Statistical Independence

March 30, 2015

In the last few posts, I’ve talked about distributions, black swans, chasms, and how these last few things change the size and width of out distributions. See Normal Distributions and a Game Tree, and A Regression Tree.

So lets start with two normal distributions having the usual dependent relationship.

21 Statistical Independence 01

Their footprints overlap. Where the footprints overlap, the two normal distributions share a relationship, a dependency. Saying the rest of the normal, the unshared portions of the two normal are labeled Independence, but shifting from a frequentist point of view to a Bayesian one has to make us wonder just how independent these normal really are.

The means are separated by some distance measured in bits. I take it for granted that the scheme described in Nowak’s SuperCooperators is a given. He describes the evolutionary computing perspective on the normal as being histograms that are only one bit different from its neighbor. This implies a packing, and some other math. The stuff under the distribution is not random, so it pushes us in the Bayesian knowledge-based approach again. The location of the particular histogram reflects exactly who the customer is, what the company does to serve them, and various aspects of the UX and services provided. Having just sold more than 50% of your addressable market allocation, having just heard from the CFO about last months close, geeks are the yesterday, consumers are tomorrow, and you have a black swan on your hands. Oh, the projections were skyward, and the quarter doesn’t close for another two months. Ouch. Yeah, but it happens to every company. The PR spin might differ.

So lets separate the means of these distributions without changing their shape. Weird stuff happens.

21 Statistical Independence 02

So we opened the distance between the means of our normal distributions, We somehow came by more bits. But, the good news is we are totally independent, statistically independent. Geometry, metrics and rates matter here. Booms deform distributions. Booms do this by stacking the distinct populations that Moore talked about in his books on the technology adoption lifecycle.  See my Innochat framing post for more on the stacking of distinct populations.

Lets look at two situations that are a bit more complicated.

21 Statistical Independence 03

I actually tried to rescale this figure, but it’s a bitmap, so it became illegible at 1.5x. Geez. Better tools someday.

Each distribution has a subpopulation’s normal under it. In our earlier diagrams, the two normal distributions were red and blue. The normal distributions of the subpopulations are dark pink and brown. Both the red and blue distributions have been subjected to volatility, so their x’ axes moved up the y-axes. The volatility for the red distribution is shown in light pink. The volatility for the blue distribution is shown in light blue. The aqua below the x axis represents the future tails of the blue distribution involving the company’s reaction to their volatility.

The distributions are statistically independent now. The yellow area illustrates the distance between their tails. In the near future, however, the distributions will become statistically dependent.

Currently, the subpopulation that was under the red distribution is no longer being served by the company represented by the red distribution. The company may earn cash from distributor sales to the dark pink population. This is typical of American style global markets where we sell, but do not market to local populations. In the game tree sense, not getting feedback from those local populations is a failure to play the deeper game. Yes, it is a cheaper game, a more profitable game, but a short-term game. Volatility might remove the dark pink population from play.

Conversely, the subpopulation under the blue distribution is still be serviced by the company represented by the blue distribution. The population and the subpopulation have been discounted due to the volatility to the same degree.

Next, we’ll look at a top-down view of the same situation.

21 Statistical Independence 05

Here we can see the future tail, in aqua, of the blue distribution that will make the red and blue distributions dependent. The future tail is labeled tomorrow. The discounted portions of the distributions are labeled yesterday. The red and blue portions are labeled today. I could have drawn a straight line between the means. This appeared to be the case in the earlier diagrams, but here we can see how the earlier linear projection was a Bezier curve instead. The gray circle at the curve’s control point is the irreducible unknown. Notice that the linear projection compresses the bits measured on the curve to those shown separating the means. In communications systems learning compresses bits. Note also that the order of the Bezier curve could be higher. In that case, the curve would be even longer.

How we see a normal distribution depends on what we put under the distribution: noise, knowledge, or genomes.

Enjoy. Comments.

A Regression Tree

March 25, 2015

Ralph Winters (@RDub2) tweeted a link the blog post “On Some Alternatives to Regression Models” on the Freakonometrics blog. The author of post explains various alternatives to linear regression as it is applied to non-linear functions. Each of the alternatives give us an approximation, or put another way, a gap between the numbers and reality.

