Physics Envy: Pervasive, But Not Incurable

Everywhere you look, it seems, people are talking about “physics envy”.  This derisive term mocks the attempt of economists and other social sciences practitioners to imbue their disciplines with the equations and mathematical rigor of physics – a rigor that many believe fails when applied to the messy environments of disciplines like sociology or economics.  It’s not a new term – economist Philip Mirowski contributed to the Finnish Economic Papers series way back in 1992 with a piece entitled “Do Economists Suffer from Physics Envy?”

kinetic energy, not supply & demand

Eighteen years later the answer from many observation posts along the byways of public discourse appears to be: yes, they most certainly do, and so do their fellow travelers, business and financial markets experts.  After all, we just barely survived the most devastating economic event of our times, deeper and more far-reaching than any downturn since the Great Depression, and all the high priests of the field can do is shake their heads and say “wow, I sure didn’t see that coming.”  Distrust of fancy math is rampant in all walks of business life.  That presents a real problem for enterprise decision-makers at a time when they need smart quantitative tools – yes, fancy math and all – more than ever.  Markets are more complex than at any time in human history.  Giant waves of transactional data inundate marketing managers with new information every day.  Managers need science to help them gain valuable insights into the markets for their products and services – but how do they know that the growing number and variety of scientific marketing tools out there aren’t infected with the nasty symptoms of physics envy? Read the rest of this entry »

Subscribe   |  

Red Beads, Management Tools and the Elusive Quest for Strategic Advantage

Management tools do not automatically confer strategic advantage.  In principle any commercially available modern management tool from Total Quality Management to Lean Six Sigma, from Supply Chain Management to Price Optimization Models, is available to any and all paying customers on equal terms.  Two competitors in the same industry space may employ the exact same suite of management tools, but it is a good bet that their relative performance will vary considerably over time.  I don’t find this particularly surprising: generally speaking I subscribe to the view of competitive strategy vis a vis productivity enhancement tools eloquently expressed by Michael Porter in his 1996 Harvard Business Review article “What is Strategy?”  To wit: “Competitive strategy is about being different.  It means deliberately choosing a different set of activities to deliver a unique mix of value”.  That is to say, the act of hiring a Process Re-engineering implementation team or reinventing oneself overnight as a Learning Corporation will not automatically confer sustainable advantage.  Rather it is how (and if) those tools are integrated into a portfolio of aligned, mutually reinforcing organizational activities distinctive from those of competitors that will most likely make the advantage difference.

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.

Read the rest of this entry »

Subscribe   |  

A Beer on the Beach, and Other Mysteries of Fair Pricing

Businesses want us to view them as fair – there is arguably nothing more important than a reputation for fairness in the daily marketplace of commercial transactions. As business managers what can we do to ensure that decisions we make – about pricing or other actions that are clearly visible at the point of the customer-product interaction – will be seen as fair? Is fairness something absolute, immutable and precisely quantifiable?  Or is it situational, capricious and ever-changing?  The bad news, perhaps, is that ‘fairness’ is a very elusive notion to pin down with certainty – it’s hard to put fairness in a bottle and label it as such.  The good news is that fairness more than anything else is about perception and the relative judgments of your customers and potential customers in varying demand situations.  That’s good news because the better you understand the granular contours of your demand environment and the precise needs and propensities of your customers, the more likely you are to understand how to make decisions in that environment that are both fair to the customer and profit-optimizing to your business.

thirst-quenching - but is it fairly priced?

Here’s a test of fairness.  Imagine you are lying on the beach on a hot summer day and find yourself craving a cold, satisfying beer.  What price would you be willing to pay to quench your thirst?  Now imagine two alternative scenarios.  In one, the only place within walking distance to buy a beer is the poolside bar of a swanky five-star beachfront hotel.  In the other, there is a rather run-down beachfront grocery store that sells beer.  Imagine further that both the hotel and the grocery store sell the exact same brand and type of beer.  Does your maximum price point change depending on whether you think you are getting the beer from the hotel or the store?  Do you think it is fair for two different establishments to sell the same commodity for a different price? Read the rest of this entry »

Subscribe   |  

Fair Price, Optimal Price

Businesses seek to maximize the value they can obtain from their revenue models.  Price is the key lever decision-makers can operate to influence revenue, and in recent years a growing number of businesses have sought to implement strategies for actively managing the price lever – strategies such as demand management and revenue optimization.  However businesses are also highly sensitive to the perception by individual consumers and the society at large that their prices are fair, in other words that they do not violate widely held individual or societal norms.  Fair pricing matters – it matters to me, and to you, and perhaps ever more so in a climate characterized by economic uncertainty, downward pressure on demand and a perceptible decrease in the citizenry’s trust of public and private institutions.

