When you continuously look over time at your frequency of use long tails for the functionality in your product, you end up with a surface. I’ve not converted my histograms into nice neat equations, but this YouTube brought surfaces to mind. Similarly, you content marketing, financial results, and progress across the technology adoption lifecycle, if you do such things can be surfaces as well.

Requirement fitness can likewise be modeled. This time, another YouTube brought this to mind. Take the original curve to be the customer’s curve, so the second curve represents our having met that customer’s requirements. Notice that the second curve is the content layer of our software as media, and that our underlying technology is not represented at all. Products foster adoption. Products in the B2B early adopter projects are about the client’s content, not the underlying technology whose adoption is being fostered. The shapes of these surfaces will persist in a given population. In the aggregate late main street phase, the B2B early adopters’ surfaces can be brought back as mass customizations, or simpler templates.

Overall, the technology adoption lifecycle looks like a NURBS curve. It shares characteristics of a uniform B-spline as the curves and distributions are different between the bookends of startup and bankruptcy. Given the uniform B-spline works, we can move to NURBS curves to represent the changes in distribution/curves moving from the Poisson game to the vertical’s normal, the horizontal’s normal, and the late main street’s/consumer’s normal. Laggard/device/cloud is probably a normal as well. The Telco normal was said to be 10x that of the first dot com that lived in the early main street phase of the technology adoption lifecycle. Such weight shifts would show up in the number of standard deviations of each of the normals, and in the weights of the NURB curves. A uniform B-spline repeats the k+1 curve until you reach the n-k curve where the curves are symmetric to the first k curves. This repetition illustrates the purpose of management in the sense of not declining on the decline side of the technology adoption lifecycle. Not declining will still look like a decline since the area under the curve still adds up to one no matter how big in terms of standard deviations the normal is or becomes. The probabilities get thinner, the margins decline.

Discontinuous innovation gives you new distributions, new populations of prospects, new revenue sources. Continuous innovation stretches out your current distribution which in turn thins out your probabilities, and rapidly regresses to the mean. Rapidly might mean five years or so, but the typical CEOs tenure is two years, so even a continuous innovation will make its sponsoring CEO into a business press hero.

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