There is no shortage of quantitative analysis in the trade spend practices of foodservice manufacturers.  Unfortunately, very little of this analysis helps give decision-makers insights about the effectiveness of their trade spend programs.  The numbers being crunched do not relate to signals about actual downstream demand, but rather to the formidable mountain of claims from their distributors.  These claims come in all manner of data formats and accounting entries and it typically takes armies of brokers, salespeople and financial staff to figure them out.  After all the cumbersome and error-prone line-by-line calculations to validate claims are said and done, you are no more informed about the profitability or the potential risks associated with any given program.  No wonder there is widespread dissatisfaction with the effectiveness of these programs.  Over 75% of manufacturers in this sector consider their trade spend initiatives to be inefficient, according to the 2010 Market Intelligence Foodservice Trade Survey.

Pricing signals matter for getting the most from trade spend activities

Decision-makers at foodservice manufacturers need a new approach: one that creates greater visibility throughout complex information chains; and applies analytical methods in order to better align and optimize trade decisions with pricing and other key marketing levers.  Abundant data exist, as do the analytical methods to gain insights from them.  Better measurement and analysis can lead managers to more profitable decisions for themselves as well as their trade partners.

Low-tech, non-standardized processes generate waste

The hodge-podge of disparate programs scattered around the organization with a variety of process and data formats do not easily lend themselves to effective measurement, performance tracking, or coordination with other key marketing and pricing decisions.  Programs tend to have non-standardized and duplicative contracts, cumbersome claims and dispute resolution procedures, and generally low-tech operational processes.  Manufacturers have little way of knowing whether the dollars they are putting into these programs are having measurable impact at the operator and patron level or whether they are simply staying in the pockets of the distributors.  The complexity of the information chain creates a tremendous amount of waste in the system over time that negatively impacts profitability throughout the chain.

New trends in distributor pricing mean opportunities for manufacturers

Such archaic practices stand in sharp contrast to a sea change taking place in distributor pricing: namely, the growing trend of setting prices according to downstream patron and operator demand rather than based on an arbitrary mark-up on the zero sum negotiated price between manufacturers and distributors.  Scientific pricing, an increasingly prevalent practice in the food services wholesale space, offers predictive demand insights for each potential product and customer combination.  Prices thus contain more information about actual downstream demand, enabling products to be pulled through the channel rather than pushed downstream based on the subjective outcomes of manufacturer-distributor negotiations.  Manufacturers have an opportunity to use the same demand signals that inform scientific pricing to guide a more accurate allocation of their trade funds to drive greater overall volume and profit.

Social networking is now standard operating procedure for many restaurant-goers

Let the Facebook Generation work for you

These demand signals are especially relevant because technology has thoroughly transformed the way that retail operators (such as restaurants and caterers) and their patrons communicate.  Digital social networking is now an established way of life for a rapidly growing group of Americans, the majority of whom fall within the most desirable demographic segments of the consumer market.  Sites like Yelp, Urban Spoon and TripAdvisor ensure that salient details about a given restaurant’s menu, prices, food quality, social environment and numerous other attributes are readily available at the fingertips of smartphone-wielding prospective patrons preparing to decide where to gather and dine for the evening.  Clearly, operators have strong incentives to match demand with available supply.  For manufacturers this means abundant information coming from points downstream that can help inform smart trade promotion and pricing decisions.  Decision-makers can gain insights about demand as it relates to geographic and demographic segments; further refine this understanding as it pertains to product categories; and experiment with alternative what-if scenarios to predict the effect of various trade promotion and pricing decisions on demand.

More about trade spend on Sentrana’s blog

In the coming weeks we will be spending some more time on this blog site looking in detail at different aspects of the trade spend challenge and the opportunities we see for foodservice manufacturers to improve performance.  Forthcoming areas of focus include: collaborative campaigns to create win-win programs with trade partners; trade program design; issues related to program execution; and other topics that can help reveal active ways to turn trade spend into trade investment.


