But let’s start at the beginning.  In the beginning there was the trade, and the trade saved humanity.  Seriously.

Homo neanderthalensis – Neanderthal man – had been occupying the planet for about 200,000 years when our ancestral gene pool, Homo sapiens, showed up on the scene (both species evolved from a common ancestor Homo habilis that had begun to make and use basic tools about 2.5 Ma (million years ago), but their evolutionary paths diverged some 600,000 Ma).  Despite what would seem to be a solid first-mover advantage thriving in the harsh Ice Age climate of Europe and Western Asia, Neanderthal man vanished from the face of the earth sometime around 30,000 years ago while the progeny of H. sapiens went on to give the world the Hanging Gardens of Babylon, Magna Carta and How I Met Your Mother.  In 2005 academicians Richard Horan, Erwin Bulte and Jason Shogren presented a well-researched argument for why this happened: trade.  According to their paper “How Trade Saved Humanity from Biological Extinction: An Economic Theory of Neanderthal Extinction” it appears that our ancestors had particularly honed skills in organizing specialized activities such as tool-making, and trading their goods between different social organizations.  As the Ice Age melted and populations grew and migrated, the skills of free trade became an evolutionary competitive edge.

With trade was born the concept of price – you can’t have one without the other.  The first trade probably went something like this: I want one of your stone axes and I’ll give you two fur pelts for it.  Pricing 1.0 was essentially the fine art of haggling between parties to agree on the relative values of items being exchanged in a trade.  The simple mechanics of Pricing 1.0 were effective enough to last for most of human history, from hunter-gatherer societies to the bazaars of the Levant and the Greek and Roman agorae, and onto medieval town square markets.

the micromarket of yore

In the town square every customer was his or her own living, breathing micromarket, and every interaction between that customer and any given product available for sale was unique.  Sellers of goods in the market got to know their buyers’ habits, buyers got to know their vendors’ quirks, and everyone kept mental images of successful transactions fresh in their heads so as to have a good basis from which to negotiate in future transactions.  The population of customers as well as the daily supply of goods was usually small enough that an average human brain could retain the necessary information to buy and sell effectively without the need for hard-and-fast systems regulating or standardizing the terms of trade.

That all changed very rapidly in the most explosive 250 years ever of human economic activity that started with the Industrial Revolution.  Actually the Revolution was just about humans doing what they do so well – specializing and trading – but on technology-fueled steroids enabling massive leaps in productivity.  Eric Beinhocker presents in his 2006 book “The Origin of Wealth” (using data estimates from University of California-Berkeley economist J. Bradford DeLong) that world GDP per capita roughly doubled from the era of hunter-gatherers to 1750 CE, then exploded 37 times again in the next quarter-millennium to the beginning of the 21st century.  As process specialization became ever more sophisticated so did the financial accounting methods businesses needed to employ to ensure they earned a profit – counting up the cash in the till at the end of the day was not going to do it.  From this was born Pricing 2.0: figure out how much it costs to produce 48,000 pins per day (using Adam Smith’s well-known example in “The Wealth of Nations”) taking into account direct labor and materials, administrative fixed costs and distribution logistics – and tack on a little percentage over that to serve as the profit. We of course know this as the cost-plus methodology that even today continues to be used by many organizations.

In the 1970s and 1980s companies in the business of producing, distributing and selling consumer goods realized that the increasing role of technology and science in the fields of operations and finance could also be applied to marketing.  By recording each day’s sales transactions into a database, marketing decision-makers could mine the information for clues as to how to better market certain products to certain customers.  Popular practices such as customer loyalty programs, combined with increasingly sophisticated third-party data about demographic and psychographic market segments, helped marketers to hone in on ever-more informed answers to the basic challenge of marketing: how to sell the right product to the right customer in the right place at the right price.  We can think of Pricing 3.0 as Managed Pricing – a broad diversity of marketing-driven strategies to price certain goods in certain stores in a manner to attract more buyers and increase revenues.  The key scientific tool for Pricing 3.0 was the concept of elasticity: how much will a unit change in price affect the quantity demanded?

The successive eras of Big Box discount stores, specialty malls and most recently e-tailing are a long way from those micromarkets in the medieval town squares.  For all that we gained since then – gains in wealth, product choice and service efficiency to name but a few – we also lost something.  Sellers lost that unique knowledge they possessed in the town square of every individual customer and the particular assortment of factors that led to successful sales.  That unique micromarket knowledge was lost in the increasingly complex value chains of increasingly abundant economies.  In order to make sense of the opportunities available Pricing 2.0 and Pricing 3.0 approached the market from the top down.  Their homing beacon was the average: what is the average customer willing to pay for a dishwasher, or pair of dress slacks, or ketchup, and how can we set the price to attract that average?

The truth, of course, is that no customer is average.  Is there a way to marry that unique micromarket knowledge of the medieval town square with the complex realities and efficiencies of our 21st century economy?  There is, and it is called Pricing 4.0 – Scientific Micromarketing.  Scientific micromarketing goes back to the medieval town square armed with the 21st century weaponry of robust computational processing capabilities and advanced mathematical techniques like Hierarchical Bayesian modeling.  In this way Pricing 4.0 comes at the market, not from the top-down perspective of the law of averages, but rather the bottom-up perspective of the market at that most granular level of the interaction between each potential customer and each potential item.  Pricing 4.0 is not a gamble based on a presumed “right price” – it is an informed bet based on the odds that the offer of a particular product to a particular customer at a particular price will be successful.

It took 129,750 years (give or take!) to evolve from Pricing 1.0 to 2.0.  It took some 220 years to go from 2.0 to 3.0, and about 30 more to arrive at 4.0, the new age of micromarketing.  Of course earlier generations never die out completely.  Just as there are no doubt still some people using Windows 95, and plenty of Cubans driving their 1950s-era DeSotos, so in many corners of the globe the art of the haggle a la Pricing 1.0 continues to retain its appeal.  And many, many of the world’s largest corporations continue to rely primarily on the cost-plus model of Pricing 2.0 despite its extensively documented shortcomings (which are too voluminous to treat in sufficient depth in this posting).  But Pricing 4.0 has arrived, and companies grappling with the challenge of truly figuring out their ever-more complex demand environments have the opportunity to begin the journey down this path.  It leads us back to the erstwhile town square in a way that the merchants of old could have hardly imagined.

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?