The regression tree model gives us an approximation that provides flat planes in various places. Deciding to be on one of those flat planes gives our business something to optimize towards for an interval of time, with a manageable level of cognitive costs. Where you see a square area in the macro sense, you’re dealing with a normal distribution. Where you see long thin rectangles, you’re dealing with a Poisson distribution. The ordinary course of business is from the Poisson towards the Normal, eventually to the normal, and the wider normal. The ordinary course of business is from discovery to enforcement in the repeatable operational sense right up to the black swan where the flat surfaces stop and fall. This all is also ordinary, but usually growth thinking and not knowing where were are under our distributions make for the black swan. No excuses for those swans, since I know the extent of my plane and I cannot change that extent.

I’ve heard of scale being the trigger for a chasm. It struck me as odd, but the flat planes of the regression trees end in two ways, rather than one. They go down or they go up. Both can be disasters to our ongoing operations. Down is the swan, but what of the up? Moore’s chasm is one of reference bases. The early adopter and their business case is made for risk takers in the vertical, but what of those that take less risk. The important thing here is that Moore found the pragmatism gradient. He found a way to climb up to the regression tree’s adjacent plane, up to the next plateau.

The regression tree also shows up the global maxima within the scope of the regression along with the local maximas where your business might be stuck. I was amazed years ago when a discussion with my CEO showed me that that CEO didn’t understand how his very successful business might be stuck on a local maxima. I wasn’t all that attentive to math back then either.

If you were to decide to serve a particular plane, that plane would be ground, aka the x-axis for any analysis going forward. Get agreement on this and make it stick.

In the figure, I’ve annotated the swans and the chasms. There is still plenty of business to be had on the plane. This plane is Poisson. This plane is your world. The other planes are out of scope until you commit to going there. There are 21 units of  business to be had. Nine face the swan. They must know where the line is. These nice share an operational focus. Eight of them face the chasm. They share another operational focus. One of units faces both the swan and the chasm. Five units have it easy. From this the executive should see the outlines of what the problems are going to be, who should be managing what, and what kind of training needs to be done. From this outline, we can design an organization that can cope, experiment, learn, and forget in its particular terrain.

For a  product manager, roadmaps and conversations should be organized around these planes. These planes also organize communications within and between planes, so marketing, offers, service provision, and pricing organize around these planes.

Enjoy. Comments?

Normal Distributions and A Game Tree

March 22, 2015

Back on March 5th Glen B. Alleman (@galleman) tweeted a link to his Slideshare “Managing in the Presence of Uncertainty,” I’m still working my way through it. But a few things got me tweeting. He makes a distinction that’s important, a distinction that provides some useful contexts that I’ll discuss here. Glen divides uncertainty into Aleatory and Epistemic uncertainty. Aleatory is the uncertainty of the classic frequentist approach to probabilities. Frequentists see noise under a distribution. Epistemic is addressable via the Bayesian approach to probabilities. Bayesians see knowledge under a distribution, knowledge that can be leveraged by establishing priors and looping through an exploration that improves those priors.

The Bayesian approach emerged after the frequentist approach was established. The Bayesian approach faced the usual adoption pressures as the frequentists leveraged their control of peer review and, hence journals. Name calling and such ensued. See, The Theory that Would Not Die, for the details of the struggle and eventual emergence of the Bayesian approach.

My own contact with the Bayesian approach happened back in 7th grade. We build a bead and matchbox-based game. Nobody mentioned machine intelligence or Bayesian statistics. The game was described in a column, computer recreations, or something like that, in the Scientific American. This was long before microprocessors. Nobody had access to a computer back then.

The game was played on a 3×3 board. On each side a row of pawns faced another row of pawns on the opposite side of the board. These pawns made normal pawn moves from the game of Chess, straight ahead one or one ahead diagonally to capture an opposing pawn. This took care of the generative side of the game. There were three ways to win: 1) occupy a square in your opponent’s pawn row, 2) capture all your opponent’s pawns, 3) Make the last possible move. This took care of the convergence side of the game.

It took a lot of matchboxes to build this game. Each matchbox displayed a board with the possible moves on it that could be made from the positions given on the board. The moves on one matchbox led to a collection of other matchboxes. The matchboxes were the nodes, the moves where the links.

Each matchbox contained beads that matched the color of one of the moves on the game board on that matchbox. A single matchbox might have three or more moves associated with it. A bead of each move color was placed in each matchbox. This gave each move even odds. These odds were the Bayesian priors.