Olay, Nivea, Neutrogena and L’Oreal are all established beauty products brands with a broad array of medium-priced product lines and multiple product offerings in each.  More to the point, for purposes of this thought exercise of mine, is that each of them offers a range of good quality facial cleansers, a product I buy on average about once every two months.  The exercise was to determine what, if any, brand loyalty existed in my facial cleanser purchases over the last 2 years.  The answer appeared to be: none.  Nada.  At some point over those past 24 months and (give or take) 12 purchases, my domestic shelf space has been occupied by at least one representative facial cleanser SKU from each of those brands.  I wondered why this was the case.  And then I remembered that it was not always thus.  Long ago (more years than I care to disclose) there was a rather splendid product by Neutrogena called the Facial Cleansing Bar.

a simpler time for consumers

It was a large amber, translucent bar of pure goodness, I thought at the time, and for many years it was the only thing that would ever come to mind in association with facial cleansing. That product still exists, but the last time I bought a bar was back in an era when folks were marveling over the newfound wonders of that thing called email… I wondered: what had happened along this journey from the devoted, faithful me of old to this fickle consumer circa 2010?  What could any one of these companies do to win back my loyalty, and presumably that of many others like me?

Brand success depends on both walletshare and mindshare. If a brand manager wants to get to my wallet then he or she has to first get to my mind and convince me why, out of all the marketing messages that assault me with mind-numbing regularity throughout the changing venues and vistas of my daily routine, this is the one that is most worthy of my time and money.  The problem is that our economy is awash in multiple products, multiple messaging formats and multiple physical & digital marketing channels.  More brands than ever before vie for our attention and our dollars (or rupees, or renminbi); and in so doing the ability of any given brand to make a meaningful impact is diluted by the sheer magnitude and frequency of audio-visual-textual images clamoring to engage our senses.  This, it seems, is what happened to that wonderful amber cleansing bar by Neutrogena – it got lost in the proliferation of categories, products and beauty care messages that exploded into our lives over the last 20-odd years.  This proliferation explosion has engendered many results both positive and negative – one of the latter of which has been to weaken the brand’s ability to connect heart and mind.

If mindshare leads to walletshare, then heartshare leads to mindshare. I don’t believe this paradigm has changed – I think it is no less true today than it was in the golden age of brand advertising (see any episode of Mad Men for a useful reference point).  But the path to the heart is different today, and much trickier.  It’s not all about the brilliant ad that manages to imprint the differentiating qualities of its product so firmly in the cultural mindset that whole households could recite them in their sleep (it is still partly about that, but much less so than the days when the Don Drapers of the world dreamed up the copy over three-martini lunches).  In fact it is not about any one thing, but rather about multiple things – things that form a complex multi-dimensional mathematical equation that would have those ad men of old reaching for the Scotch bottles they kept in their office credenzas.  Namely: how do you match the right message with the right format, for the right geographic region and customer segment via the right marketing channel, for optimal effect? The answer to this equation is not obvious – in fact it is entirely unknowable to the unaided human brain.  To solve it requires vast computational power and advanced scientific methods for solving multivariable problems where many of the variables are interdependent (which adds several levels of complexity to the math needed to get to the solution).

To put it more mundanely, it requires disentangling the clues from that vast sea of transactional information that form a composite picture of the customer-product interaction leading to my walking to the CVS checkout counter with a particular facial cleanser in hand.  What are the demand chain activities that could make this customer-product interaction more predictable; and more personally satisfying for me, the customer?  Is it a pricing question, or a product mix question, or a channel promotions question or an image question?  Traditionally, marketing managers have viewed these as separate issues rather than forming an integrated portfolio of activities around which to optimize a particular objective (like brand value).  But they are not separate, and they cannot be optimally solved in the isolation tanks of marketing department silos.

