What the Bell Shaped (Normal) Curve Can Teach Executive Leadership

Let me preface this Blog by saying that this is not a treatise on statistics, but rather some insight by a non statistician on how and why to apply a well known statistical tool to help you run your company. So a brief writing on application and execution as opposed to theory.

Back in the ‘94/’95 era, I obtained an independent manufacturers rep engagement with a technology company. I was asked to find out why the company’s products were not gaining traction in the market and to see if I could find out the reasons why as I called on prospective customers. Since I wasn’t a previous sales participant in this market segment, I started by looking for information (remember, this was light years before Google and the Internet as we know it today) I could use that would help me increase market penetration.

A good friend and business associate of mine gave me a book (Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers, by Geoffrey A. Moore – today every legitimate technology salesperson is well versed in the tools in this book and its sequels) to read he thought might be of some help, as it dealt with the dynamics of the market I was selling to. In his book, Moore portrays the technology adoption cycle (which also determines length of the sales cycle) as a bell shaped curve, with roughly 67% of prospects falling within one standard deviation of the mean (average) and 95% of prospects falling within two standard deviations of the mean. As I began to dig into the book, I realized two things, one of which is still contrary to the way too many companies sell to this day (“Here’s your prospect list, call 50 companies on it each day. Remember, sales is just a numbers game”.) First was that some companies & people are likely never to buy (laggards), and some will buy later than others (late majority vs. early majority vs. early adopters). Second was that there are ways to determine which technology adoption categories prospects fall into, if one is willing to do the necessary research.

So I decided to do the research from which I could build prospect profiles. As soon as I could determine that a prospect fell into either the late majority or laggard category, I would put those in my “much later” file and continue my research until I could identify prospects that would fall into the early majority (at worst) or early adopter (best case) categories and pursue those prospects. Application of Moore’s work gave me a wonderful opportunity to help grow a virgin technology market for a company that 16 years later is now the dominant player in that market.

Several years after that assignment, I began to apply the normal curve to other areas of business, concluding that if the numbers in the universe being observed are numerous enough, the universe in question will present itself as a normal distribution or a bell shaped curve. So what does that mean to those of us who run businesses? If you are evaluating performance or behavior (as opposed to measuring height, for which there is no “good’ or “bad” – except maybe in basketball), the closer to the far left of the mean that performance or behavior is, the worse it is. If we, as leaders, recognize that we then have an opportunity to manage against those negative performances or behaviors by either eliminating them or fixing them. Think what will happen to morale and profits if occurrences at the left side of the curve are lessened or eliminated!!

I hear company owners and CEOs say they wouldn’t trade their employees for any other company’s employee group. Good thinking, but for the wrong reason, because if they would, they would still have roughly 5% of a new group of employees that would be creating a drag on the other 95%, just as is the case with the company’s existing group of employees!

Irrespective of the size of the company after you get to about 10 or more of the following categories, good leadership will require the study of processes, employees, vendors and customers and anything else that can be measured in order to determine who or what falls more than two standard deviations to the left of the mean and will work to improve that variable’s performance, or, failing improvement, eliminate it.


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