Analysing the technology adoption lifecycle

The Technology Adoption Lifecycle identifies psychographic profiles that help you understand the adoption of new innovations.

Definitions:

Innovators:

Have a problem, know they have a problem, are actively seeking a solution, maybe even cobbling a solution together

Are the easiest to please. As long as the solution you make is better than their cobbled together one, they will adopt it. It is important to focus and do one thing extremely well, as this customer base will become evangelists and help you lure early adopters.

Early adopters:

Have a problem, know they have a problem, are actively seeking a solution

Your solution needs to be a bit shinier to impress this group. They are seeking a solution to their problem, so let the innovators convince them yours is the best.

Early majority:

Have a problem, know they have a problem

This group is not actively seeking a solution, so it is difficult to cross this “chasm”. You need to find and nurture a lighthouse user, to attract other users in this category. Their demands for product quality will be much higher.

Late majority:

Have a problem, don’t know they have a problem

You’ve now got to convince this market they have a problem for your solution. It therefore needs to be flawless. You need to accumulate a good sales pitch from your earlier market experiences.

Laggards:

Have a problem, don’t know they have a problem, even when they find out they pretend not to have a problem

Your highly tuned sales and marketing engine is going to be put to the test. This market is very hard to find, and also to penetrate.

Models

The Bass diffusion model (1969) predicts the growth of disruptive new products that are purchased infrequently (replacement sales excluded):

http://www.bassbasement.org/F/N/FMB/Pubs/Bass%201969%20New%20Prod%20Growth%20Model.pdf

The technology adoption lifecycle:

Wikipedia- https://en.wikipedia.org/wiki/File:DiffusionOfInnovation.png

The graph of adoption from YCombinator:

http://andrewchen.co/after-the-techcrunch-bump-life-in-the-trough-of-sorrow/

Recent data from Gobble:

If we combine the two:

The scale of this graph: 14 months to saturate innovators, 20 months to saturate early adopters, and 14 months to saturate the early majority.

Finally, if we compare this Gobble graph to the adoption of power lawnmowers:

We see that the graph shape in the early days is very similar over the same time period.

The point: can we still use these old techniques to predict growth of modern startups?