Fortunately for the beleaguered brand decision-makers of the world there are holistic quantitative marketing solutions that can help them leverage the insights necessary to build and sustain their brand’s value by treating these questions as components of an integrated whole.  The starting point for these solutions is the recognition that what happens in one part of the demand chain affects things that happen in other parts.  As a consumer, my world is more complex now than it used to be and most likely my attention span for any one message is shorter.  But some configuration of activities undertaken by a company – or more than one company along a supply chain – can have a significant impact on me at that reinforces the value of the brand and gives it a meaning more closely aligned with how I actually make purchasing decisions today.

These solutions may lack the simple goodness of that old facial cleansing bar – but they may yet win the heartshare of facial cleanser (and many other) consumers all the world over.

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.

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.

The Red Bead Experiment is quite simple. It starts with the simulation of a factory tasked with the sole objective of making white beads.  The factory’s customers will only accept white beads; beads of any other color are rejected as unacceptable.  In the simulated experiment we represent the operations of the factory with a sampling device that contains a total population of 80% white beads and 20% red beads.  The red beads in turn represent defects caused by one or more organizational or operational flaws (such as poor design, faulty machinery, improper order communication, inadequate resource allocation, shoddy quality control and similar shortcomings).

In the first step of the experiment a manager selects an operational team consisting of six workers, two quality inspectors and a chief inspector.  This team simulates the factory’s “production process” as follows: every day, each worker draws an independent sample of 50 beads from the sampling device.  When a sample is drawn each inspector will separately record the number of red beads in the draw and report that number to the chief inspector, who will record the results.  This initial simulation can go on for several days, i.e. by the end of, say, four days each of the six workers will have drawn four independent samples of 50 beads and the number of red beads (i.e. “defects”) will be recorded for each draw and the results will be averaged to produce a consolidated “performance result” for each worker over this period.

At this point the experiment calls for the manager to employ a combination of suggestions, processes, incentives, threats and so forth (which we can think of as “management tools”) to extract better performance from the workers.  For example the manager may tell one worker whose “defect score” was higher than average to use a different technique when using the sampling paddle to extract the 50 beads (“flip your wrist a bit to the right – yes, like that!”), while telling another whose draw of red beads was lower than that of the group as a whole to “keep up the good work, expand your knowledge of white beads and there will be a year-end bonus in store for you”.  The experiment will repeat over several further iterations, each recording different performance results and with the manager constantly discarding and implementing performance tools in response to the results achieved.

The point of all these performance improvement devices, of course, is that they are pointless: the “system” from which the samples are drawn contains 80% white beads and 20% red beads. Actual results will simply reflect random, independent deviations from this 80/20 distribution and over successive iterations the average of all the draws will converge towards that 80/20 split.  The real underlying message is that measuring the effect of any given performance tool (whether it be based on incentive, threat, knowledge or process improvement) is useless without a grounded understanding and (where possible) measurement of the system itself.  In the language of Deming’s experiment, if you want to optimize the system for minimal red bead production then figure out how to change the 80/20 stasis at the system’s heart – then use appropriate management performance tools to align the activities of all the organizational resources in a self-reinforcing manner to achieve this desired strategic outcome.

Management tools have proliferated in the years since Deming’s heyday, and many of them offer the potential for real performance improvement. For example, organizations have the ability to surgically manipulate the operational levers at their disposal through performance approaches such as Supply Chain Management on the cost side and Price Optimization on the revenue side.  However, translating the benefits from such approaches into sustainable competitive advantage requires something more than the mere implementation of these (or other) tools: a granular understanding of each activity underlying the organization’s supply and demand chains, an ability to disentangle and measure the impact of numerous variables on cost and revenue performance, a deep and holistic understanding of the constraints presented by different management and operational decisions, and a transparent view of the full portfolio of activities from all the silos and subsystems throughout the organization.  That is no easy accomplishment – which of course is why sustainable advantage is no easy thing.

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.

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.