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. Read the rest of this entry »

Subscribe   |  

From 1.0 to 4.0 in 130,000 Years: Pricing’s Extraordinary Adventure from Haggling to Scientific Micromarketing

Pricing has evolved from the ancient art of haggling to the application of scientific methods to the micromarket.  In a sense we are going back to the unique knowledge of individual customers and products that existed in the old bazaars and town squares – but we’re armed with powerful technological tools of the 21st century.  The world of Pricing 4.0 is upon us.

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. Read the rest of this entry »

Subscribe   |  

Revenue Optimization: Coming Soon to a Big Drug Company Near You

Large brand-name drug companies – Big Pharma in the common vernacular – are not exactly known for competitive pricing or razor-thin margins.  For 2008 the industry was ranked third most profitable in the U.S. according to Fortune magazine, with average profit-to-sales margins of 19.3%.  That’s a pretty fat comfort zone compared to the scorched-earth landscape of many other industries…or is it?  Until recently Big Pharma was pretty consistent at the #1 spot in those rankings. A look under the microscope reveals some troubles bubbling up in the hitherto happy world of magic molecules and blockbuster brands.  These days the whole country seems transfixed by the subject of healthcare, and no matter what does or does not come out of the legislative sausage factory this year, some major trends are afoot that have potentially far-reaching consequences for Big Pharma and may influence the normally lackadaisical approach drug makers have exhibited to the prices they charge for their brand-name drugs – in particular when those drugs reach the end of their exclusivity protection period and go off patent. Read the rest of this entry »

Subscribe   |  

Optimizing the Playing Field Where the Great Deleveraging Meets Freetopia

Two economic developments are currently having a profound effect on the playing field of consumer demand.  One is the Great Deleveraging: the painful scaling back of the household debt burden that reached a historical peak, at 133% of household income, in late 2007.  The Great Deleveraging means that household dollars that several years ago would have been earmarked for new discretionary spending are instead being diverted to pay down the hangover of old discretionary spending.  As fewer dollars chase the same supply of products we would expect some combination of lower prices and/or a reduction in the quantity of products supplied – a reversal of the SKU proliferation that has been a dominant feature of our consumer experience for the past several decades.

At the same time, though, a second major event appears to be unfolding:  the emergence of the economics of “free,” or “freeconomics” as provocatively described by Chris Anderson of Wired magazine in his recently published book “Free: The Future of a Radical Price.”  “Free” in Anderson’s formulation is the notion that the near-zero cost of doing business online turns upside down the conventional notion of economics as the science of parsimonious choices under conditions of scarcity.  The “economics of abundance” in Anderson’s phraseology may filter through the prism of our traditional understanding of markets as being good news for cash-strapped consumers (more stuff for which I don’t have to pay money) and bad news for suppliers of goods and services (“free” doesn’t sound like a price that will shore up my profit margins). Read the rest of this entry »

Subscribe   |  

Quantitative Intuition II: The Bayesian Brain’s Achilles Heel

In a previous posting (“Quantitative Intuition: It’s Not Counterintuitive”) I described some of the advancements that have been made in bringing together the disparate worlds of quantitative methods and human intuition, ending on the rather happy note that advanced scientific micromarketing models today are capable of introducing qualitative human judgment and experience into quantitative models, such that the models are able to “learn” from humans about important factors such as competitive threats, nuanced negotiation strategies and even meteorological vagaries – factors that traditionally have been difficult to crunch into the binary 1s and 0s of machine language.  The human brain works in a hierarchical manner, embedding propositions within propositions to think a potentially infinite number of thoughts.   In the example I used in the last posting, a sales rep who reads about a national wholesaler coming to town to open a discount distribution center can nearly instantaneously form a series of mental propositions to evaluate the importance of that news and the probability of potential outcomes that may (or may not) require decisive competitive action from the sales rep’s firm. Read the rest of this entry »

