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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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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Is Pricing a Core Competency?

I’m not sure whether or not Tom Peters actually coined the term “core competency”, but it certainly took firm root in the business world following publication of his Ur-management tome In Search of Excellence: Lessons from America’s Best Run Companies back in 1982.  The equity bull market that started that same year may have run its course, but core competencies are still with us.  A core competency is supposed to be a unique configuration of intelligence, skills, experience, processes, systems – the things that enable a company to do something really, really well, that are hard for others to replicate and therefore lead to an enduring competitive advantage.   In the business world “advantage” is achieved through profitability, and profitability is achieved through doing things that lead to higher revenues and lower costs.  And the strongest lever the company – any company – has at its disposal to shape its profit line is price.  Given this rather widely understood fact, would not it be logical to assume that a large number of our best-run companies manage pricing as a core competency? Logical, perhaps – but the evidence seems to indicate otherwise.

For all its impact on the bottom line pricing often seems to be more on the periphery of the activity flow than at the center – an outer rather than a core competency, and sometimes not much more than an afterthought – oh, yeah, we need to stick a price on that now… hmmm, let’s see.   There are three commonly-used methods for firms to price their goods and services, and none of them could be considered the basis for a core competency.   The Old Faithful of pricing methodologies is cost-plus: add up a bunch of direct and indirect costs, slap an arbitrary profit margin on top and voila – that’s the price. Then there’s competitor-based pricing, which many people seem to think is several evolutionary legs up from cost-plus but which Michael Douglas’ Wall Street character Gordon Gekko might have called “a dog with a different set of fleas”.  Why should either bean counters in the accounting department or your competitors be the metronome for what you charge your customers?

The third common pricing method perhaps comes closer to hitting the mark, but it still falls short of a core competency.  In fact it is not really a method per se but more an amalgam of several things – gut instinct, trial and error and maybe some back-of-the-envelope elasticity calculations .  Here the intention is right – try to figure out what motivates a customer to want to pay a certain price and then try to meet it – but the delivery is weak.  Even mid-sized companies in retail or distribution businesses face literally millions upon millions of pricing decisions every day – what product to what customer in what location via what marketing message and selling channel?  The permutations are too staggering to handle in any way other than with technology-aided, systematic rigor.  For a long time the tools did not exist to facilitate this – but as more companies become aware of the tools and best-in-class practices evolve (to borrow another one of those indispensable bons mots from Tom Peters) I expect that we’ll see some migration of the pricing discipline from the periphery to the core.

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The Price You Pay for Not Changing Price

The WSJ ran a story on 3/10/09 on the financial success of McDonald’s Corp. throughout the present recession. Since the company is one of only two DJIA members (the other being Wal-Mart Stores, Inc.) to have ended 2008 by posting a gain for the year, it is perhaps only fitting that the Journal devote a few inches to McDonald’s. The only student to pass a difficult exam rightly deserves a gold star. But amidst the discussion of McDonald’s zeal for succession planning, controlled expansion and keeping a lid on costs in the face of the last year’s commodity price swings, one item deserves more attention than it received: McDonald’s is encouraging individual locations to experiment with prices.

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

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

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