Looking at the human mind this way, as a machine constantly evaluating possible outcomes based on prior knowledge and assigning probability weights to those outcomes, gave rise to the notion of the “Bayesian brain,” a term popularized in 1983 by Geoffrey Hinton of the University of Toronto and Terry Sejnowski of Johns Hopkins University.  This notion has subsequently received a good amount of validation by neuroscientists as they continue to make advances in understanding how the brain really works.  Neuroscientists Alexandre Pouget and David Knill of the University of Rochester in 2004 referred to a “growing body of evidence that human perceptual computations are ‘Bayes optimal’ (“The Bayesian brain: the role of uncertainty in neural coding and computation,” Trends in Neurosciences, vol 27 issue 12, December 2004, pp 712-719).  That’s a fancy way of saying that we make estimations based on likelihood – for example we routinely make estimations about things like the distance from us of an object, or the speed at which it is traveling, based on our prior knowledge of the shape, clarity and movement patterns of such objects and the likelihood that the present reality fits into that a priori knowledge.

So far, so good.  But there is an Achilles heel to our hierarchical mental gymnastics.  Briefly, we may be great at the kind of proposition-within-proposition reasoning that our sales rep exhibited in getting to the essence of the competitive threat posed by the wholesaler’s move to town.  But we humans are generally pretty lame when it comes to computation.  Our intuitive reasoning skills fail at the task of instantaneously calculating that 34 x 57 X 71 = 137,598.  One way that we get around this failing is through heuristics – basically, shortcuts that we use in conditions of uncertainty to help us get from information to evaluation and decision.  Take the example above of evaluating the distance a particular object might be from us.  Now, there is a physics formula we could apply to measuring distance, height, momentum (if moving) and so forth to give us the precise answer as to how far away that bicyclist in the yellow jersey is from us.  But our brains lack the ability to do spontaneous physics equations.  Even if we precisely knew one or more of the variables it would be hard to do an on-the-spot computation.  So we need something else – a proxy, a heuristic.  That something might be clarity.  Can we see the bicyclist clearly?  Can we make out the details of his black helmet with red stripes, and the ‘Elf Aquitaine’ logo on the yellow jersey?  That can give us enough information to call upon our “cyclist in a yellow jersey” neuronal connections and infer a likelihood that he is, say, 50 yards away.

The problem with heuristics is that they are subject to error: not occasional lapses in judgment but systematic, predictable biases.  For example if we use clarity as a heuristic we may overestimate the distance of that cyclist from us if there is poor visibility.  Understanding the role of heuristic errors in human judgment and decision making was one of the main contributions of Amos Tversky and Daniel Kahneman to our understanding of behavioral factors in human decision-making (“Judgment Under Uncertainty: Heuristics and Biases,” Science New Series vol 185 no. 4157 Sep 1974 pp 1124-1131).  Tversky and Kahneman documented specific heuristic errors such as representativeness (drawing broad or sweeping conclusions from a limited data set), availability (assigning the likelihood of an event based on the easiest example that comes to mind, whether or not appropriate, and anchoring (relying heavily on one piece of information when making a decision even if it is irrelevant).

Since heuristic errors are part and parcel of human judgment and decision-making under uncertainty, we have to take this reality into account when we attempt to integrate quantitative modeling methods and qualitative human judgment.  What are the best mathematical tools and frameworks to integrate these two domains?  One area in which we at Sentrana are spending considerable time is that of Bayesian hierarchical modeling. The Bayesian approach is particularly useful in marketing situations such as modeling differences in the needs and wants of customers using both generalized and conditional assumptions involving multiple variables. Bayesian frameworks provide a natural way to pool disparate sources of information. A Bayesian model requires the formulation of prior distributions and the estimation of a likelihood function, which can add complexity to the model-building process.  However we expect future insights and innovations in this area, alongside the development of more robust computation capabilities to bring more firepower to bear on this difficult but potentially valuable quantitative approach.