Diffusion in PR

Just a brief intro before I crack on. The last post was a bit theoretical and long and this is largely the same but probably more complicated, longer and certainly more difficult to explain. Before you read this ridiculously long post (it should be three or four different posts to be honest), a word of warning, there are more questions than answers – I’m just reading interesting stuff and testing it out. Don’t be hating.

Diffusion

In this post, I want to explore how time affects how a new idea or product is spread within a network and whether it has any actual practical uses for PR professionals. The general consensus is to use the term ‘innovation’ (for the new thing, idea, product or tool – if you are Zengestrom) and ‘adopt’. For those who read the last post, Markov centrality is about how quickly a message spreads through a network. The idea of adding a time dimension to network analysis is further explored by ‘diffusion’ which is “the process by which an innovation is communicated through certain channels over time among the members of a social system” (thanks Wikipedia). You’ve probably heard about Diffusion of Innovations, which explores adoption and much more in detail.

Diffusion works on two main assumptions. Firstly, that social relations is one of the most important channels for contagion and persuasion. Known as social contagion it implies that when you connect to people they also influence you. The assumption that social ties are important is because with an innovation, there is thought to be a risk associated with it because it is new and therefore an unknown quantity. Depending on the situation, family members, friends, colleagues will all exert different levels of influence and the individual will have different thresholds for adopting an innovation depending on what it is.

We’re nowt but sheep

Secondly, the more a person is exposed to an idea, the more likely they are to adopt it – think about all your friends who have purchased an iPhone based on the amount of nagging from iPhone users. This in turn, results in a self-perpetuating effect where the idea gets even more exposure and thus more adopters.  An example of this would be the web, where popular websites continue to attract more links than less popular ones.  Often refered to as preferential attachment, it is a case of the rich getting richer. In the PR industry a blogger starting his blog will probably include popular blogs such as Stuart‘s or Richard Edelman‘s blogs (both of which were created before I was born) on their blog rolls because they have heard about it from their peers. (It is also worth reading about information cascades for further evidence that we rarely make a decision on our own)

Social pressure also plays a part during the adoption process, especially if the innovation becomes popular or cool. This could be due to peer pressure and overlaps with the idea of social currency – where people do stuff to talk about it – such as buying the latest CDs or in my case, reading The Sun every morning the week before I go see my friends in Bradford. Using the example above, the new blogger may want to be seen to be reading Stuart’s blog because it’s what everyone else is reading and it also gives the blogger something to talk about/share when he meets like minded people at the various networking dos.

The pressure to adopt an idea might also be a practical one. According to Metcalfe’s law “the value of a telecommunications network is proportional to the square of the number of connected users of the system (n squared)”. Without paying too much attention to the maths (n squared should be seen as a generalisation rather than an exact formula to work out ‘usefulness and that’), you can see that during the 90’s, as more people adopted email systems in their communications it became increasingly difficult for others not to adopt. This effect can also lower individual thresholds for adoption, making it less risky for people to try new ideas or products. You could argue that Twitter has become less useful as ‘people’ like Megan start using the channel – I’ve certainly found it less useful for information since I started following more people. However, saying that, if more of my mates from Bradford starting using it, that would change and Twitter could become  useful for finding out who has just had another kid, who’s been in jail, who’s managed to use a computer for something other than porn, etc.

Super susceptible vs. Super influencers

By understanding how the diffusion process works, one of the things we can do is identify groups of people who are more suseptible to persuasion and target them instead of the traditional “super influencers”.

In a directed network, nodes with more inbound links are the ones more likely to be ‘infected’. The ‘exposure’ of a node may result in the person simply having more chance of hearing about the product or could lead to more intense social pressure to conform. (I’m ignoring for this post the idea that each person has different thresholds when adopting an innovation, and the type of innovations itself). In a real world setting, it you were looking for a prominent tech blogger, the bloggers who are more likely to have their head turned are those that also subscribe to a large number of relevant blogs. Note the use of the term ‘relevant’ – as in relevant to the network you are targeting. If the blogger reads a large number of blogs that are not relevant to technology, for example, then they are less likely to conform because they are likely to have more influences. In an extreme case scenario, Apple releases an update for its iPhone and lets the users of various Mac forums know how amazing it is. Members of all the forums will be bombarded with positive messages and those that subscribe to a large number of these forums and little else will have very little evidence to distrust Apple’s claims that the new iPhone makes you 30 per cent more interesting to other iPhone users (to everyone else you are 70 per cent duller and geekier – it’s a made up fact that since Mat got an iPhone, 80 per cent of our conversations is spent talking about apps).