As the game was played, a record was kept as to the path taken during the game. If the machine lost, you removed the  beads that led to the loss. If the machine won, you put two beads of the winning color back into each matchbox. In both cases what you did was update the priors based on what you learned during the last game. It was classic Bayesian. It was classic Stewart Brand’s How Buildings Learn. They learn through accretion. They learn, but they keep secrets.

So lets explore how a game tree organizes its normal distributions.

40 Game Tree Two Tiers

I let the game get just beyond the second move. We’ve played to this point several times, so we have a histogram of the possible moves. N isn’t high enough to give us a continuous rendition of a normal distribution,  but the discrete hints are there. The game tree looks like a binomial tree with equally weighted branches, so the normal is not skewed. Then, we play two more moves deeper into the game tree.

42 Game Tree Three Tiers

Here I’ve depicted the normals for the second and forth moves. We could change the representation by putting the second tier normal under the forth tier normal. This would reflect a frequentists approach depicting the smaller normal as a subset. It looks smaller, but remember that both have an area of one. The deeper we move into the game, the wider the normal would get. To keep the area at one, the normal would also lose height: 6×1, 2×3. I’ve not depicted this, so just imagine it. It happens all the time out in the business world. The F2000 company has thin margins. At F4000, thinner still. Yes, even for F4000 companies, the area under the normal remains one, although the base is wide.

43 Two Normals Overlaid

This figure is just fine. It depicts a proper subset being a normal with the same mean as the containing set. But as a depiction of the game tree, it’s just wrong. Game play flows through the mean at the top of the normal and flows to the base. Further, future game play expands the base and height of the normal. To get to the base, you have to make those first two or four moves, increasing their frequency, after which you expand the base outward and another tier deeper. But, the future is not known yet.

44 01 Two Normals Overlaid

Now, we’ve shown how the two normals fit together. The normal for the subtree converges sooner than the entire tree. The differences between the tails of the normals is a function of the depth of the subtree and tree. Notice that the two normals are not fractals of each other. We are seeing the normal at two different times in its life. The change in tree depth is also a change in bit depth. The set gets the x-axis. The subset gets the x’-axis.

44 02 Two Normals Overlaid

Now, we show that the early normal grows towards the top of the later normal, and the later normal grows down and out. Again, to make a later move, you must make an earlier move. Those probabilities change together. In the pawn game, described earlier, wins terminate a branch of the game tree. This stops the accumulation of frequency and moves the histogram outward towards the outliers.

46 Black Swan

Next we consider the black swan. For product managers, commoditization is a black swan that happens often enough. When some portion of your product becomes commoditized, you lose bits, and you lose addressable market population. Tomorrow’s future is smaller than yesterday’s. As for the normal it converges sooner on another x’-axis. Of course, you knew that commoditization was coming, and given today’s preference for trade secrets over patents, you’ve built under the base of yesterday’s normal.  You were ready. I know. We’ll pretend politely.

47 Black Swan Recovery

You’ve added some bits via an effort represented by the red triangle, the red decision tree, which like playing a game deeper pushes the base x-axis down, which in turn moves  your convergence with the new x-axis into the future.

So I’ve moved your convergence into the future. Congrats. Comments?

Just a few Quick Notes

March 20, 2015

A tweet lead to this NYTimes article, “How the Recession Reshaped the Economy in 255 Charts” by Updated: JUNE 6, 2014

Notice the data visualization on the first page of the article. Those graphs showing up the upward trends tend to be bubbles. What you don’t see is how the category for each of those bubbles consumes the future. What you don’t see is the shadow cast by those upward efforts. Those shadows tell us how long it will be before we can sell again. The heights tell us of the more immediate or historic falls, the black swans, the fragility, the lost valuations, and the lost bits of the larger world suddenly smaller.

Another tweet led to another NYT article more conventional, “A 3-D View of a Chart That Predicts The Economic Future: The Yield Curve by and MARCH 18, 2015. Imagine your roadmap on this curve.

The figure below is not one of mine, but I thought it was neat enough to buy the book it was in, “How To Be Interesting,” by Jessica Hagy. I picked it up at an airport bookstore a while back. I don’t usually read self-help books, but this one was an exception. As for the figure, it reminded me to see the numerous contexts beyond the reach of our offer into the user experience.

