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.

Health care policymakers may agree on little else, but they do largely agree that the industry’s cost structure is unsustainable.  The whole process of providing health care – including the prescription drugs that account for about 10% of total health care spending – is under the green eyeshade scrutiny of the cost cutting crowd.  Meanwhile insurance companies are increasingly uninclined to pick up the tab for a prescription drug where generic alternatives exist.  And end-consumers themselves are becoming a more central part of the economic equation, as even those with stable employee benefits find their health plans passing on more costs to them.  Prescription drugs then wind up a direct expense item on the monthly household budget, taking a place alongside traditionally more price-sensitive household staple categories like groceries and personal care products.  Those cheery pharmaceutical ads with happy, beautiful people attesting to the wonders of the latest anti-coagulant or cholesterol reducer that saturate the TV channels may seem a bit less compelling to families that have to weigh whether the factors that make those brands more expensive are actually worth the added burden to the household budget.

In particular, I see this as presenting a looming challenge to some of the current practices in managing one of the most (if not the single most) signal economic events for drug manufacturers: the transition of a brand-name drug from on patent to off patent.  Over $60 billion of on patent drugs are scheduled to go off patent between now and 2011, including such widely-known blockbusters as Pfizer’s Lipitor and Aricept, Merck’s Singulair and Sanofi-Aventis’s Xalatan.  If the fate of past blockbusters – Eli Lilly’s off patent experience with Prozac in 2001 comes to mind – is any indication of what is in store for these drug makers then we can expect to see revenue declines of 80% or more when the day of reckoning comes.

That almost looks like the pharmaceutical industry’s equivalent of the “liquidation event” so well-known in the consumer retail sector – but there is a major difference.  Virtually all of those off patent revenue declines come from volume reductions, not changes in price.  In fact, a variety of academic studies show evidence that, to the extent the drug companies actually change their prices in the approach to and immediate aftermath of exclusivity expiration, those are actually price increases, not decreases.  The calculus behind the industry’s preferred mode of competition to date has been based largely on maintenance of a significant price differential with generics based on brand loyalty and certain other means of differentiation.

This price differential is intuitively surprising given the relatively narrow scope of area for competition between originator drugs and generics (by law, generics in any molecular specification must have the same active ingredients as the originator drug, the same route of administration, similar bioequivalence and must have been produced in facilities that meet manufacturing process standards of adequacy).  However, much of the academic inquiry into generic drug price competition has affirmed the success of the drug firms to date in maintaining that differential, labeling it the “generic paradox”.  Essentially, the strategy is to identify that subset of the market to which it can continue to maintain brand differentiation, throw substantial amounts of marketing and sales dollars at that target segment, and live with the predictable revenue declines for off patent drugs while at the other end of the pipeline seeking to shepherd lots of new molecules through the FDA approval process in the hopes that the next Prozac or Lipitor will emerge onto the scene with a 14 year-plus patent protection.

What challenges this status quo more than anything else is the rise of the generic drug industry and its growing acceptance among healthcare providers, insurers and patients alike.  Generics account for about 60% of the drug market today and this sector is growing at just under 10% per year.  Wall Street analysts predict now that Teva Pharmaceutical, the world’s largest generics manufacturer, will see profits growth of 14% annually for the next five years as compared to generally flat earnings for the five largest pharmaceutical concerns.  This implies market share gains at Big Pharma’s expense.  Israel-based Teva already has a market cap ($45 billion) larger than Bristol Myers Squibb or Eli Lilly.  The company’s 37 production facilities generate over 8 billion pills each year.  The smart money seems to be saying that in a world where cost considerations dominate the healthcare landscape, the leading generic makers like Teva and Canonsburg, PA-based Mylan stand to reap the lion’s share of the benefits at the expense of the big brand names.

Unless, that is, Big Pharma figures out a different way to fight back.  The industry does not lack for the size of its marketing budgets, but those dollars are not necessarily being spent in the right places today given the trends described above – heavy sales force deployments to the offices of physicians and other health care providers, and those interminable ads we all have the dubious pleasure of viewing during virtually any prime time television experience.  The real question is: what is the most optimal way to squeeze the most revenue out of every marketing dollar allocated, factoring in the relationship between all the demand levers at the company’s disposal – price, product mix, sales force mechanisms and marketing spend vehicles?  There are potentially rewarding answers to this question, and those answers can be found through innovations in revenue optimization and micromarket science.  I don’t expect to see Big Pharma’s leaders collectively sit back and watch Teva and its ilk cut their markets further down to size.  A relentless focus on optimizing revenue may not be the industry’s historic strength, but I’ll be surprised if it isn’t a fixture in its immediate future.

