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.

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.

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.

Here’s an example to help you understand the magnitude of the problem your organization is facing: you sell thousands of products to tens of thousands of different customers each and every day, which is equivalent to millions (if not billions) of distinct customer-product interactions every day – impossible for even the most experienced sales managers to analyze individually. Now grab a pen and some paper and write this down: every sale is an interaction whose revenue can be uniquely maximized! Most companies fail to detect the subtle changes in their customers’ preferences over time, leaving significant profits on the table. And hence the reason for the detective work we’re often called to do; companies don’t realize they have all of the necessary data to maximize revenues right under their noses.

The solution here is Scientific Micromarket Management, which makes it possible for organizations to assess how each customer values your product and offer exactly that price every day in every market. Sure, we may be talking pennies and nickels here, but if you multiply these adjustments by the millions of potential customer-product combinations, then multiply these daily adjustments over the course of a year, and you will realize the significant amount of impact this will have on your bottom-line. Capitalizing on these billions of tiny demand shifts with a dynamic pricing system more targeted than human intuition enables companies to finally understand why every single customer buys what they buy from you and what they are willing to pay for it every time. This is far more comprehensive than any pricing strategy; this is a complete revenue optimization solution. Your customers are getting smarter about you, I think its time you got smarter about them.