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?

It’s a good question, and one that any decision-maker should ask before embarking on a quantitative marketing solution.  Here are three important questions the manager should ask of any solution being offered:

(a) Is the solution inextricably wedded to a single model of how the world works? This was one of the really fatal flaws in the thinking and modeling practices of economists and financiers in the lead-up to 2008, a flaw most poignantly confessed to by the highest of the high priests of rational economics, Alan Greenspan, in his post-crash testimony to Congress.  Models are supposed to simplify the real world, but not to the point of completely misinterpreting and distorting the behaviors and practices that actually prevail in the world.  Robust quantitative models need to be flexible, adaptable and agnostic in regard to any one single theory that, like rational economics, can become more of a rigid ideology than an objective attempt to explain how the world works.

(b) Is the solution measuring the right thing? Here is where even marketing models with no ideological baggage and with the best of intentions can fall into a trap.  For the past thirty-odd years marketers have tried to define their demand environments through approaches like customer segmentation – identifying demographic segments and then marketing and pricing to the perceived “average” customer in that segment.  A similar approach is segmentation by geography, otherwise known as the “country strategy”.  “How can we optimize our profits in country X?” goes a common problem definition.  But what if the model’s independent variables are (as is often the case) limited to country and product line – answering the question above with a formulation like “sell more turbo widgets in Country X to optimize profits” when in fact the most important influencing variable is actually something else, say a macroeconomic variable like growth in per-capita GDP?  In a world where products proliferate and sales cycles become ever shorter there are numerous variables that influence demand (and hence profitability), and decision-makers need to know these variables at a very granular level – ideally at the level of each potential interaction of customer and product.

(c) Are the right tools being used to measure the right thing? A third pitfall on the road to scientific marketing excellence is the danger of using the wrong tools from the toolkit.  There is a propensity among technology vendors to say things like “our algorithm is better than our competitors’ algorithms”.  In truth there is no one right algorithm because there is no one-size-fits-all solution to anything as complex as markets where product-customer combinations number in the tens of billions.  In this environment there is no such thing as an “average” customer and there is no one single scientific formulation that will solve the problem of making decisions that optimize firmwide performance goals like profitability or market share.  Solutions need to be customized to reflect the unique and constantly evolving contours of each enterprise’s market for its goods and services.

There may be no one perfect, fail-proof screen to detect and avoid the lurking ill effects of physics envy in the market for quantitative business solutions.  But answering a few simple questions like the ones suggested here may go a long way to helping decision-makers avoid the dangers of mathematics for its own sake – and appreciate the value of what mathematical methods can do when applied in the right way for the right reasons.

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?

Those questions were at the heart of a study by a team of behavioral economists and reported in the Journal of Business in 1986 (“Fairness and the Assumptions of Economics” by Daniel Kahneman, Jack Knetsch and Richard Thaler).   Participants (playing the role of the thirsty beachgoer) were told where the beer would come from (ritzy hotel or rundown grocery store) and asked what their maximum permissible price would be.  The results were interesting: respondents who thought their beer was coming from the downmarket store were willing to pay a maximum $1.50 while those who were told the beer would be purchased at the luxury hotel were prepared to shell out $2.65.

What’s so fair about that?  We have to assume that, give or take, the procurement cost to each vendor was roughly the same.  The results of the study seem to indicate a calculus in the minds of the respondents that the beer will inevitably cost more if it comes from the hotel, so they were willing to adjust their own demand curves upwards to meet the perceived point of supply, as opposed to boycotting the transaction opportunity because of a perhaps unfair price differential.  Instinctively that makes sense to me.  Putting myself in the position of the parched beachgoer in the shadow of the ritzy hotel I think I would be more likely to go along with the reality of the $2.65 hotel beer than take a principled stand on the arguable unfairness of a 77% markup.  My experience tells me that it’s simply the way these things work, like it or not.  The results of the Kahneman study say largely the same thing: despite a potentially strong case to be made for the unfairness of the hotel’s pricing scheme, most people willingly go along with its reality and adjust their own internal pricing mechanisms accordingly.