Features w Use Cases

How far do you want to reach into the user experience? As for myself, I’ll reach into the underlying cognition via ethnography. I’ll insist on attention being focused on carrier rather than carried, on the real world that existed before programmers showed up, rather than carrier focused toys. Toys are fine. They’re just not me.

Anyway, enjoy. Comments please.

Foster Ecologies

March 4, 2015

So here was this tweet today


If you want to go fast, go alone. To go far, go together – African proverb

Out on Twitter over the last week, I dropped a tweet or two about product ecologies after watching a YouTube on the human biome. Actually, there were several human biomes that we migrate across and live within or under over out lifetime. Products do a similar thing.

Your whole product vendors have third-party developer programs that operate for the vendor’s benefit. These programs are multisided markets, so the third-party developers get something as well. Multisided markets can serve the carrier vendors with their API expanding the original vendor’s functionality, the carried vendors with their models, and another carried vendors data–layers, many layers all on the same roadmap, all moving at different speeds, all moving in different directions–going together. Everyone in this tech ecology gets functionality at some level, but they also get prospects. They get channel. Channel is hard.

Channel is hard because channel participants are there for their own reasons. They have their own motivations. They have to be led just like your matrix team members. They serve and monetize around a population. You’re participation in their channel might demonstrate objectivity to the population they serve. Sales in that case might be accidental.

So we have lots of interests to serve beyond prospects, economic buyers, and users, beyond our monetizations, and beyond the interests of our internal interest groups. Oh, the messiness of going together.

Alone, we will not create a category. I know. Most of us are not trying to create a category. Most of us are not trying to build a business around a discontinuous innovation. We have far to go, so we have to go together. If you tried to go it alone, you’d be facing antitrust issues. Competitors help overcome these issues. No negation tends to go a long way to keeping competition respectful. You can’t have more than 74% of the market, and you would be lucky to get that 74% from a market power allocation. These days it’s market share as an outcome of ones promo spend and VC funding, which doesn’t get anywhere near 74% and isn’t lasting like that 74%. Early and late tech adoption businesses and economics are vastly different.

Going together reminded me of when my employer got a supercomputer gratis. That hardware vendor wanted us to support their machines. Our application generated code. Compiles took a while, so now we could support a new value proposition, compiling our customer’s code for them using that supercomputer. Long ago, I know.

In a later company, the computations moved, so we could move them to machines that were faster, or slower. You could play with an algorithmic ecology. You could run several heuristics to get there ahead of the more accurate algorithms with the algorithm with the best accuracy coming in last, at last. The sort of thing we do with Monte Carlo simulations running in our edges of the unknown later substituting knowns as we capture them.  Running fast, but going far together.

Fostering ecologies gets us down the road together. Foster an ecology today.


Limit Cycles

February 21, 2015

I’m reading Colin Adams’ Zombie & Calculus. Zombies are not an interest of mine. There are no Zombie Rescue stickers on my car. I’ve never seen a Zombie movie and won’t. All right, I’ve escaped the Zombies. My differential game.

In the book the protagonists, those who have not yet been caught by the Zombies, and the antagonists, the Zombies engage in differential games of capture and pursuit. A differential game is a game played between two or more players where each player has their own differential equation. A differential equation expresses a rate like iterations per year. Some design considerations might involve rates like how fast will we drive away our geeks fast will we attract consumers. Or, bringing it back to capture and pursuit, what can we do about that fast follower?

In the book, the Zombies, playing these games, eventually caught almost everyone. Well, eventually caught up with almost everyone is more like it. Almost? Well, the author lived to tell the tail. Meanwhile back in the games, some of the protagonists were able to see each individual’s attempt to stay away from the Zombies. They saw every experiment. In one of the experiments, the protagonist took a circular path. The Zombies kept their eyes on the runner. The Zombies ran in a circle, so they could keep their eyes on the runner. The Zombies kept moving, but could never get closer to the runner. The runner was faster; the Zombies, slower. The ratio of their speeds determined how far the runner’s circle was from the Zombies’ circle. The runner circle is the limit cycle. Cycles like in trigonometry class where we spent time going round and round. Not cycles like those in The Lincoln Lawyer. Nobody was spiraling. Nope, just circles. So, lets look at a limit cycle.

