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.

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… Monopoly is played under normal rules – so what was so different about how things turned out in our “recessionary-rules” Monopoly? Well, the first thing to note is that there was no difference at all on these 4 major characteristics of the game. In other words, despite overall lower levels of wealth and the unavailability of credit, we still see that buyers were prepared to pay prices that were above the lowest price stated on property value card and each player was prepared to pay a price different than what other players wanted to pay. Attenuation of wealth and credit did not reduce the price dispersion in this economic system, and in fact revealed that buying behavior did not rest on who provided the lowest prices for which properties. Rather buying behavior, and the prices that transacted, continued to rest on the specific utility or value that each player individually felt they could derive from a given piece of property. Even though the property is the same, each player’s valuation of that property is different (see Graph) – and no amount of wealth erosion or credit crunch could change this fundamental fact of the market.

So, now we come back to what were the differences between what we observed in “recession-rules” versus “normal-rules” Monopoly. First and foremost, purchases took longer. Players waited longer to accrue savings before deciding to purchase any property. Secondly, there was much heavier buyer concentration and interest in trading properties at the low-end of the pricing scale than at the high-end (not too much interest in Boardwalk, but Baltic was hot). Third, there was much more price dispersion for the low-end properties than for the high-end properties in ”recession-rules” Monopoly as opposed to “normal-rules” Monopoly. The key take-away from this is that with many more buyers for the same good, the odds are higher that there will be a broader spectrum of how each buyer values this good and the net result will be a greater dispersion in offer prices. In other words, the market becomes even more segmented for the low-end properties and this gives rise to a wide variation in prices. As a seller holding the property, the question of should I sell now or sell later where a different buyer with a higher valuation comes along is more important for those properties that we can predict will have more price dispersion because of their “average” low price.

Lesson Learned: Look at your prices and your product assortments simultaneously – don’t drop the wrong thing. In other words, don’t drop prices without thinking about dropping your assortments. If you have 10,000 SKU’s, create product bundles and price each bundle optimally and recognize that offering the lowest price in the market does not increase your chances of weathering this economic storm and may in fact lead to self-created demise.

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.