Most of us have been somewhere where we have paid much more for something than we would otherwise – the infamous mini bar and local telephone call surcharges in hotel rooms come to mind.  Ordering a bottle of wine in a restaurant brings about the same experience – I know that a particular 2005 Gigondas retails for $18 at the local wine store but I’ll have to shell out $40 for the same quaff over candlelight and soft music at that romantic little cuisine provençale place down the street.  That $8 bag of peanuts or $40 bottle of wine become reference points – prices we anchor in our brains as reflective of actual experience, and call upon each time we are presented with similar transaction opportunities.  In this process a subtle shift takes place; we are no longer focused on the inherent fairness or not of the underlying state of affairs (high markups in restaurants and hotels) but rather on the fairness of any transaction offered to us in relation to its reference point.  So, going back to the sun-baked beach, if someone offers to go buy a beer for me and tells me the only option is from the hotel bar then my brain calls up the reference point of prior hotel-based transactions and I set my maximum price accordingly.  That $2.65 is an imprecise stab at establishing a benchmark for what the hotel bar should charge for my drink, and as long as it is somewhere in that neighborhood I am okay with the purchase.

salamancan scholars found fairness in the micromarket

Luis Saravia de la Calle, a member of what was known as the Salamancan School of the Late Scholastic period in 15th century Spain, stated that “the just price of a thing is the price which it commonly fetches at the time and place of the deal.”  Interestingly the Salamancans strongly influenced the philosophies of later Austrian School thinkers like Joseph Schumpeter, but also seem to resonate with the more recently emergent tenets of behavioral economics avatars like Kahneman (the 2002 Nobel laureate in economics) and the late Amos Tversky.  In this line of thinking fairness is not some arbitrary notion of a justifiable price based on component costs of production and delivery (like a cost-plus model); if it were, then more people would throw down the gauntlet at the prospect of shelling out 77% more for the same beer just because of where it happens to be sold.  It’s more along the lines of de la Calle’s notion of what prevails at the “time and place of the deal” – which is also what we at Sentrana think of as Pricing 4.0 – the intricate configuration of the needs and propensities of each individual customer at the point of interaction with each individual product.

We may not be able to pinpoint the precise meaning of fairness at all times and all places for all people.  But by better understanding the reference points that anchor buying and selling decisions in the micromarket we have an improved chance of achieving results that are both fair and profitable.

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.

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).

But is that right?  I would argue differently: the playing field where Freetopia meets the Great Deleveraging presents unique opportunities for enterprises that are able to use scientific methods to figure out the detailed contours of this new environment.  Household dollars are hard to come by.  But there are other things of value that factor into Freetopian economics: things like time, attention and reputation.  The key challenge for organizations is to figure out what these things are, who cares about them, where they fit into the picture and how to quantify them for optimal outcome.

I distill the following principal arguments from Anderson’s work: (a) the cost of doing business online is nearly zero; (b) transactions in Freetopia are not classical binary exchanges between a single buyer and a single seller, but rather involve a mix of parties where the exchange of cash is only a part of the value equation; and (c) some of the parties to the transaction are willing to offer some things for free in exchange for other things that confer some other value notion.  These complex multiparty transactions involve exchanges of product, service, cash, convenience, labor, information, gifts, reputation and awareness. In other words, Freetopia is not synonymous with free lunch (though, enjoyably, we discover in Anderson’s book the origins of this phrase as a value proposition used by San Francisco saloons in the late 1800s: anyone paying for a beer got a “free” lunch to go with it).

What this prompts us to do is to think in new ways about how our customers’ demand curves fit into that complex web of interests.  What are the components of the total cost borne by the customer in any given transaction, and what are the terms of value?  How valuable to the customer is a reduction in the cost of search?  What would induce the customer to pay more for A while getting B and C for nothing, or perhaps bartering a service (such as writing a review or filling out a questionnaire) that would benefit some other party to the transaction who would then subsidize part of the cash price of A to make it more appealing to the customer?  These are the types of opportunities that emerge on this new playing field.

