The 5,000 Year Marathon: In the Race to Buy & Sell, Who Wins & Loses? (… Especially When Product Choices Grow Faster than Incomes!)

Inflation rates provide a reasonable yardstick for measuring buyers’ purchasing power.  By comparing income growth with inflation, we can determine how well buyers are able to keep up with rising product prices.  But, there is something that is perhaps much more important in our ever-expanding (or, nowadays, contracting) economy that is unmeasured.  Just comparing inflation with income growth does not allow us to see how well consumers are keeping up with rising numbers of products.  And this product proliferation not only impacts consumers’ purchasing power, it has deep impacts all the way up the supply chain to the purchasing power of retailers, distributors, and ultimately manufacturers.

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

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

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In Economic Modeling, Can Hindsight Lead to Foresight?

In thinking more about my last posting here on failed Wall Street quant models and the dimensionality curse I started to wonder whether we could ever be more than the archetypal Monday morning quarterbacks:  commenting brilliantly on all the reasons why X should never have happened, after X has already happened and done its damage.  Can the mistakes of hindsight lead to foresight?  In other words, can we apply foresight to develop “good” economic models that won’t blow up in our faces?

In trying to answer this postulation we must go back to examine the eternal challenge of good modeling: how to create a simplified representation of reality that in ignoring many real-world features still manages to convey an inherently robust facsimile of the real thing.  For example, one of those maps of New England you buy at Exxon gas stations can serve as a good model for getting you from Hartford, CT to Boston, MA even if it ignores most of the streets and alleyways and other real-world detail that exist along the route.  In their book “Complex Adaptive Systems: An Introduction to Computational Models of Social Life” John H. Miller and Scott E. Page observe that the “ability to ignore is a crucial component of scientific progress”, using the image of a parent’s being able to respond to the incessant “why” questions of a three year old child by saying “just because”.  The trick, as the authors point out, is knowing when (and perhaps more importantly when not) to say “just because”.

While I wholeheartedly agree with that assertion I don’t think that it quite gets us to an adequate level of comfort in applying foresight to the creation of good models.  In his fascinating book “The Origin of Wealth” Eric D. Beinhocker points out that economic modeling took what many consider to be a wrong turn back in the latter years of the 19th century when leading thinkers of the day like Leon Walras and William Stanley Jevons borrowed heavily from the referential context of physics to create models for explaining economic activity, including such notable concepts as a mathematically representable state of equilibrium that continue to serve as the conceptual foundations of modern economics textbooks.  As Beinhocker elaborates, the problem with these models was that some of their fundamental assumptions – like the perfect, robot-like rationality of human beings in making economic choices – didn’t seem to simplify reality as much as contradict reality.  Thus we find ourselves in the present ruminating over the precise, mathematically elegant language of physics and the complex, evolutionary language of biology and debating whether a choice of the wrong science by the founding fathers of economics back in the 19th century led to the failure of models to adequately explain much of what is going on in the economy today and in particular the string of boom-bust upheavals that have become part and parcel of the last 20-odd years of economic activity.

I still don’t think we are there yet in getting closure on the foresight question, but we may be getting closer.  To tie in the strands of thought presented by Miller & Page and Beinhocker, when we get to those basic defining assumptions, Read the rest of this entry »

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Forget Your Competitors, The Power to Consistently Lead Your Market Lies In Understanding How Every Customer Values Your Product

Far too often, we have companies seeking our expertise to ascertain their competitors’ competitive strategy vis-à-vis their pricing, as if this will provide the magical insight they need to help them maximize their own revenues. My advice: save the detective work for Colombo and forget about your competitors! Your bottom line profits should not hinge upon a competitive response strategy that reacts to your competitors’ price moves, where you surrender control over your revenue structure and end up locking your firm into a race-to-the-bottom pricing with the rest of the industry. Escaping this destructive cycle lies in focusing relentlessly on your customers rather than your competitors. If you’ve read the news in the last 10 years, you may have realized that your customers are the most informed consumers in the history of the world! They are utilizing every available resource, from various news and industry websites to trade magazines to word-of-mouth gossip to Oprah to… well, even your price helps them determine their perceived value of your product. They are better informed about their purchases than ever before, but I wonder if you are learning as much about them and how they view your products?

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.

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What a Rainy Day Teaches Us about Pricing in a Recession

As the weather soured this past weekend, our plans for a long outdoor hike morphed into a long indoor marathon of Monopoly™. There were 5 of us, and figured that given the unexpected rainfall, we might as well dust off the Monopoly board and spend our afternoon keeping dry. To make the game a bit more interesting and reflect the current economic climate, we altered the rules – which we referred to as “recession-rules” Monopoly (as opposed to “normal-rules” Monopoly).

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

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

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

We observe these same characteristics when… Read the rest of this entry »

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The Micro-Monopoly Phenomenon

Here’s an interesting market experiment that you can try without leaving your desk. Go to www.pricegrabber.com, choose a merchandise category, and then select a product that has more than a half-dozen or so different sellers. Sort the list by price, and compare the highest price to the lowest. Having just performed this for the HP Laser Jet 1022n laser printer, I see that I have the option to pay as much as $290.00 or as little as $115.00, plus a range of prices in between. That’s a lot of variance for the exact same product. The highest price is almost three times as high as the lowest. Yet all sales have not been captured by the lowest-price seller, nor has the most expensive retailer (which happens to be HP itself) gone out of business. Intuitively, you may already be rationalizing this phenomenon to yourself. People are willing to pay for things like the seller’s brand strength, return policy, warranty, service packages, availability, and so on, which is why different prices are charged. I didn’t bat an eyelash when I saw the price range on the screen, even though it seems to contradict the premise of market-clearing prices in perfectly competitive, transparent markets. We understand the reasons for these differences, but there is a deeper insight to be gleaned from this apparent oddity.