This is also linked to the strength of weak ties concept I mentioned in my last post. The people who have more ‘weak’ ties – i.e. peripheral to a network – are likely to have more information at their disposal (one of the reasons the UK PR social media scene often feels like an annoying echo chamber) and therefore less likely to conform (although conversely – and I know I’m contradicting myself here slightly, they are more likely to be early adopters of new innovations). Again, using the example above, those that read a more diverse range of forums, blogs, etc are more likely to be informed of flaws and/or alternatives to the latest iPhone update. What I’m saying is that those with many weak ties are less likely to conform to norms and more likely to try new ideas, products.

Using tools such as Pajek, we could find whole groups of people who will be more likely to adopt the innovation. For example, you could work out how centralised (connected) a network is – a network with more connections means that messages will spread more effectively. If it is less connected and full of bottlenecks (often called cut vertices and bridges – nodes and lines which when removed disconnect a network completely) then the messages will take longer to spread if at all.

PR 2

Above is a quick sociograph of the PR comms industry. Using Netdraw (software which let’s you analyse networks), I’ve resized the nodes so that the more blogs they link to, the larger they are. Therefore we can see that Richard Bailey, Tom Murphy and We Are Social new boy, Simon Collister appear to be more susceptible to change.  There are also two areas of interest – the one to the right of the centre appears to be a group of educational-focused blogs.  Please bear in mind that this diagram was literally created in minutes,  I took Brendan Cooper’s PR Index and mixed it with Jed‘s blog roll. Hardly scientific, it’s probably far from accurate and what’s to say that Simon is actually still reading blogs? I just wanted to illustrate what I meant and just realised that this blog post was well text heavy.

*Minor update – I’m getting confused between new innovations and adopting ideas here. People on peripheral of network (strength of weak ties) are more likely to adopt new ideas/products, people who have a lot of influencers are more likely to conform. At least I think that’s what I mean…

What do you do once you have identified these areas of high susceptibility? I think it would help in a campaign where we needed purely numbers – i.e. we need X amount of coverage (which is still often the case in PR). We would seed the information in positions with a high centrality (Markov or otherwise – though the recommendation from the brainy people seems to be nodes with high betweeness centrality) so that the message would spread to many people.  By analysing the network and working out how susceptible it is to change, we can alter our tactics accordingly. For example, memes would spread much more effectively in a connected network such as the PR blogging network in comparison to a network of mid-level accountants – who, if you were trying to reach, you might use one to one meetings.

It would also help when setting goals for clients. Blogger outreach programmes are still an unknown quality – some work, others don’t – it’s due to the individuality of each blogger (and often your relationship with them) as to whether it is a success or not. The traditional media is much more predictable and established. Although you are essentially still selling your story to an individual when pitching to traditional media, ground rules have been set and they are much more consistent with what they are writing about. However, by identifying areas of high susceptibility, you could get a better idea of how a campaign will spread in a network.

Critical mass

Now I’ve hopefully established that what I’m writing does have some foundation in truth, let’s look at how it could be useful.

There’s one aspect of diffusion, which I’ve found particularly interesting and hopefully relates to our day-to-day PR trudge. During some instances of adoption, something called Critical Mass occurs, which is the point where there are a minimum number of adopters needed to sustain the process. It’s a tipping point (notice the lack of capitals – I’m not referring to Gladwell’s often criticised theories, I’m using it as a general term), when the innovation has enough momentum for it to reach the whole network and sustain – and fuel – its own growth.

It is similar to the way bacteria develops – either it does not develop quickly enough and anti-bodies wipe them out, or it grows quickly enough that it multiplies and overwhelms the body’s defence.

If networks are critical to adoption, there are a couple of rules of thumb. Bear with me here. When the idea has been adopted by between 10-20 per cent of the people who will eventually adopt – (it is no surprise to find that innovators and early adopters/opinion leaders represent 16 per cent of consumers and falls nicely in there) the acceleration of adoption rate decreases, although the adoption rate still increases. This is known as the first second order inflection point. Basically, people are still adopting the innovation but not as quickly. At this point, social cognition takes over the diffusion process and adoption reaches its critical mass – it has reached the point where enough people have adopted the idea that it self perpetuates (because of the some of the points highlighted earlier – social pressure, exposure and preferential attachment, etc), fueling its own growth. Random activity of unrelated events becomes seemingly more predictable as a kind of self-organisation takes over.

This is Roger’s innovation adoption curve shows what the diffusion process looks like:

*Stolen from http://www.mitsue.co.jp/english/case/concept/img/02/fig1.gif

The ‘S’ shaped curve being the cumulative rate of adoption (or diffusion curve) and the bell curve being the number of new adopters. So in an ideal rate of diffusion, the point of critical mass (which those Japanese fellas have labeled as the diffusion rate line on this graph) is around the point of the first inflexion in the ‘S’ shaped curve and is about 10-20 per cent the total number of adopters. Theoretically then, at the first inflexion we can assume that the total amount of eventual adopters will be between five and ten times the number at that point. As with triangles and the distance/speed/time diagram in physics,  if we knew the eventual number of people who will be adopting the idea (i.e. the client has set a goal of X number of people), we can predict what the critical mass should be and plan our comms activities accordingly. Am I making sense?