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?

A single price captures everything that makes you special.  It embodies the value the market sees in your product, the value of your product in this particular season, the value your brand wields in the marketplace, the degree to which your product satisfies the needs of specific customer segments, the degree to which buyers are willing to pay for your reputation, the degree to which buyers are loyal to your product despite competing products, etc.

Once you reveal your prices to the world, your competitors instantly know how much brand equity you have, they immediately see how much value your product has in this particular season, they immediately see your reputation is strong, they are able to assess the amount of loyalty you command, and so forth.  By putting your prices out there for all to see, you implicitly give your competitors a leg-up.  To compete against you, all they need to do is see your price and shoot for something just a tad lower.

What would a future world look like where you only… show your prices to your customers and to no one else?  Only those who are committed to buy your product are actually privy to the price, and this price is zealously protected like the Coca-Cola recipe from all of your competitors.

To see this future world, we can actually look to the past and examine yesteryear’s bazaars.  Prices of the day’s harvest did not appear on some store shelf.  Rather, farmer Jack brought his tomatoes to market and as you held his tomato in your hand, you asked him his price.  And once he told you, you inevitably countered with a lower offer.  After some back and forth, you and Jack settled on a price and the transaction closed.  Jack’s competitor – farmer John –sat at the other end of the bazaar and sold his tomatoes in perfect ignorance of the price Jack just charged.  Researchers have found that in such bazaars there is no single optimal price, and indeed, there is very large price variation for the same good (see Figure below from Epstein & Axtell).  This is ferreted out in multiple computer simulations as well, where the basic laws of buying and selling behavior are imposed on artificial buyers & sellers, and over time the prices offered and counter-offered are seen to drift all over the place, but the drift does narrow over time.

Of course, farmer Jack is just as ignorant as John of his competitors’ prices – he has little idea how much farmer John is charging for his tomatoes.  And if you return day after day to purchase your tomatoes from Jack, you build a rapport and a loyalty – this loyalty is reciprocated by Jack.  He holds the best tomatoes for you, he offers you reasonable discounts in exchange for your loyalty, during times of hardship he may have even give you a free tomato.

Meanwhile, John builds up his own loyal following and sets his own set of prices where no two customers get the same price.  Why should they?  After all, no two customers are the same.  No two customers derive the same utility from a tomato.  No two buyers experience the same satisfaction in the conversation they have with John.  By concealing their prices, Jack and John force their customers to negotiate individual prices, and in the process, Jack and John respectively build “micro-markets” of buyers within the larger market of buyers in this medieval bazaar.

So, did our medieval farmers possess greater pricing wisdom than we do today?  They closely guarded their prices, and achieved the marketing holy grail of one-to-one relationships.  Did they know that their prices were a valuable asset, and therefore should be whispered and meticulously haggled with each individual customer?  Or, did history finally catch up with our farmer Jack?  Was there an economic renaissance where Jack awoke one day and realized that if he broadcast his price to the entire bazaar – even at the risk of letting John know his price – and coupled this broadcast with a marketing message that he has the best tomatoes in the market then he could not only serve many more customers per hour (i.e., he no longer had to spend 10 minutes per customer haggling), he can attract customers.

This historical awakening marks the rise of marketing.  The ascent of marketing is deeply entwined with prices becoming uncloaked.  Your prices are indeed an asset.  They are indeed your most valuable intellectual property.  But, it need not be kept secret because the prices you charge, and can charge, are unique to you.  Only you can charge the prices that you are able to charge.  And that is because you occupy a unique position in the marketing sphere.  There are attributes about you that the market holds dear, and your marketing has influenced the market to hold these very attributes with a unique level of dearness.

That uniqueness gives you the ability to charge prices that need not be the lowest in the market – your marketing uniqueness gives us an optimal price that you can scream to the market with a megaphone and not worry about becoming competitively undermined even if your competitor offers the same exact product.  Because of your marketing uniqueness, your competitors cannot replicate your price.