The added complexity posed by these non-traditional transaction webs suggests that going by gut instinct alone will not suffice for organizations trying to figure out how to optimally supply their customers’ demand curves.  Nor, however, will the methods embedded in earlier generations of revenue optimization solutions be up to the task.  As Freetopia moves more into the mainstream of our economic lives the scientific methods that help us uncover the most important insights will need to do more than apply conventional optimization algorithms to historical daily prices.  At Sentrana our focus is on achieving mastery at the micromarket level – disentangling all the variables that connote what matters to a given customer at a given node in a given transaction opportunity.  As we look into the kind of future that Freetopia presages, we see an increased urgency for nuanced clarity and a growing role for scientific micromarketing – not as a one-off management tool but something at the strategic core of making the most from the opportunities this daunting – but potentially lucrative new world – will provide.

The problem many corporations frequently suffer from is fractured pricing policies where disparate departments within the organization have conflicting rules regarding pricing strategy. This is often a result of unimpeded change within each department, where every manager relies on their own gut instincts at pricing based on their limited view of the ever-changing picture of customer demand. In addition to this proliferation of pricing policies with the potential to impact the market’s demand, other departments in the organization are also making demand-impact decisions, such as advertising and product mix. These practices are often left to chance because most leaders A) don’t realize the problem exists, B) are currently surviving this economy with a meager profit that is most often derived from a “survivalist” measure like cost cutting, layoffs, and running tighter operations, etc., C) are consumed by the sheer volume and complexity surrounding marketing decisions due to the proliferation of advertising channels, products, customers, and supplier networks, or D) if they realize there is a problem, they aren’t aware of what solutions may exist. What these business leaders don’t realize is that they’re leaving enormous profits on the table all the while giving competitors the opportunity to lure their customers away with the “right” price.

To help shed light on the problems these business leaders are facing, I reflect on a quote from John Wooden – one of the most respected college basketball coaches of all-time with 10 NCAA basketball championships during his tenure at UCLA – where he said, “Before you can lead others, you must be able to lead yourself.” This brilliant insight by a legendary sports icon can also serve as an invaluable business axiom: you can’t lead your market until you lead your organization. Simply put, companies – especially those struggling in this economy – should turn their attention inward. Doing so will require new thinking, advanced technology and a change of focus towards effectively generating growth organically (as opposed to via manic serial mergers and acquisitions) for your firm. Forget about the credit crunch (i.e. insufficient investment capital, the dried up commercial-paper market, etc.), falling consumer demand and other external factors that you can’t control.

I believe that in order for companies to be profitable in this economy, they need to adopt both an enabling technology and decision-making infrastructure to help them determine the optimal prices, marketing mix, and product assortments that will maximize revenue and profitability. Successful implementation requires leadership from the executive suite and bottom-up support from the people who work in all functions throughout the organization. Technology is a necessary component and must be able to serve the firm across its organizational silos in ways that allow each part of the organization to achieve optimal productivity and profitability, taking into account both departmental and firmwide objectives. Visibility throughout the organization is also essential, where the stewards of each of the demand and supply levers in effect have the ability to discover previously unknown or misunderstood elasticities – this can lead to a virtuous loop of discovery and profit.

Times like these require business leaders to move past temporary measures to be successful in both the short and long-term – to not only help their companies survive this downturn, but also to be visionary and lead their market! The insight a company requires to effectively manage their marketing decisions should be robust and holistic – not only do they need optimal prices, but also recommendations on products for customer penetration (cross-selling), optimal deals and promotions and guidance to curtail customer churn. These organizations that are able to not only manage, but also execute their ever-growing array of marketing decisions will have the ability to drive their markets, rather than being driven by them, and will be better positioned to continue leading their markets when the economy returns with vigor, as they will be more adept to discover, view, analyze and act upon the opportunities present in their demand environment. I can only assume that the shrinking credit supply, weak consumer demand and other external factors will likely be with us for some time, but they are of little relevance to any business leader looking to grow their firm organically, and seek to embody John Wooden’s principles in the process.

