Fortunately for business decision-makers, fair pricing and optimal pricing are not at odds with each other but can comfortably coexist.  Over the course of the coming weeks my colleagues at Sentrana and I will be approaching the rich topic of fair pricing in a series of exchanges on this blog.

debating the age-old question of fair price

What is a fair price?  This question has perplexed humanity throughout history.  Leading thought output of the ages, from Aristotle’s Nicomachean Ethics to the Summa Theologicae of  Thomas Aquinas, Pierre de Fermat’s probability proofs and Adam Smith’s classsical economics, have all weighed in with considered opinions on the fairness and justness of alternative ways to price economic goods and services, and the debate continues today.  A series of letters exchanged between Blaise Pascal and Pierre de Fermat in 1654 is often regarded as a primal cause of the development of modern probability theory: this exchange was actually an attempt to establish a scientific basis for the notion of fair price.  In his paper “The Unity and Diversity of Probability” Rutgers professor Glenn Shafer shows how these letters created hypothetical games of value that we today can recognize as the application of probability methods to defend a price as ‘fair’ under conditions of uncertainty.

Uncertainty is the 800-pound gorilla in the room when it comes to price-making decisions.  Buyers and sellers operate from positions of considerable uncertainty in approaching transactions with each other: buyers have only partial information about the features of what they are buying such as quality, reliability, service support and the extent to which a given offered price may be reasonable in relation to these features, while sellers have a limited perspective on what demand exists for their products and what combination of levers such as price, assortment and marketing could influence that demand.  Buyers thus face the risk of inequity in their exchange – paying more than the intrinsic worth of the object acquired, while sellers face the risk of their transactions being unprofitable and, if persistently so, driving them out of business.

Having worked for a number of years in the investment industry I offer up a useful model from this corner of the economy for dealing with uncertainty.  In the investment world uncertainty commands a price: investors demand more compensation, in the form of return on investment, for assets that exhibit higher levels of short term volatility.  Participants widely view this as fair: it is not thought ‘unfair’ that an investor in, say, a 5-year U.S. Treasury note earns a dependable return of 5% whereas someone who takes a punt on the shares of a small-cap biotechnology company may potentially earn over 25% in the same time period.  There is more likelihood that the value of the biotech shares will plunge in the wake of unexpected news or that the company will go out of business than there is of the U.S. government failing to honor its legal obligations to bondholders.  A capitalist economy offers the potential for greater rewards to the investor willing to assume greater risk.

what matters is the customers who don't come

How is this concept analogous to the uncertainty faced by businesses that sell in markets for real (i.e. non-financial) goods and services?  I thought about that the other day while dining out at one of my favorite Northern Italian restaurants, located in a trendy urban area chock-full of good eats.  As I looked around the dining room on a late September Tuesday evening it occurred to me that the uncertainty this business experiences on a daily basis is plainly visible: the number of empty seats during peak dining hours.  Restaurant patronage is a notoriously fickle notion to quantify and is subject to considerable fluctuation in real time.  I wondered about the methodology through which this restaurant’s owner translates the uncertainty of empty seats into the revenue model. It seems to me that the real art to the formulation of this model is not based on the tables that have patrons sitting at them, but rather the ones that are empty.  The hard part of revenue calculation is not figuring out what the average occupied table will spend on any given night – it is dealing with the uncertainty of those empty tables.

Now in theory, the owner could simply build an ‘uncertainty factor’ into menu prices as a partial compensation for the prospect of empty tables.  In practice this is unlikely, and the reason why it is unlikely brings us back to the concept of fairness.  Prospective restaurant patrons (including yours truly) are very unlikely to be sympathetic to the notion that they should have to pay a higher price for the verze e luganega because it helps the owner’s revenue model – to us patrons, that is an unfair offloading of the owner’s problem onto us.  We don’t even have to explicitly know the owner’s motivation.  Discerning customers have plenty of access to comparative information – from other restaurants in the area, our social networks, Internet reviews and so forth – to form strong perceptions of the fairness or unfairness of prices at any given spot.  We will wield our verdict of ‘fair or unfair?’ with much self-righteous certitude in making future dining out decisions.

So what is a ‘fair’ way for our poor restauranteur to manage uncertainty toward a more profitable outcome?   Rather than accepting empty tables as a given fact of life the owner can try to figure out intelligent ways to fill them.  Who may be walking by the restaurant in the late afternoon, or working in a nearby office building and considering an after-work dining outing with colleagues?  What combination of factors might entice these and other prospective patrons to choose this establishment over numerous other choices?  Is there a way to figure out attractive deals that would lure certain prospective customers and to surgically target each such customer with a unique offer?  Yes – it is possible through scientific micromarketing techniques that optimize at the granular level of the customer-product interaction.  The next question – if it is possible, is it also fair?

All those centuries of debate on the notion of fairness and justice in economic commerce now come back into this discussion.  Paul Krugman expressed a concern about this in a New York Times op-ed piece titled “What Price Fairness?” all the way back in October 2000, when price optimization methods were in a much, much earlier stage of development.  His remark (related to the notion of dynamic pricing in general) was that while it may be “arguably good for the economy,” dynamic pricing is also “…unfair: some people pay more just because of who they are.”  Sitting in the restaurant, I imagined a hypothetical case where the gnocchi with sweet basil pesto, which I ordered for the menu-listed price of $14.50, was being enjoyed by the gentleman at a nearby table for $11.30 simply because the restaurant’s micromarketing system contacted his iPhone with a targeted discount offer just before he left his office just down the road.

Is that unfair?  I don’t think so.  Who wins and who loses in this scenario?  The gentleman who receives the offer wins – he gets the opportunity to enjoy a dining experience targeted to his personal preferences.  The restauranteur wins by filling a table that would otherwise be empty, reducing uncertainty and improving the nightly profit intake.  I am still enjoying the gnocchi I ordered at full price and am no worse off than I would have been otherwise; having already concluded that $14.50 is a reasonable price for the dish and ordered on that basis.  On a broader social scale the notion of micromarket pricing does not discriminate between the two of us in a way that I would deem unfair.  I have my own set of preferences that may benefit me with a different offer set on a different day.  In fact, were I to be made aware of the circumstances under which the gentleman got his gnocchi for a lower price, I may well be inclined to leave my own contact information with the establishment in anticipation of future benefits.

There is a road ahead before scientific micromarketing becomes a more accepted feature of B2C commerce situations like that of my hypothetical imaginings while dining out (no doubt helped along by the delights of a 2003 Castello di Camigliano Brunello).  And I expect that a vigorous debate about the question of fairness versus optimality will be part and parcel of this journey.  At day’s end, though, I believe the two are fundamentally compatible.

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.