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.

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.

Multiple optimal prices can exist for a product, even in transparent markets. Note that all of the prices in this image apply to the exact same HP printer.

The lessons of efficiently cooling a home can be applied to many scenarios. In business, having a system in place for tweaking procedures continuously is easier to manage over time than are prolonged periods of stasis followed by dramatic transformations. Transformations are complicated. They are often expensive. If too much time passes between transformations, the organization’s inertia coefficient (a 100% made-up term) passes a critical threshold. After that point, two outcomes are the most likely, with a few shades of gray in between: (1) transformation projects mushroom from merely “expensive” to “expensive and painful”, or (2) the company is too lethargic to change, effectively dooming the business to eventual defeat or absorption by more innovative rivals. For the sake of comprehensiveness, I have to acknowledge that for a fortunate few, “federal bailout” must now be added to this list as a third possible outcome. However, in a few years we will see if my suspicion that outcome three eventually finds its way back to outcome two turns out to be correct.

Unlike management theory and the social sciences, the physical sciences rest on a set of principles which can serve as a bedrock for many different streams of work without having to re-establish the foundation. When we put the rover on Mars, we did not have to re-establish that gravity exists. We did not have to re-create Galileo’s experiments by bringing the town of Pisa’s only distinctive landmark to another planet in order to drop the balls from the window again. Experimentation occurs of course, and advances such as quantum mechanics and relativity are challenged, but the principles used to conduct research remain constant for the most part. The principles of management and competitive strategy have no business being treated the same way, however. For an organization to remain healthy, it must continuously challenge and question its assumptions about how best to manage its employees and connect with its customers. By not doing this, businesses invite future competitive disadvantages as static processes outlive their usefulness and simply become too ingrained in the organization to change.

Pricing is a corporate discipline that is unfortunately in need of transformation at most companies. The problem is that most companies, especially those with extremely large numbers of products, customers or both, are incapable of making consistent changes to their pricing strategy or practices. They don’t have the people, tools or the required knowledge to make these adjustments in a principled way. Yet pricing decisions are among the most impactful ones that a company makes when it comes to top- and bottom-line performance. So what do businesses need in order to be able to make constant tweaks to optimize their prices?

First, you need a model. In fact, many models are required. As I have stated before in this space, each price that a business shows to the market is a bet that the figure on the price tag is the one that will bring the greatest profit to the business. What you’re really betting on is the customer’s response: when they see the price, will they buy or reject? To predict the probability of customer response, you need mathematical models. Without them, you are not betting, you’re gambling. Now, it stands to reason that each customer is a little bit different from the one next to him or her. There are many broad similarities that can be identified among customers such as their age, income, gender, ethnicity and so on, but it is the differences that really determine how much a customer is willing to pay for something. Determining what these differences are and how to adjust prices to account for them requires sophisticated analysis of the conditions surrounding past transactions as well the specific attributes of every product that you sell and every customer that you have. It’s not child’s play, true, but it is critical to pricing in the 21^st Century.

A business with 100,000 SKUs and 400,000 products actually has (100,000 * 400,000) = 40 Billion individual customer-product combinations that can be priced!

To really tweak prices at the most granular level, individual models for every customer-item pairing would be required. Remember, a single model for each customer won’t do the job. Personally, I am a completely different customer when I am in the market for fresh fish than I am for gym socks. I buy fish based on freshness without much regard for price, but I buy socks based on price and price alone. A model that labeled me as either “highly price conscious” or “completely insensitive to price” would incorrectly predict my behavior in most circumstances. Similarly, a model built to predict my behavior that somehow averaged out my different price sensitivity levels by labeling me as “medium price-conscious” would be wrong for both examples above. So the need for customer-product-specific models is clear, but that leads to a second issue. Customers’ preferences change over time, and so does their wiliness-to-pay for each item. Models need to adjust regularly as well, possibly even every day.

Pricing can truly become a source of competitive advantage in business if the organization internalizes the ability to adjust pricing models continuously. Why? Pricing at the micro-market level (as we refer to it at Sentrana) requires an extraordinarily nuanced understanding of your customers. Predicting customer response at the individual product level means that your prices are not leaving any money on the table. Moreover, re-building the models continuously to incorporate the newest data (with some mathematical tricks to ensure that important shifts are not ignored or misinterpreted) gives the business a way to continuously learn what the market wants, and what they will pay for it. For this reason, businesses must acclimate themselves to the fact that pricing is a continuous process of discovery, rather than a periodic exercise.

In my next post, I will explain how such a seemingly daunting idea can be automated and become a functioning part of the enterprise fabric.

Restaurants sit at the crossroads of both cost and demand volatility. Much to their detriment, companies such as McDonald’s often buffer both their customers and their upstream suppliers from feeling the financial impact of this volatility. Now McDonald’s is at least hinting that it wants out of this arrangement, and our experiences working with multi-billion dollar partners in the food distribution industry points to this being a wise move. We have long observed significant daily fluctuations in food prices across all categories. Couple this with the effect that a strong dollar can have on McDonald’s overseas business, and it quickly becomes clear that understanding how much a customer is truly willing to pay for a menu item is of huge value for a company so proud of its billions and zillions served.

The real question is why don’t more restaurants (or any number of businesses for that matter) treat their price as the valuable asset that it is? It is not overly difficult for a restaurant to approximate a schedule of demand and create several different menus with prices tailored to different Cost of Goods Sold (COGS) environments. For a restaurant grossing $500,000 in revenues annually, every 1% increase in sales corresponds to a $5,000 improvement to the top line (subtracting the printing costs later). In our experiences in food distribution, a 1-2% increase in the organization’s top line can translate into a bottom line improvement of over 8% – an observation that we have seen replicated in numerous industries. Projecting forward a few years, I would be willing to bet that the majority of companies with the highest valuations among their industry peer groups will also be the ones that are trying to actively shape demand through their pricing strategies.