Yes, what you are describing for Flickr is a classic diffusion process. Somewhere in the book the authors describe the characteristics of quick adoption, and visible use is one of them.

The cycle is a ‘typical’ example of a family, not the only pattern. The book lists some variants, like a two-peak curve (with a second small peak at the start of the slope of enlightenment) and ‘high-flier’ vs ‘low-flier’ curves for higher/lower plateaus etc.

I think the curve is likely to be a sort of fractal curve in reality, the somehow folds in lots of smaller cycles of peaks and troughs, but the way Gartner tracks it is based primarily on media reports I believe, not on every bug and every release version of s/w. That, as you say, would be a very noisy signal.

Tom:

Good point about survivorship bias. The book does suggest that lots of hype cycles never make it past the trough, such as in the case of the more vacuous management fads. They do not propose any leading indicators at the trigger/peak stage (such as “if all early adopters have green beards, the thing will fail in the trough.”).

Overall, I don’t view the hype cycle as a particularly good predictive tool. Though the authors have bits of the book devoted to “spotting” the peaks and troughs, they aren’t very convincing. I think it is best to treat the hype cycle as a conceptual and retrospective-analytical tool, whose evolution can only be observed with a short lag.

Predicting the peak/trough and especially the onset of the slope, without fundamental analysis of what the curve is tracking, is obviously going to be as difficult as timing the stock market. But with technological analysis, it should be possible, since developments will actually mean something.

I think the best way to understand the value of the cycle is to compare somebody, say A, who thinks with the HC model vs. somebody, say B, who believes a more naive model (say monotonic exponential or worse, a ramp or step function hype signature).

At the very least, A sees and can take advantage of one opportunity B cannot: the equivalent of shorting a stock, by investing in a technology via fire sales after a peak, getting stuff dirt cheap and then persevering through the trough.

Venkat

I wonder about the curve applying generally to many web 2.0 services that evolve via a beta model… would you say that each release has it’s own curve? I mean, many of the 2.0 applications out there are launching, developing, and (hopefully) improving, at a rate which creates a volume of information that is fairly unmanageable for individuals… and so the channels of distribution tend to be more non-traditional. I wonder how the curve will hold up as the rate at which technologies develop becomes increasingly exponential and the noise that occurs when information starts to exceed capacity begins to make it even more difficult to keep up with new developments…

I think a good example would be flickr. I wouldn’t say that flickr is still considered the darling of early adopters, my parents use it. I would argue that they have steadily grown the user base by focusing on quietly improving from the get go, and relying on the web’s “grapevine” to spread the word. I really don’t recall a real flickr hype, personally (other than the Yahoo purchase, but is that the same kind of hype?), just running across a friends stream and thinking “oh, neat.” I certainly never saw flickr advertised anywhere… and I’m willing to bet their adoption rate has been pretty steady.

agreeing that it’s not the best way to gauge:

http://www.google.com/trends?q=flickr%2C+second+life&ctab=0&geo=all&date=all&sort=0

Tom K