Limit Cycle 01

The faster person, the red dot, runs on the black circle, the outer circle, the limit cycle. The slower person, the blue dot, runs on the gray circle, the inner circle. The blue arrow illustrates the speed of the slower person. The blue arrow is a vector. Enough of that. The slower person keeps the faster person in sight along the thin blue line, the tangent line. Stop that. Oh, another differential game. Colin Adams, the author didn’t throw down a theorem to define the limit cycle. No, he evaded, the proof-based mathematics, he had his own differential game going. He was pursuing an audience that wouldn’t stand for that.

In the next figure we’ll illustrate what happens when the slower person speeds up or slows down.

Limit Cycle 02

The faster person, the red dot is still on the black circle. At the top, the second person has sped up, which made their circle bigger and closer to the limit cycle. In the middle, the second person has slowed down, which made their circle smaller and more distant from the limit cycle. The blue circle is just there to remind us that there are larger scopes even for the larger player. Ultimately, a limit cycle comes with a category. The market leader as determined by market power sells more, has more customers, more money, and more people than anyone else. If you compete with this company you won’t exceed their limit cycle until they bail out of the category. That’s something they wouldn’t do if they saw the point in staying behind.

And, yes, nobody competes for market power these days. Here in the late main street/consumer phase of the technology adoption lifecycle, its all about promo spend and the myth that you can beat the limit cycle. Las Vegas will take that bet.

But, just for grins, lets say your pursuer finally managed to match your speed, what happens next?

Limit Cycle 03

Once you’re pursuer is as fast as you are, they won’t be inside the circle looking at you. They will realize that they still can’t catch you. They’ll stumble on the Wayne Gretzky quote about being where the puck is going, instead of where it was just now. Well, your staff saw the day when a chunk of their offer that that this pursuer competes on would commoditize, and they made sure that they had some discontinuous technological innovation that everyone could ride for the next twenty years. They put a real option on the circumference of the current limit cycle, an unweighted control point ready to bare some weight.

Limit Cycle 04

So your pursuer was surprised–“Oops,” but they keep trying to catch you. Unfortunately a miracle happens.

Limit Cycle 05

They end up inside your new limit cycle. Your pursuer can’t be faster than you, so being slower, the parallelogram flips rotating them inside your new limit cycle. An odd thing happens though. The center of their cycle is not concurrent, is not the same, as the center of your cycle. The more knowledge it took to create your new offering, the more learning they will have to do. They will be elliptical, not shown, until their process maturity has them following you again without additional learning. Mean while, they dream of the day they own the limit cycle, aka they day you cash out of the category. The elliptical brings with it some messier math.

So as product managers, we compete about and in many limit cycles.

Limit Cycle 07

Each of our features are either about a limit cycle, like the red dot on black circles, or in a limit cycle, like the red dot on the thin blue circle. Each red line in the factor analysis on the right corresponds to the radius of a limit cycle on the left, except inside the blue circle. The red dot is you. Inside the blue circle the factor line is attached to your red dot. All the limit circles are inside a hyperbolic surface. We could just say F2 is a belief function under distribution. Or, we could learn to draw the hyperbolic surface first and fit the circles inside it later.

Realize that you are a fast follower of your whole product components’ providers. You are using someone’s APIs, you follow them, you chase them. It doesn’t feel like pursuit and capture, because they keep their third-party developers informed. Or, do they? Value chains involve limit cycles as well. And, after doing the math, it might be quicker just to buy their company and integrate their functionality–the pursed captures their pursuer, not a good thing when Twitter or Microsoft did it.

As for the Zombies, the last page still eludes me.


Spatio-Temporal Maps

February 16, 2015

Saturday, I looked some of the pages linked to a visualization site I came across over the past week or so. A visualization of the trains out of London and how the trains changed travel times.

Spatio-Temporal Mapping

Spatio-temporal maps have been a topic of mine for decades. Each of the circles represents a half an hour. Here things are fairly straightforward. But, the realities on the ground are quite different. Yes, the train takes you there quickly. Yet, step off the train, and suddenly it still takes five minutes to cross the street in front of the train station. Things slow down. The 3.5 hours it took to get to Neiuweschans did not deform the map so the physical and temporal, the spatio-temporality, stayed aligned. If you map by time, distance gets deformed. Well, not in this viz. But, try flying to China and then taking the train to your home, several days away via that train. That map would get bent up quite a bit.

