Pragmatism organizes the technology adoption lifecycle (TALC). While the TALC is usually represented by the normal distribution summed into the normal we use to summarize what’s going on. We see the phases, the larger scale pragmatism outcomes. Not the smaller scale pragmatism outcomes within the phases, the pragmatism slices.

To begin in the beginning, we illuminate when we don’t have a sensor that can detect a signal. Otherwise, we go straight to the sensor, which gives us data in some range. We might have to clean it up. For a normal distribution or a Poisson distribution, we count up how often a value occurred, or the arrivals of values.

Eventually, we end up with a distribution or an envelope for randomness. That distribution houses the “noise.” We captured the data points. We summarize the data points into parameters that determine the shape of the distribution we are using to summarize our data. We make a standard normal with just two parameters: the mean, and the standard deviation. With three pairs of numbers, we have the three normals of the TALC covered.

The TALC is a system built on noise. Yes, sorry, but sales is a random process. Marketing likes to think of itself as a methodical organization. Marketing discovers prospects, nurtures prospects, uncovers the buying process and the participants in the buy, and once the nurturing process moves all of those participants into the “I want this,” state, they set the appointment for the sales rep. Then, sales throws that lead in the trash.

While marketing was busy with all of that, sales picked up the phone and random walked themselves to revenue. And, finally, having sold, management tells the sales rep that they can’t do the deal because the prospect is an outlier. Just another day in the war between marketing and sales.

The TALC is anything but random. The TALC is a highly organized stochastic system. It’s like a radar. A radar sends out noise in a given distribution, a physical one. Only the frequencies that fit in the pipe make it to the antenna where they are transmitted. Then, they bounce off stuff and get back to the antenna where they again have to fit in the pipe. Outliers are trashed. In a company, that outlier prospect moves the population mean too far at too high a cost, so the company refuses to sell to them right now. A few years from now that too far at too high a cost problem was so yesterday.

Marketing already knew that. But, marketing is not random. Marketing has to be pragmatic when it faces a population organized by pragmatism. All that population wants is a business case that makes the buy reasonable. Reasonable is the real organizer. Jones bought this and got a hell of a success from it. But, you know us, we are not like Jones at all. Jones is an early adopter. We wait, not long, but we wait. We want to see the successes of businesses like ours. Jones is too early for our tastes. Just like sales is too early.

That the TALC is based on a summed set of normal distributions doesn’t help either. Those normals make this a stochastic system. The prospects do a random walk towards up. And, we do a random walk to out qualified prospects. “Qualified” filters those prospects. But, so does pragmatism.

I read across the “Markov Chains: Why Walk When You Can Flow?” blog post on the Elements of Evolutionary Anthropology blog. Twitter random walks all of us. This post is about random walks.

The author started with an application demonstrating a random walk under a normal distribution. He shows the next attempted step in the random walk with a vector that is either red for failure or green for success. When the vector is green, the next step is taken, which results in a new data point being added to the distribution. When the vector is red, the data point is not added to the distribution, and another step is attempted.

I annotated the author’s figure to show where the outliers sit, the Markov chain underlying the Metropolis-Hastings random walk and the TALC phases.

On the y-axis normal, I indicated where the data generated by the random walk are either over or under the expected frequencies. Then, I added a hypothetical path via the green vectors. I colored the outliers in gold, but later I realized that there were more outliers beyond the six sigmas of the normal representing the talk. I used the red circle to divide the additional outliers from the non-outlier tail of the normal.

Then, I labeled the TALC. That labeling might be unfamiliar. From the left, EA is the early adopter; C is the Chasm; V is A vertical market. The bowling alley (BA) is comprised of the early adopter and their vertical. The Chasm guards entry into the vertical. The technical enthusiasts are present across the TALC, not just at the beginning, so they have their layer. Their layer included the cloud form-factor (C) as part of the technical enthusiast layer. This population was formerly considered to be phobics (P) or non-adopters, but the disappearance of the technology and admin-free/infrastructural, aka somebody else’s problem presentation fits the needs of phobics. Then starting at the right again after the vertical phase, at the tornado (T), enter the early mainstreet (EM), otherwise called the horizontal (H) or IT horizontal phase. Next, we enter the late mainstreet (LM), otherwise called the consumer phase. We exit the late mainstreet one of three ways: the M&Athrough a second tornado (T), or by moving through or to the form factors of the device (D) phase, and the cloud (C) phase. NA here means non-adopter.

We may extend the life of the category by going down market. The gray outermost circle represents the extent of the down market move. This is where Christensen disruptions live, in the down market. They live elsewhere as well, but all of them are firmly anchored in the late mainstreet or consumer phase. Foster disruptions require discontinuous invention and innovation prior to the technical enthusiast phase.

I further illustrated progress through the TALC with thick red and blue arrows. Discontinuous innovations need the full pathway starting with the technical enthusiasts (TE) phase. Continuous innovation can start anywhere. These days it is typical to be in late mainstreet (LM) leaving a lot of money on the table, but the VCs investing there only know that phase, so they do not reap the returns that paid for everyone else. Cash is the game in the late mainstreet. B-schools preach the late mainstreet with its steady long-term commodities and the sport of competition.

The extent of the downmarket is shown with the light blue horizontal lines and the angled line that denotes the end of the category. The line the company going downmarket ends up on depends on how far downmarket they went. The end of the category depends on the extent of the downmarket move as well.

The author talks about the efficiency of the next step in the Markov path and how one explores only the areas under the normal that need to be explored. So his next figure takes a random walk around a narrow ring under the normal.

In this figure, you see one phase of the TALC being rotated around under the normal. This would be the technical enthusiasts in their phase and the phobic or cloud phase. We find the next data point less often, less frequently, but the frequency of a given data point would be the same if a normal was used, but the overall process is faster when the area being explored is smaller.