If there is a lot more to purchase, or a lot more stuff that can be incorporated into the products you make, each party in this supply chain needs to have the financial ability to entertain such a large set of choices.  Looking at income growth and inflation alone conceals the true nature of spending power.  It is not as much about whether or not our incomes today are keeping up with the prices of things we bought yesterday. It’s about whether or not our incomes are keeping up with the additional things we can buy. It’s about whether or not manufacturers’ incomes can keep pace with the exploding set of ingredients they can choose to put into their products, and whether distributors can cost-effectively stock and sell an ever-widening mix of products, and so forth.  The rate at which these new things emerge is faster than the rate at which incomes grow – and therein lays the crux of the pricing problem (firm birth data obtained from U.S. Census Bureau and Income data obtained from U.S. Bureau of Labor Statistics):

Even though inflation may be growing at a rate that is in line with wage growth, the burgeoning number of items available to consumers (and perhaps even critical to consumers – just a decade ago there was no anti-bacterial lotion, and yet now you can’t walk 10 feet in a hospital without walking past an anti-bacterial gel dispenser) makes consumers have less spending power.

This spending power is a 2-dimenional thing, but we have tended to focus on only one of those dimensions – i.e., we’ve levied most of our focus on inflation versus income growth, and have not focused as much on product variety versus income growth.  Today, there are many more things to buy both directly and indirectly (for instance, when we purchase a car today that contains twice as many parts as a car from 20 years ago, we are indirectly purchasing “more things”) and this breadth of choice bites deeply into our spending power.

It is not just whether or not the prices of things that we bought 20 years ago have grown in pace with our incomes, its whether or not the sheer number of products and the total global value of those products have kept pace with the total global value of our incomes. And by this measure, spending power has failed to keep pace.  The obvious response as a seller is to flock to everyday low pricing – but, this “obvious” response actually fails to respond to the right problem (which is one of burgeoning product assortment).  Price reductions alone will not bring spending power up to the levels of power we had just a generation ago.  And the problem is only going to worsen, for innovation will continue to accelerate and the diversity of goods and services offered in the global economy will continue to mushroom.

So, what’s a pricing manager to do in the face of this shrinking spending power headwind?  First and foremost, recognize the strong interplay between your marketing efforts and your pricing.  Every dollar invested in marketing will impact the prices that you can charge for every product in every market (or, for B2B vendors, sales & marketing dollars directly impact the prices that you can charge for every product that can be sold to every customer – so, if you have 100,000 customers and 50,000 SKU’s, you have 5 Billion customer-item combinations that you need to understand).  Marketing effectiveness and pricing excellence are joined at the hip, which means that marketing managers and pricing managers must couple their decisions optimally.  This is especially true now because your marketing voice is drowned out each day by more than 3,000 other voices.  The chart below shows the sharp rise in advertising expenditure in the U.S. alone (data obtained from Coen Structured Advertising Dataset):

Secondly, recognize the strong interplay between your product assortment and your pricing.  In the face of ever-widening product choices, being able to identify the right bundles of products for the right customers or customer segments is pivotal to combating ever-narrowing spending power.  Remember, everyone’s Achilles heel in this race to sell is the explosion of assortment mixes.  If the crux of the problem is product assortment, then therein lay the solution.  Identifying which products to co-sell with other products, and what price that entire combination should have for every single customer or within any single market is the key to winning this race.

Instead of each player receiving $1500 at the start of the game, we would each receive $1000 (to reflect the $50 Trillion of wealth that has been lost in the last 18 months), and instead of collecting $200 for passing “Go”, each player would collect only $100 (to reflect the massive wage losses seen in the last 12 months). To further reflect the broader economic climate, no loans were permitted in the game (i.e., players were not allowed to mortgage their properties to receive cash from the bank, nor were players permitted to issue loans to one another). With these altered rules, our goal was to see how purchase behavior and wealth would unfold on this artificial economic landscape. The results were rather eye-opening, and sheds light on the fundamental dynamics of price in a down economy.