Facebook Ads and critical mass

Let me show you a real life example. I recently created a Facebook fan page for a client. We had no budget for Facebook ads and much of our traffic came from Twitter. I’ve tried to take into account the number of fans who joined the group as a result of an email from me or from Twitter (I used cli.gs to track as much as I could) but it’s far from perfect.

The graph looks like this:

Chart Facebook

And the numbers were as such:

Facebook fan

*Adoption rate is the number or percentage of new adopters at a particular moment in time; and the Acceleration is how quickly the innovation is being adopted.

At Time 3 the total number of fans is 29. Therefore, we could estimate that the number of eventual fans to be between 145 and 290 (the mean of which is 217.5). For this particular campaign, we had no targets set for the number of fans (and who’s to say that a fan page needs X amount of people to be successful?), but sometimes the client wants a certain number of fans for it to be deemed a success. So for example, if the client wants at least 1000 fans, at that point of critical mass we can see that there is very little chance we’d ever get close to reaching that number.

Creating momentum

The question (and solution?) is, could we then artificially create the required number of people through above and below the line activities to create the momentum needed to reach that goal? In the case of the Facebook Fan page we could create an ad campaign or target relevant groups promoting the fan page, inflating the numbers so that at the first point of inflexion, the number of fans is closer to 200 (and so the final number of fans is around 1000 at least).

Could we do the same during a blogger outreach programme? If the figures were not quite sufficient, we could spend more time targeting more bloggers in the network?

What we are essentially trying to create here is a ‘viral’ effect. I know viral, word of mouth, etc is an outcome not a strategy/tactic – but it’s not some mythical, impossible dream – there are some things we can do to try and achieve that effect. Mat has previously written a post about creating the appearance of acceleration on his blog, which talks about how evangelists do exactly what I’m proposing we do.

Caveats

Here’s a few things to consider and why I may have just waited an hour of your life. Critical mass theoretically exists only when the network is the key driver in adoption – i.e. the innovation spreads organically via social ties and word of mouth. To this end, it could be argued that if you artificially created the appearance of acceleration using above the line activities then you would not be relying solely on networks to promote your Facebook page – your fans would be isolated, unrelated groups of individuals who exist in different networks. However, the great thing about Facebook Fan pages (and why I’m a bigger advocate of them over Facebook groups for clients) is that you can use Facebook Ads to target audiences based on specific demographic and interests. Want to target a married male, aged 23 from London who has an interest in 3D animation? Facebook Ads lets you do that. While two people who have an interest in say, Manchester United may not be directly connected, they will exist within the same network. Studies have shown that people tend to associate with others who have a common interest. Known as Homophily, it is often expressed as the adage: Birds of a feather flock together. Therefore by targeting a specific demographic, you are in effect, targeting whole networks. Have a look at this post, to see many of the decisions we make are governed by our networks.

On the other hand, there is no actual proof that critical mass even exists. Even in examples where the point of critical mass is around 16 per cent of the overall population, it only implies that critical mass exists. There are also many that arguments as to which point in the adoption process critical mass actually takes place (some argue that it is when 50 per cent of all the population have adopted, while others believe that it is the period with the highest adoption rate).

Diffusion, also works on the assumption that innovators and early adopters influence the early majority. While I don’t doubt this, their influence may not be as profound as many believe. The idea that a chasm exists between this group and the early majority has been explored in Crossing the Chasm – a book I haven’t read so do not want to write about it too much. But basically, the network effect is not as simple as my fancy diagrams would have you believe.

Any way food for for thought and that and I hope you try some of this stuff out to prove me wrong. I’ve no idea why I spent so much time writing the second half of this post knowing that much cleverer people than me argue whether it is in fact bollocks.

Good night!

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16 responses to “Diffusion in PR

  1. Thanks Tim, some great stuff here. Will take me a while to digest, but really interesting.

  2. I’m going to name my first born Tim. Even if it’s a girl.

    This is quite brilliant. As with Danny am going to read through it all and digest.

  3. Timmy,

    Echoing the points above, there is some fantastic content here that I think needs to be digested in stages. Will come back to you with specific feedback.

  4. Congratulations Tim, you’ve just been awarded a PhD in Networks and Social Sciences 😛

  5. I’d love to comment but am physically unable to read posts longer than about 250 words. Sure it is great though 😉

  6. You’ve clearly spent a great deal of time reading and reflecting on this topic. What really excites me about this is that we can start to overlay mathematic models on communication plans to predict behaviour and results, per your Facebook model. It’s incredibly powerful. Immediately this makes us credible alongside other peers in the marketing industry. It should see PR shift from an art or craft to an absolute science. Keep up the excellent work.

