Flattening the Curve
Computerworld ran an interesting piece last week about the slowdown in the phenomenon known as Moore’s Law.
Moore’s Law was never a law in the scientific sense. Gravity is a law. What Gordon Moore postulated in 1965 was an exponential growth curve that said the number of transistors that engineers could put on a chip would double and the cost of computing would halve about every 18 months for the foreseeable future. It was a no brainer almost a tautology, really. It lasted only 50 years and just when things started to get interesting, bang!
The trouble with growth is that some factor always intrudes to stop it. Food limits population size unless you can keep expanding the supply for example. In any system with a finite resource base infinite expansion is impossible to contemplate. So it is with computer chips. There is only so much real estate on a chip.
According to the article chip designers are working at the nanometer level with only a few atoms between transistors now so it is hard to see how the next doubling is going to happen. More expensive processes and materials can put off the inevitable but by then you’ve been painted into a corner.
This does not mean that computers can’t get any smarter or that we are at some limit to our software creativity. Bigger chips, better strategies for chopping up hard compute intensive jobs can still make a difference. But what will this mean for the size of, say, your handheld device? Think of it as a carton full of eggs, where are you going to put the next one? You can make a bigger box but how big can you make a chip?
You might call this a limit on growth and this has been much on my mind since I read the 30-year update on the original publication — “Limits To Growth” — from The Club of Rome recently. That cheerful ditty applied computer modeling to a host of data and concluded that at 8 or 9 billion people on the planet, with increasing pollution, decreasing energy resources and raw materials, and decreasing food production, things get a little dicey.
Don’t look now but with increasing population, global warming, droughts or heavy rains affecting farmlands, depleted fishing grounds, expensive energy, and declining raw material bases, we seem to be on schedule. You can’t expect infinite growth with finite resources. First Moore’s Law, then what?
Economics of sales forecasting
I have been studying sales forecasting and forecasting tools a lot recently and I have come to the conclusion that we need better tools as well as better ways of using them.
There is a lot that can be said about forecasting, its current state and how to improve it and I don’t want to leave anything out but I will try to be brief. First off, how we forecast says a lot about our views on economics. Given that most of us are not economists, our views of economy are most likely derived from what we see and hear on a daily basis, much like our view of the weather.
For over thirty years our view of economics has been increasingly colored by the ascendant views of the New- or Neo-Classical school of economics. To over simplify, it is a view that goes back to Adam Smith, of supply and demand and a belief that economics is a hard science governed by equations as rigorous as Newtonian physics — wishful thinking I’m afraid.
The most germane idea for our purposes is Say’s Law. Say was a French economist, very much in the Classical school who said that “production creates its own demand” and from that we derive the famous supply side economics of the last thirty years. Supply side economics corresponded nicely with another phenomenon in our world, the introduction of the CPU chip in 1968 and the cascade of new products that ensued over the coming forty years, roughly the high-tech era.
Increasing CPU power followed Gordon Moore’s famous dictum, now Moore’s Law, of increasing CPU power and decreasing cost, and it created a special circumstance that governed supply and demand for technology goods. Moore’s Law made Say’s Law work like a charm. A corollary to Say is that all markets clear, i.e. all supply is eventually absorbed at some price — but maybe not a premium price.
Moore’s Law ensured that a fresh supply of technology goods that superseded the earlier generation would arrive and drive demand thus ensuring Say’s Law would operate as advertised. But if Say’s Law requires something like Moore’s Law to operate smoothly, then it must be said that Say’s Law is a special case, not an iron clad law of economics.
What’s that got to do with sales forecasting? Quite a bit. In the special case of selling into a market with undiminished demand, sales forecasting need not be a lot more complicated than determining where we are in the sales cycle. If we’re ninety percent through the cycle we ask for the order and there is a reasonable chance that we will get the business — no guarantee, but a reasonable chance.
It hardly matters that our ninety percent is not really a probability derived statistically but really just a milestone in a process. In an expanding market there are enough deals percolating that reasonably diligent effort will result in on-quota performance. But on-quota performance is not what it once was and forecasting is in disrepute in many places.
According to Jim Dickey and Barry Trailer at CSO Insights, only about fifty-eight percent of sales people manage to make or exceed quota. Also, according to my research less than ten percent of sales forecasts have an accuracy of ninety percent; the rest aren’t worth the time and effort it takes to compile them.
What’s happening to sales forecasting is not surprising. With Moore’s Law slowing down and with so many formerly new market niches filled with products, we are transitioning from an era of expanding markets to one of zero-sum conditions. In a zero-sum situation, if you are going to win business you need to do it by displacing another product. If you are a customer in a displacement game it is always easy to do nothing and wait for a better offer and continue using an existing product that might not have all the bells and whistles you want but fills the need nevertheless.
A zero-sum economic environment has a lot of uncertainty in it. You might use the words uncertainty and risk interchangeably but they are not the same. Risk is something that is unknown but knowable. If a deal forecast is at risk a sales representative — frequently at the urging of the sales manager — can ask more questions, get more data, and piece together an answer. There are many issues in sales that are simply unknowable or mostly unknowable, for example, the details of the bid your competition makes.
When uncertainty — not just risk — enters the picture, our forecasting paradigm that relies on milestones in the sales process becomes useless. We need better tools if we are to forecast in the face of uncertainty and those tools exist but few of us have taken them up yet. For example, prudent managers might start with the territory planning process. How much white space is in the territory? What percentage of that white space is likely to churn this year? What is the overall economic forecast? Given our market share what is the probable share of that white space that we can capture? Is that enough to sustain quota for one or more people? How should we incentivize them?
Sales forecasting will always be an inexact science but we can do better than we are currently. We could persist in basing our forecasting ideas on Say’s Law but inevitably it is a race to the bottom, to pure competition on price. The airlines do that but none of them makes any money.
