This makes sense to me.  Nonetheless I am often astonished by the frequent tendency among many corporate decision-makers to conflate the application of some management tool with a fabulous consultant-ese moniker into a “magic bullet” that will effortlessly change the organization overnight from a laggard to a market driving leader.  Then, as egregiously as they confer magic powers on the tools, after a few fiscal quarters the decision-makers realize they are not getting sustainable performance improvement, decide in their infinite wisdom that the inherent inadequacy of the tools is at fault, and consign them to the trash heap of unrealized expectations.

meaningful tools or random noise?

This misguided tendency – to ascribe awesome powers to something and then discard it for the wrong reasons – brings to mind one of my favorite management lessons: a timeless exercise developed by W. Edwards Deming called the Red Beads Experiment (actually, what I call “timeless” Deming himself calls “a stupid experiment you will never forget”).  Deming was one of the founding fathers of Statistical Process Control, itself a prototype of the management tools that abound in our age, and something of an iconic hero for several generations of Japanese business leaders dating back to the 1950s.  The phrase “you can’t improve what you can’t measure” is often attributed to Deming, though not always in the right context.  A more accurate reflection of his philosophy would perhaps be “measuring the wrong thing is much worse than not measuring at all”, and that brings us back to the Red Bead Experiment and its lessons for managers of today in the use and misuse of performance management tools.

The Red Bead Experiment is quite simple. It starts with the simulation of a factory tasked with the sole objective of making white beads.  The factory’s customers will only accept white beads; beads of any other color are rejected as unacceptable.  In the simulated experiment we represent the operations of the factory with a sampling device that contains a total population of 80% white beads and 20% red beads.  The red beads in turn represent defects caused by one or more organizational or operational flaws (such as poor design, faulty machinery, improper order communication, inadequate resource allocation, shoddy quality control and similar shortcomings).

In the first step of the experiment a manager selects an operational team consisting of six workers, two quality inspectors and a chief inspector.  This team simulates the factory’s “production process” as follows: every day, each worker draws an independent sample of 50 beads from the sampling device.  When a sample is drawn each inspector will separately record the number of red beads in the draw and report that number to the chief inspector, who will record the results.  This initial simulation can go on for several days, i.e. by the end of, say, four days each of the six workers will have drawn four independent samples of 50 beads and the number of red beads (i.e. “defects”) will be recorded for each draw and the results will be averaged to produce a consolidated “performance result” for each worker over this period.

At this point the experiment calls for the manager to employ a combination of suggestions, processes, incentives, threats and so forth (which we can think of as “management tools”) to extract better performance from the workers.  For example the manager may tell one worker whose “defect score” was higher than average to use a different technique when using the sampling paddle to extract the 50 beads (“flip your wrist a bit to the right – yes, like that!”), while telling another whose draw of red beads was lower than that of the group as a whole to “keep up the good work, expand your knowledge of white beads and there will be a year-end bonus in store for you”.  The experiment will repeat over several further iterations, each recording different performance results and with the manager constantly discarding and implementing performance tools in response to the results achieved.

The point of all these performance improvement devices, of course, is that they are pointless: the “system” from which the samples are drawn contains 80% white beads and 20% red beads. Actual results will simply reflect random, independent deviations from this 80/20 distribution and over successive iterations the average of all the draws will converge towards that 80/20 split.  The real underlying message is that measuring the effect of any given performance tool (whether it be based on incentive, threat, knowledge or process improvement) is useless without a grounded understanding and (where possible) measurement of the system itself.  In the language of Deming’s experiment, if you want to optimize the system for minimal red bead production then figure out how to change the 80/20 stasis at the system’s heart – then use appropriate management performance tools to align the activities of all the organizational resources in a self-reinforcing manner to achieve this desired strategic outcome.

Management tools have proliferated in the years since Deming’s heyday, and many of them offer the potential for real performance improvement. For example, organizations have the ability to surgically manipulate the operational levers at their disposal through performance approaches such as Supply Chain Management on the cost side and Price Optimization on the revenue side.  However, translating the benefits from such approaches into sustainable competitive advantage requires something more than the mere implementation of these (or other) tools: a granular understanding of each activity underlying the organization’s supply and demand chains, an ability to disentangle and measure the impact of numerous variables on cost and revenue performance, a deep and holistic understanding of the constraints presented by different management and operational decisions, and a transparent view of the full portfolio of activities from all the silos and subsystems throughout the organization.  That is no easy accomplishment – which of course is why sustainable advantage is no easy thing.