Subscribe   |  

Quantitative Intuition: It’s Not Counterintuitive (Nor an Oxymoron)

Think of the best salesperson you know: if you’re fortunate, perhaps someone in your company or, less happily, in a competitor’s firm.  What are the qualities that make this person excel at the job of sales?  In a classic Harvard Business Review article “What Makes a Great Salesperson” (July-August 1964) David Mayer and Herbert Greenberg likened a star salesperson to a heat-seeking missile: “Sensing what customers are feeling, they [the sales stars] are able to change pace, double back on the track, and make whatever creative modifications might be necessary to home in on the target and close the sale.”   Whereas most of us have intuitive abilities to a greater or lesser extent, excellent salespeople lever this intuition with strong empathy skills (sensing what the customer’s needs are) and the relentless personal drive necessary to cross the finish line.  If they could, managers would bottle this elusive elixir of talents and have all their salespeople drink it, every morning of every day. Read the rest of this entry »

Subscribe   |  

Models Didn’t Bring Down Wall Street; People Brought Down Wall Street

“Burn the mathematics” wrote economist Alfred Marshall in a letter to a friend, musing about the proper role of mathematics and scientific inquiry in the field of economics.  That 19th century cogitation would seem to be a prêt-a-porter soundbite for these latter days of the 21st century’s first decade – a time in which the mathematical infrastructure that underpins longstanding economic and financial theories stands accused of all manner of malfeasance, particularly given its presumed role in the decade’s signature economic event – the financial market meltdown of 2008.  The logic behind the accusation goes roughly thus: More complex (but not necessarily more “accurate”) models allow for more complex instruments to be created. Increased complexity means it takes more time to process and then fully comprehend what the numbers may be telling you. At the same time, though, technology allows buy and sell orders to be executed almost instantaneously through electronic trading systems. Time is of the essence, and ponderously complex computations simply won’t do.  A seemingly elegant (and fast, and commercially viable) shortcut is discovered and becomes the currency of the day. The models’ outputs come to be trusted blindly simply because there is no time to question them (and too much money to be made by using them). The impenetrable Greek letters obfuscate the sensitivity of the models to changes in important assumptions – which is fine for a few years because those assumptions (e.g. rising housing prices) don’t change – but then all of a sudden they do. The models start losing more money than they make. Then the chasm widens further as the high levels of leverage in the system make themselves felt. The losses accelerate dramatically, wiping out years of profits in just a few months. Burn the mathematics, indeed.

But let’s take a different look at this apparent tight coupling of mathematics and dire outcomes. Our recent correspondence with an author who has been widely published on the subject of Wall Street’s use of mathematical models recently offered to us an interesting opinion. His point was that the problem with the models was not so much their complexity, but rather that they were models in the first place. His argument was that you can’t ever perfectly hedge model risk.  Now, I agree with that observation: a model by definition selects some aspects of reality to represent and omits others, and the choice of what to include and what to omit is subject to human error, therefore fallible and not perfectly hedgable.  But I take issue with the idea that the fault lies in the existence of the models themselves.  Models can be misused – I think that much is clear. But the notion that models are all doomed to failure obscures a deeper truth about the goals of predictive modeling; namely that you can seek either to reduce the world or truly explain it. By trying to elegantly reduce the world to as few predictor variables as possible, you are more likely to be sowing the seeds of future failure, because complexity and actual drivers of outcomes are taken out of the equations to make them more solvable (or perhaps sellable, as in the case of the Gaussian copula function that was behind Wall Street’s demise, as we discussed in a previous posting “You Can’t Punt Away the Dimensionality Curse”). Predictive modelers don’t have to go down that road, however: they can also set out with the goal not of reducing an entire system to a single neat, tractable equation, but to quantify and explain all of the relationships that dictate outcomes to the absolute fullest extent possible. Tractability and computability are things to address later in the process, through technological means, but they should not dictate the fundamental mathematical approach at the outset. Read the rest of this entry »

Subscribe   |