There are 2 classic aspects to cover when we think of innovation – diffusion and adoption. We explore these classics with a twist. We consider a network first approach to understanding and manipulating diffusion.
And there is 1 additional aspect that belongs in this group of classics – resistance. Yet we often forget or don’t know of it. This has a good potential of damaging your innovation prospects.
The classic definition of diffusion is that it is Roger’s: “the process by which an innovation is communicated through certain channels over time among the members of a social system“.
We explore that definition by considering the social system as a network in Diffusion of Innovation. That allows us to look at Bass’ model where we see the social system comprising of innovators, who are externally influenced, and the internally influenced imitators. Building on this aspect from Bass, we can now look deeper and see Rogers’ five adopter types in context (the innovators, early adopters, early majority, late majority and laggards). As well as Rogers’ adoption curve.
As we consider the network behind diffusion we find it is several connected networks. There is friction, or gaps, between these networks. In particular, Moore shows us that no adopter type is persuaded the way the adopter type to the left is. And in particular we have to be crossing the chasm created by the early majority not trusting the early adopters. Malony’s 16% rule takes a marketing view of this chasm. We must change the message and medium after the early adopters. From a message of scarcity through exclusive mediums to one of social proof through mass marketing.
Gladwell brings us back to a network view with his Tipping Point and the law of the few. That in any social system there are connectors, mavens and salesman. Can we use network metrics to find or engineer these entities?
In Diffussion of Innovation II: Bass Diffusion we get hands on with Bass mathematical formula to see how we can predict innovaion diffusion. We see how to get co-efficients of innovationand co-efficients of imitation from existing sales data that we can the use to predict how new innovations will behave. The maths allows us to refine Rogers’ adoption type sizes allowing us to set better marketing budgets for our external and internal marketing. We can forecsast peak and total sales, get the right supply chain, understand waiting time in case of undersupply. Determine when to launch the next generation of an innovation, where to best apply offering samples of products, the impact of piracy and impact of patent infringement as well as assess expansion opportunities. Even how to estimate value of business based on market penetration.
Why do we get Resistance
How do we minimise resistance?