Since San Antonio has been my hometown if I ever had one, and having worked in Austin at too very different times, I travelled back and forth a lot. At times I took the Greyhound. Once, on Thanksgivings Day, I blew a hose, popped the hood, opened my trunk, someone pulled over, took a look, dug around in the camper topped-pickup truck found a hose, put it on, filled my car with water, and sent me on my way gratis. Dad didn’t get to tell me off. Thanks. And, I didn’t have to walk 45 minutes to get to a phone in what was at that time ag wilderness. Now it’s convenance stores, burbs, same distance, different mindset. Same spatial, different temporal. Well,  tore up the concrete since then and changed the physical as well. Roads get wider, flatter, and straighter over time.

So here would be something to map and look at the math behind intrinsic curvature. Well, try to if you will. So I pulled the northbound distance and time data for the cities between San Antonio and Austin. I threw away the first attempt. I modified the second. I waffled on how to represent the gaps and lags. Then, I realized that the physical, the miles were continuous, as were the times. I used horizontal bars for each quantity. To get both bars ends to line up meant bending the bars, aka introducing curvature. Except for one thing, I’m working in MS Paint. I know all the tricks. Actually, I learned a few Saturday.


There were four segments. Two of the towns were obvious. The third was just pulled out of necessity. The Greyhound used to pull into Kyle, so I know it’s there, along with DQ and a Wachenhut staffed county jail on the frontage road.

I did not pull the southbound data.

So here’s my spatio-temporal map.

Spatio-Temporal Map 01

The graphic at the bottom uses spheres to represent each maximum quantity. The minimum quantity is a smaller circle on the maximum quantities sphere. I put the two larger spheres out front and the smaller ones behind them, so both the spatial quantities and the temporal quantities could maintain their continuity. The line charting had gaps. That just isn’t real. The world is seemless, so the spheres let me present that continuity.

The leftmost sphere represents the minutes traveled with the larger circle. The smaller circle represents the miles travelled. The leftmost sphere is blue because travel time trumps distance travelled. That making it a slow segment of the trip. The travel represented by the second sphere is black, because the miles travelled trumps the time travelled. Again, this sphere has a smaller circle on it representing the shorter quantity, time. The two spheres contact each other miles to miles. That would be the smaller circle on the first, and the outer circumference of the sphere on the second. The third sphere is like the second, but they contact each other times to times, aka the small circle on the second sphere to the outer circumference of the third. The fourth sphere contacts the third, miles to miles.

Too complicated, I know. With a better 3D package, it could be clearer.

Each sphere’s rotational axis is shown. The system of spheres are organized along a curve, rather than a plane.

I went on to draw a curvature graph based on the same data, but the formulas didn’t give the correct results. The arc length of the arcs are too long. The red line edits a spike out of the graph, because it too isn’t real. Again, the road is seamless.

Spatio-Temporal Map 02

I continued my quest for a curvature view with this, the last one.

Spatio-Temporal Map 03

No explanation for this one. I did manage to get a nice arc, but instead of covering the first three points, it went all the way to the fifth point, to Austin, fitting well, but missing the point.

In these diagrams, I used hard data. Well, hard for the moment. I did go back to get the southbound data and found it at odds with the northbound data. Construction can account for why the trip south took more time. Traffic could do same. I did get to that data later in the day. Escaping the big cities eats up the time on this trip. Still, we can consider the hard data. But, there is a soft component. Back during the dot-com one days, the trip was lit up all night. The convenience stores/gas stations stayed open all night. This shortened the psychological distance. These days the speed limits have been reduced for revenue generation purposes–new speed traps. But, I still remember making the trip from south Austin to north San Antonio in 45 minutes. Forget that. But, yes, the speed limits affect the psychological distance as well.

So tying this to product management? Two things:

  • The original train visualization showed a process over geography. Sprints are such a process. And, I’ve talked about product manager geography previously in this blog. For me a roadmap is a map, not a list. Populations are like lakes. Come up with your own analogies. Make your map a real map.
  • Consider my maps to be maps of a user experience. When I reach New Braunfels, please don’t make me open a Wal-Mart popup window. I don’t have time to stop. Your features are organized like trips. Different outcomes, different trips. Done again and again, it becomes familiar and routine. The user knows their way until Agile changes something and DevOps thought nothing of injecting a bug into the user’s UX. Click here, then click there is a geography, a series of histograms/long tails, and flow or psychological time.

Enjoy. Comments.


February 7, 2015

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.