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

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

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You Can’t Punt Away the Dimensionality Curse

A single mathematical formula brought ruin to the global financial markets.  What happened was not a failure of quantitative methods per se but rather a lesson in the perils of ignoring real-world complexities in favor of deceptively elegant shortcuts.

The fault, dear investor, lies not in the head of AIG’s Financial Products Group or members of the Bear Stearns Investment Committee or any other anthropomorphic entity: rather it was a single mathematical formula that apparently felled the pillars of global finance.  That’s the gist of a recent article in the 3.17 edition of Wired magazine entitled “Recipe for Disaster: The Formula that Killed Wall Street” by Felix Salmon.  The formula, known as a Gaussian copula function (when is the last time that term became a fixture of the public discourse?), purported to solve the mother of all securitization problems: establishing default correlation factors between the many constituents of the pools of mortgages and other credit obligations whose cash flows served as the underpinning for the complex derivative securities known as collateralized debt obligations (CDOs).  Awareness of the potential in this arcane formula helped power the CDO market to some $4.7 trillion in volume over the course of the housing bubble years of this decade.  As the Wired article explains, the explosive commercial viability of this formula can be explained by its use of a simple sleight of hand.  Rather than modeling out the default correlation implications of pools of thousands upon thousands of individual mortgage obligations – an extremely complex undertaking requiring powerful algorithms and massively robust computational processing technology – the CDO market’s Wall Street practitioners used a shortcut that appeared elegant but proved deadly: using the market price of credit default swaps (CDSs) as a proxy for the actual historical data.

What happened in essence was that the CDO market ran up against one of the most challenging of quantitative modeling problems: the dimensionality curse.  This refers to what happens in complex environments where numerous variables interact with each other and all of the resulting combinatorial possibilities influence the economic value.  The addition of an incremental variable to the pool exerts an exponential effect on the number of possible outcomes.  Think of a simple case: if you have a pool of two variables then the number of potential outcomes is four: add a third dimension (variable) to the mix and the potential outcomes expand to nine, and so on.  In an environment like pools of thousands of mortgage obligations or credit card receivables influenced by a bevy of macro- and micro-economic, behavioral, seasonal and other random factors there are literally billions of combinatorial outcomes that could affect the incidence, magnitude  and frequency of default events and hence the price of the CDOs whose economic value derives from those pools.   Getting to the right answers – and doing so with enough speed to satisfy the blistering pace of 24-7 investment markets every day – is a daunting challenge to say the least.  So when Daniel X. Li, a quantitative analyst at JPMorgan Chase, posited the use of CDS prices as a proxy for historical data in a 2000 paper published in the Journal of Fixed Income Securities, the CDO market rejoiced and basically punted away the dimensionality curse by using this shortcut.  The reasoning and the assumptions employed proved to be flawed and the disastrous results are entirely visible to the naked eye in all their graphic detail.

In quantitative methods as in life there are no free lunches.  You can’t simply punt away the dimensionality curse – you have to embrace it and try to achieve mastery over it using all the knowledge and technology tools at your disposal.  At Sentrana we deal with dimensionality curse problems every day – the demand markets for the products and services our clients sell are highly complex environments: tens of thousands of products for thousands of customers in hundreds of locations reachable by any number of marketing vehicles and sales channels.  Modeling these environments is not for the faint-hearted: but the problems are not impossible.  The computational technology does exist, as does the modeling science.  The critical ingredient is the will and determination of those who practice quantitative methods in business to forego the easy outs and stay focused on solving the real problems, however daunting.

Perhaps the field of quantitative methods needs a variation of the medical profession’s Hippocratic Oath: First of all, do no harm.  Clearly the Wall Street experiment egregiously failed that standard.  Let’s hope that the next time some arcane mathematical formula figures into the cultural Zeitgeist it will be for better, not for worse.

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If Price is Your Most Valuable Asset, Why Put it out There for Everyone to See?

Of all the intellectual property your organization possesses, nothing is more important than your prices.  But, unlike all of your other intellectual property, which you protect with impenetrable secrecy (i.e., the recipe for Coca-Cola, the manufacturing process of an Intel microprocessor, the not-so-open source code for Microsoft Office, etc.), you indiscriminately broadcast your prices to the market and lay it bear for all to see.  Yet, there is so much proprietary knowledge echoed in this single price, and you essentially give this knowledge away for free to your competitors.

A single price captures everything that makes you special.  It embodies the value the market sees in your product, the value of your product in this particular season, the value your brand wields in the marketplace, the degree to which your product satisfies the needs of specific customer segments, the degree to which buyers are willing to pay for your reputation, the degree to which buyers are loyal to your product despite competing products, etc.

Once you reveal your prices to the world, your competitors instantly know how much brand equity you have, they immediately see how much value your product has in this particular season, they immediately see your reputation is strong, they are able to assess the amount of loyalty you command, and so forth.  By putting your prices out there for all to see, you implicitly give your competitors a leg-up.  To compete against you, all they need to do is see your price and shoot for something just a tad lower.

What would a future world look like where you only… Read the rest of this entry »

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