One startling feature of the game that remained consistent between “normal rules” and “recession rules” was that the price of any property on the board, or the price of any house/hotel was publicly displayed for all to see. This price conveyed essential market information about the value of “the goods”. Yet, despite the publicly known value of a property, property prices always deviated from the stated value once a buyer wished to purchase the property from a player that already owned it. Moreover, different buyers were prepared to pay different prices for the same exact property and in all cases the offered prices were higher than the stated value of the property (i.e., the price paid by the original buyer). This pattern was held true despite the recessionary conditions that were imposed on the game. There are a few important observations to note here:

1. Different people were prepared to pay different prices for the same good.
2. Those prices were always higher than the stated value of the good.
3. Buying & selling still occurred despite lowered wealth levels.
4. Buying & selling still occurred despite the unavailability of credit (no mortgages were allowed and no player-to-player loans were allowed).

We observe these same characteristics when… Monopoly is played under normal rules – so what was so different about how things turned out in our “recessionary-rules” Monopoly? Well, the first thing to note is that there was no difference at all on these 4 major characteristics of the game. In other words, despite overall lower levels of wealth and the unavailability of credit, we still see that buyers were prepared to pay prices that were above the lowest price stated on property value card and each player was prepared to pay a price different than what other players wanted to pay. Attenuation of wealth and credit did not reduce the price dispersion in this economic system, and in fact revealed that buying behavior did not rest on who provided the lowest prices for which properties. Rather buying behavior, and the prices that transacted, continued to rest on the specific utility or value that each player individually felt they could derive from a given piece of property. Even though the property is the same, each player’s valuation of that property is different (see Graph) – and no amount of wealth erosion or credit crunch could change this fundamental fact of the market.

So, now we come back to what were the differences between what we observed in “recession-rules” versus “normal-rules” Monopoly. First and foremost, purchases took longer. Players waited longer to accrue savings before deciding to purchase any property. Secondly, there was much heavier buyer concentration and interest in trading properties at the low-end of the pricing scale than at the high-end (not too much interest in Boardwalk, but Baltic was hot). Third, there was much more price dispersion for the low-end properties than for the high-end properties in ”recession-rules” Monopoly as opposed to “normal-rules” Monopoly. The key take-away from this is that with many more buyers for the same good, the odds are higher that there will be a broader spectrum of how each buyer values this good and the net result will be a greater dispersion in offer prices. In other words, the market becomes even more segmented for the low-end properties and this gives rise to a wide variation in prices. As a seller holding the property, the question of should I sell now or sell later where a different buyer with a higher valuation comes along is more important for those properties that we can predict will have more price dispersion because of their “average” low price.

Lesson Learned: Look at your prices and your product assortments simultaneously – don’t drop the wrong thing. In other words, don’t drop prices without thinking about dropping your assortments. If you have 10,000 SKU’s, create product bundles and price each bundle optimally and recognize that offering the lowest price in the market does not increase your chances of weathering this economic storm and may in fact lead to self-created demise.

Let’s say hypothetically that this printer has 10 different attributes like the ones mentioned above on which every buyer places a value, even it happens to be zero. There is a segment of the printer-buying population that wants all 10 attributes, including the HP brand name of the seller, and that segment is willing to pay a higher price. No other seller can satisfy all 10 attributes, giving HP a monopoly on that attribute set. But as a seller, HP operates within constraints since other sellers offer the same exact printer at a lower price in return for providing fewer attributes. Thus, HP cannot set its prices as a pure monopolist, because an excessively high price will drive too much of the market to the next lowest price tier. HP’s competitive position is what I call a micro-monopoly (or “Micropoly” if you prefer the conflation, as I do). The explanation for this price dispersion is that every seller of this printer satisfies a unique mix of attributes demanded by a particular segment of the market. For that segment, the seller has a limited amount of micro-monopoly pricing power.

When viewed from this angle, it becomes easy to see why it makes more sense to set prices based on what your customers value rather than what your competitors charge. The reason is that one firm may compete only tangentially with another firm that sells the same products. The obvious question then is what happens when two firms fulfill the same exact mix of attributes. At this point, firms would then compete on price, but I think this logical extension can be somewhat misleading. In the real world, no two firms ever truly occupy the same attribute space. There will always be at least some differences in the total experience and feel that the customer gets from making the purchase, and thus the potential for price differentiation exists. Multiple optimum prices for the same product can exist in the marketplace. A profit maximizing firm’s objective should not be to race to the bottom in a low price battle with competitors, but rather to understand very clearly what its price ceiling is.

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.