  7. Cheers Wadds. Agreed, the reason why I’m spending so much time on this is because I think it can change the way other comms disciplines perceive our credibility. Advertisers and marketers have been using these types of models for ages – why can’t they be applicable to the PR industry? In fact it’s probably more relevant because our aim is to identify influencers and thinking about networks and diffusion etc can make it easier/more scientific in a way. However, as Anthony Mayfield pointed out in my last post, working with too much information can render it useless. I think you are still going on gut to some extent.

    Everyone else:
    Apologies, I know it’s a long one. Basically this is how I write posts:
    -I spot something interesting in a book Mat has lent me and wonder whether we can apply it to PR
    -I speak to Mat but struggle to explain it properly, he gets the gist and suggests I look at X blog and X theory
    -I go away and write the post
    -When writing to make sure I’m not an idiot I do more research which contradicts what I have written or adds to it
    -I rewrite the blog post
    -The last two steps are repeated for 5-6 weeks until I just think “balls to it” let’s just throw it out there.

    As I’ve said to a couple of you over DM – I look forward to having a proper discussion with you once you’v read it through

  8. Pingback: Predicting PR outcomes | Wadds' PR Blog

  9. Hi Tim. No hating, I like your “balls to it” approach. 🙂 As Wadds says this sort of stuff could be pretty powerful if you could build a robust case for it. Being a geeky numbers man myself if you are interested/want any collaborative assistance around analysing example data sets give me a shout.

  10. Cracking work here Tim. Whether or not it is bollocks is negligible really, the fact that you’re applying this to PR in a meaningful and accountable way is a big step in the right direction.

    Don’t apologise for length – it’s well worth taking the time to digest, and it’s a tactic that’s good enough for Brian Solis and plenty of others.

    I’ll look forward to seeing how this pans out. I’ve been building a Facebook fan page for Newcastle Uni alumni (amongst other stuff – our LinkedIn group seems to be more self-perpetuating) but I’ve probably blown the diffusion analysis with a load of promoting. Plus I guess it would be different for an (albeit unconnected) existing network?

    Anyway, thanks for this and keep up the great work!

  11. Andrew Bruce Smith

    Tim – well done – we need more people like you applying brains to these issues. We are only just scratching the surface of network science.

    I realise you are probably sick of reading books, but Crossing The Chasm and Inside The Tornado are still worth reading. A key point about the chasm is that the buying behaviour of tech early adopters is completely different to the early majority (hence why so many tech companies fall into the chasm). And the management and marketing skills that get a company to the early adopters are the ones that will often kill it in the early majority market.
    Of course, the Chasm was written in a pre-Internet, pre-social network era (and Geoffrey Moore’s later books have disappointingly not taken much of this into account). Nevertheless, still think there is a good deal to be learnt from Chasm theory.

    As ever, the challenge is working out which variables count in a given context – and whether there is a way to measure and replicate that in a meaningful and profitable way.

    Anyway – keep up the good work – your reward will be, er, somewhere.

  12. @adam – Still a long way from building a robust case for the PR industry to rely on network analysis as the basis of measurement, but as I said on Wadds’ post, I just wanted to get people thinking about what they were doing – not necessarily take my posts as authoritative sources. I’d be the first to admit that I’m just winging it but it’s so bloody interesting.

    @danh right this bit I’m writing is without research but I don’t think in your case there should be anything wrong with promoting the page especially because I suspect your target group are largely connected in their network anyway. It’s just a case of working out whether a sharp rise in adoption is a minor hiccup or not.

    @Andrew – Crossing the chasm is definitely on my list and i really wanted to explore it much more but as you can see the post is already too long and has taken me ages to write and I had to leave it somewhere. It’s interesting though and might be something I look at for my next post but only if I can add something to the debate or apply it to real life PR. There’s little point in me regurgitating what someone much cleverer has already written.
    As you rightly point out, the skill to PR and all of this is knowing which variables will work for the client’s goals, one of the reasons why I’m still confused as to why the measurement of social media debates continues (I’m not saying we have the answer, I’m just saying its a case by case basis which can’t be ‘solved’).
    My reward is the satisfaction that I’m allowed to write like a child on my own blog post, test stuff out and question everything. These and my great haircut is enough reward for me

  13. Pingback: Cool stuff – September 21, 2009 — Danny Whatmough.com

  14. Hi Tim! Now I finally understand all those “sketches” you drew out for me!! Just direct newbies to your blog?! Might have to print this one out and read it on the commute! Take care x

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  16. I was recommended this website by my cousin. I’m not sure whether this post is written by him as nobody else know such detailed about my difficulty. You’re wonderful!
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