the accuracy of the maps as you get more and better data comes.

How could we improve this?

Well – we’ve only been acting on the nodes. There are ties, too – the arrows and lines between the various influencers. A more thorough model would give these values, based on (for example) things like trust, or proximity, or media consumption or something…

But this is a work-in-progress blog for stuff that I’m doing that won’t necessarily be seeing the light of day any time soon.

Should I really expect you to try this out? And if you do, and find it useful, should I expect to hear back from you: what you think; what worked; what didn’t; how you’ve improved it? I don’t know. But I’d be pleased if you did. Even if you don’t want to try it out, I’d value your opinion.