But, the key is that you need to remember that there is indeed an optimal price that you can charge and you must shy away from the temptation to charge the lowest possible price in the market.  You must recognize that your prices and your marketing go hand-in-hand.  The race to capture customers should not be forsaken with a race to the bottom of the barrel pricing.  Your marketing gives you brand equity, it gives you loyalty, it gives you reputation, it exposes many different customers to you– and all of this gives you a certainly ability to charge prices that lie outside the ability of what our competitors can charge.

We have grown beyond our medieval forbears.  Gone are the days of the bazaar where Jack knew all of his customers by name.  Today, we may not know all of our customers by name – but, thanks to the advent of modern pricing technology and deep analytics, we can know our customers better now than ever before in history.  In fact, our data warehouses and our data mining capabilities allow us to know many more customers with much more intimacy than Jack’s comparatively feeble mind could fathom, and we can optimally market ourselves to all of them and we can mathematically determine the best prices to charge and we can do all of this without spending a single breath haggling.

But, to broadcast prices to the world without using modern pricing science would make us more primitive than our medieval forbears, where in the absence of pricing science they at least knew they had to guard their prices.

If you’re going to let your guard down, do so with prices that you know are optimal for you!

In October 2004, Chris Anderson coined the term the “Long Tail,” referring to a new economic model where companies sell more of less. This was a direct result of the ubiquity of the Internet (along with increased processing power and cheap online data storage), where an unlimited selection exists for information, products and services 24/7/365. He argued that consumers were no longer confined to a narrow list of choices that emerge from large corporate entities in the form of “blockbuster” hits that are meant to satisfy the masses. Instead, consumers were wandering further from mainstream tastes and discovering that their preferences lie in the form of smaller niche movies, books, music, websites, services, etc. I found the theory intriguing back in 2004, but am now reconsidering it’s viability in the context of the global economic crisis: will the long tail survive?

To answer this, I can simply skim the news headlines to find companies scrambling to trim the fat off their product portfolios. No longer is cutting prices a viable strategy for dealing with declining consumer demand. Companies have turned to the ax to focus marketing dollars on their higher-margin, best-selling brands to help retain consumers, who are trading down in the recession. Auto companies have been hardest hit, where GM’s Hummer, Saturn and Saab brands will likely be lost if a buyer isn’t found. Chrysler management has already stated that the company has too many brands and too many dealers. Ford remains afloat, but for how long? Food companies from Sara Lee Food Corp. to H.J. Heinz Co. are trimming their offerings. In the airline industry, Aloha, ATA, MAXjet, Skybus, and Champion Air grounded their planes. Simply put, the long tail just got a little shorter. OK, a lot shorter. As shrinking payrolls, housing values and credit availability continue to push consumer demand down, I think it’s likely Chris Anderson will annotate the theory of the Long Tail to show its existence is more often a byproduct of exuberance in the markets rather than a permanent trend.

For all its impact on the bottom line pricing often seems to be more on the periphery of the activity flow than at the center – an outer rather than a core competency, and sometimes not much more than an afterthought – oh, yeah, we need to stick a price on that now… hmmm, let’s see.   There are three commonly-used methods for firms to price their goods and services, and none of them could be considered the basis for a core competency.   The Old Faithful of pricing methodologies is cost-plus: add up a bunch of direct and indirect costs, slap an arbitrary profit margin on top and voila – that’s the price. Then there’s competitor-based pricing, which many people seem to think is several evolutionary legs up from cost-plus but which Michael Douglas’ Wall Street character Gordon Gekko might have called “a dog with a different set of fleas”.  Why should either bean counters in the accounting department or your competitors be the metronome for what you charge your customers?

The third common pricing method perhaps comes closer to hitting the mark, but it still falls short of a core competency.  In fact it is not really a method per se but more an amalgam of several things – gut instinct, trial and error and maybe some back-of-the-envelope elasticity calculations .  Here the intention is right – try to figure out what motivates a customer to want to pay a certain price and then try to meet it – but the delivery is weak.  Even mid-sized companies in retail or distribution businesses face literally millions upon millions of pricing decisions every day – what product to what customer in what location via what marketing message and selling channel?  The permutations are too staggering to handle in any way other than with technology-aided, systematic rigor.  For a long time the tools did not exist to facilitate this – but as more companies become aware of the tools and best-in-class practices evolve (to borrow another one of those indispensable bons mots from Tom Peters) I expect that we’ll see some migration of the pricing discipline from the periphery to the core.