January 25, 2015

One day, our founder decided to go to attend a SBA training program. They taught him about a business plan, and recommended that he answer the RPs requested in DARPA and DoD SBIRs. So I got t read a lot of SBIRs. And, I got to waste time writing proposals. Reading SBIRs is one thing. Replying to them another.

Our technology was about decision making in loosely coupled networks. This was back in the day before Microsoft hijacked the term loosely coupled network. DARPA had inferential warfare in mind. They took the OODA Loop and made it formal. Then they looked at the individual commanders doing their OODA loops from the perspective of the commanders of those commanders, and the providers of external services for things like artillery, and air strikes. Lags were involved as were conflicts of interest, so some other researchers came up with solutions built around shifting points of view. Nice stuff. Stuff you’ll see executives asking for in about a decade as the officers trained on the stuff move into the private sector.

An army SBIR was asking for tools that would give them the bigger picture of large-scale coordinated hacks. This involved masses of streamed data from server logs all over the network. In discussions with the lead for this particular project, the lead insisted that what he was asking for could not be done. Well, the company I worked for before I got involved with this founder could do it, but probably never thought to sell it to the federal government sector, because of the overhead involved. They got acquired so the VCs could leave, and the technology vanished. The acquirer had competing technology that couldn’t do the job. These SBIRs are research projects, phased research projects. Get into Phase I, or forget about it.

In another discussion, we were looking at a later phase of a project, this before we learned not to do this, and found that SBIRs are awarded to academic researchers, not technology startups looking for clients. So it boils down to SBIRs not being the way to go. After an SBIR project, those academics can form companies that sell the SBIR technology into the private sector, but the private sector won’t startup within one of these projects.

Subscribe to SBIR lists, read SBIRs, get a grasp of future technologies, do your research thing, know how your market populations will change as the technologies in those SBIRs are adopted. But, don’t write proposals in response to SBIRs.

There are companies that respond to SBIRs, but they were set up to comply with federal service acquisition laws. A bare bones software startup does not exist in that world.


The Bowling Alley

January 24, 2015

I’ve owed this blog post to one of my twitter followers for a long time, so finally.

The bowling alley is a place on Geoffrey Moore’s Technology Adoption Lifecycle (TALC). I remember it as the first or second of his books on the topic that I read. The other one was “Inside the Tornado.” The bowling alley book is apparently out of print. I may be totally hallucinating, but I think I had it physically in my hands. I read those two before I ever read “Crossing the Chasm.” I read a lot of the same stuff over and over again from one book to the next, but Moore follows Tom Peter’s practice of giving away your methodology while constructing a new one. After the dot bust, Moore moved into the more orthodox regions of standard management practice–stuff startups can’t do, or at least startups that are fostering the adoption of a discontinuous innovation–something the internet is not today. The internet is a well adopted technology sitting in the late main street and later phases of the TALC. That the internet still fits into the TALC along with smart phones and the cloud tells us that the TALC is still relevant. Even if nobody pays attention to it these days.

One thing we hear is how risky innovation is, particularly discontinuous innovation, the kind of innovation that creates new wealth, new categories, and new companies. The bowling ally is a risk reduction mechanism. Risk be damned. Yes, if you do the orthodox pro formas and business plans, you’re boat gets sunk. The risk manifests itself. Management practice inserted that risk. Thinking economies of scale inserted that risk. Stage gating on a guess, or gut inserted that risk. Well, stop it. We can mitigate our risks via the bowling alley.

Moore’s bowling alley starts with the early adopter. That early adopter runs a business in a particular vertical. That early adopter runs the whole business, so you can span the enterprise, or focus on a few business or functional units. You don’t want to do anything horizontal for the early adopters, since you will be selling his application into his vertical. The early adopter will pay you to build his product visualization using your technology. You won’t need VC money yet. You could potentially bootstrap.

Given you know the vertical you will eventually enter, stage gate the early adopter engagement on that vertical. Is there enough seats and dollars in the vertical? Are you providing real competitive advantage to the eventual economic buyers in the vertical? Is there a real market, or is the use of the application driven by laws requiring use of the application, or reporting? Be careful here. Is there enough IT horizontal seats in the vertical? You’ll need those IT people, because the very last thing you do in the bowling ally is enter the tornado where you are selling to the IT horizontal. You get them on board during the vertical. Now, is too early.