It’s hard enough for enterprises to locate those rare possessors of this sales magic and retain their services, but harder still to deal with the fact that in today’s choice-rich, multifaceted demand environments even those talents alone are not sufficient to achieve sales excellence.  We live in a world, after all, where there are purportedly more SKUs (stock-keeping units) on the planet than there are species of living organisms (see for example Eric Beinhocker’s excellent book “The Origin of Wealth”).  A sales representative working for a company with over 100,000 SKUs, which is the norm for large companies in fast-moving goods industries, has to deal with a dimension to the art of the deal that unfortunately has little to do with charm, wits or good grooming: he or she has to figure out on a daily basis which subset of five or six products, out of that universe of tens of thousands, to offer to customers at whatever combination of price points might stand the greatest probability of winning the business.  The computational dimensions of that notion are staggering – quite simply, they are beyond the realm of the feasible when contemplated by the unaided human brain.

Enter technology and the computational powers of quantitative methods.  That which overwhelms the human mind amounts to a few split microseconds of run time for robust data management platforms.  Revenue optimization models can sift through billions of customer-product combinations to recommend pricing configurations with relatively high probabilities of success.  Perhaps these quantitative models could replace those hard-to-find sales skills – after all, if these models can really crunch all that data and recommend prices with the highest likelihood of success, then anyone holding a BlackBerry can access the information and make the sale, right?  Not so fast.  The world may have changed a great deal from 1964, when Mayer and Greenberg produced their article, but intuition is still intuition, and it is no less a necessary ingredient for sales success today than in years past.  For all that computers can achieve, intuition and empathy are simply not things they do.

But is it possible to teach intuition?  At first blush that would seem to be a stretch.  In the minds of many the concept of quantitative methods is intertwined with that of an opaque, algorithm-powered monolith that spits out Delphic recommendations based on historical data crunched through a process unknowable and unviewable by mere mortals – what is commonly (though not always accurately) referred to as a “black box.”  The problem is that in dynamic environments like consumer goods demand markets, decision makers have to negotiate offers based on a kaleidoscope of real-time inputs that require intuitive judgment.  For example, say that you are a distributor in the food services industry and you see a news item that a national wholesaler has opened a discount distribution center in your sales territory.  How would a salesperson process and assign a value to this information?  As human beings, we are uniquely able to compose propositions out of discrete units of information and then embed those propositions within other propositions and so on, creating a hierarchical tree of a limitless number of propositions.

For example, upon reading the headline “National Wholesaler Opens Discount Distribution Center” a sales rep might begin to formulate a succession of hierarchical propositions in rapid sequence:

• wholesaler opens discount distribution center
• wholesaler who is our competitor opens discount distribution center
• wholesaler who is our competitor opens discount distribution center right down the street from our biggest client
• wholesaler who is our competitor and offers everyday low prices opens discount distribution center right down the street from our biggest client
• wholesaler who is our competitor and offers everyday low prices opens discount distribution center right down the street from our biggest client who was a tough price negotiator in our last sale

Our empathetic, capable sales rep will immediately assign a value of high importance to this information and use it to gauge the tone, tenor and negotiating position of the upcoming sales call with this client.  What if the sales rep could also “inform” the quantitative revenue optimization system about this development and have it factored into the ensuing price recommendations ahead of the sales call?

In fact that is possible in today’s environment.  Market awareness models are able to take qualitative human insights, like our sales rep’s awareness of the real-time implications of the competitive threat, and translate them into quantitative factors the models can employ, in conjunction with all the other relevant variables, to produce improved decision support recommendations.  Of course this is not a brainlessly simple exercise: we still face the challenge of translating the sales rep’s instinctual thought process into a language the machine will understand and recognize.  Nonetheless, market awareness models are one of the leading areas of development in the application of quantitative methods to marketing and sales problems.  It all comes back to that basic question posed by Mayer and Greenberg more than 40 years ago: what makes a great salesperson, and how can we best capture and deploy those skills throughout our organization?