Moore suggested engaging the technical enthusiasts in the vertical you are entering, as they might surface an early adopter willing to use your technology. In the figure below, I’ve color coded the bowling ally. I omitted the relevant technical enthusiasts. I’m using a overhead looking down view of the TALC. This will enable us to see adoption and sales of our products as vectors, and in the bowling alley in particular, a collection of Poisson games, games or unknown populations. Moore uses a normal, but after seeing a visualization of startup financial data, I’ve seen it as a Poisson distribution tending to a normal. This was irrespective of the bowling alley.








You don’t see companies going through the bowling alley. I interviewed at one, the one that created the idea of demand pricing. The airline industry was their first client, their first lane in their bowling alley. They had other clients as well. Yes, companies do this. They do it to productize and drive the adoption of their discontinuous technology. You wouldn’t do this for continuous innovation/technology. You do see companies getting stuck in a highly profitable vertical. PeopleSoft is one example.

Doing the bowling alley is a slow process. You want to do eight custom development engagements for early adopters. To keep your intellectual property, you’ll need to give the early adopter something. Moore talked about a two year period of exclusion. That’s two years after delivery where you don’t sell the application. The early adopter wanted a competitive advantage, and gets it during those first two years. All is not lost, since you can have a very broad scope, deliver functionality inductively, deliver functionality as minimal marketable functionality, and engage in management consulting to help the client maximize their competitive advantage.  You’ll have plenty of time with the earlier lanes of your bowling alley. Later, you won’t.

In the next figure, I’ve illustrated the bibliographic maturity process as feeding the technical enthusiasts, some of whom may advocate attractive technologies to their business unit executives.  Again, not to IT. IT will not buy never adopted before technologies. When productized, business unit executives do buy applications that do what they need even if the technology is discontinuous. Of course, these executives are the least risk adverse executives around.

Inbound Idea

Notice that the outer edge of the technical enthusiast phase is gray to indicate that this edge is porous. The outer most red segment represents the technical enthusiasts that can advocate. The inner segment that is not red is the tornado and IT horizontal. The bibliographic maturity process is shown to the left. Since the 60s, we have allocated basic research to universities. Basic research is the kind that breaches physical constraints that are at the core of discontinuous innovation. Ideas move via tennis shoes and show up at the perimeter of a firm where they are subsequently brought in house and commercialized.

When stage gating, you should also consider the spread of your early adopter engagements across the industrial classification scheme that economists use. Moore suggested eight early adopter engagements each in a different industry. Scattering these engagements across the classification scheme enables you to move up and down the vertical with minimal effort, and it enables you to sense economic events via the normal operation of these eight businesses. Different industries propagate economic events at different rates. While one of your lanes closer to the event slows down, all of your lanes will not slow down at the same time.

Industrial Classification Scheme

The figure shows both the movement and sensor net effects of having a collection of well placed lanes.

Eight engagements are taken on serially, one after another. Doing more than one at a time requires us to grow our headcount and capabilities. In the next figure, I start out with one engagement or lane in the first year, doubling the number of engagements in the second year, and doubling the engagements again in the third year. Since my aim is to continuously do discontinuous innovation, I transition to another discontinuous technology once my eight engagements for a given technology are underway. The figure shows subsequent technologies. The figure reflects a very clean process where reality is messy.

Eight Lanes

Now, imagine wrapping the above figure around the radius of the tornado and IT horizontal. Each of the products would be represented by a vector extending from the technical enthusiasts  and ending at the tornado. We ended up with the following figure.

Eight Lanes on TALC

In this figure the products are scattered around the industrial classification scheme. Thick red radial lines represent ongoing product development. The brown areas show how the radials get shorter and shorter as we approach the last engagement or lane in our bowling ally for a given technology. The last engagement will not be waiting around in its vertical. This decreasing engagement window happens because we will combine all our products into a single application that brings all the users with it as it enters into the IT horizontal. The blue areas illustrate where we begin to focus on the IT horizontal. The red radials represent our vectors and the Poisson games associated with them.

It’s messy. There is a lot of work involved in getting across the bowling alley quickly. The goals change. Ultimately, the bowling alley sets you up to win your tornado competition and win your market leader designation, and market power market allocation that grants you a near monopoly for years to come.

Don’t listen to the siren’s call of risk, risk, risk. Mitigate those risks by taking the time required, and putting in the effort to cross the bowling alley.




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