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.

To answer this question, let’s take a closer look at the solution itself. Dynamic pricing is a form of yield management (also called targeted pricing, flexible pricing, tailored pricing or discriminatory pricing), which formally emerged in the mid-1980s as a means for airlines to capture some value from plane seats that would otherwise go empty by offering, for example, lower than published fares for customers willing to forego other benefits (such as the ability to change a flight date or cancel the ticket). This breakthrough science allows organizations to understand, anticipate and influence consumer behavior in order to maximize revenue or profits from a fixed, perishable resource (e.g. airline seats, hotel room reservations, etc.). In the case of the San Francisco Giants, dynamic pricing is being implemented to allow them to dynamically adjust prices by weighing ticket sales data, weather forecasts, upcoming pitching matchups and other variables to help decide whether the team should raise or lower prices right up until the day of the game.

The problem with dynamic pricing is that it doesn’t enable organizations to truly understand the needs, preferences and spending propensities of each and every customer they serve. For example, the problem I see with dynamic pricing for baseball franchises is that it relies on a basic set of variables (e.g. weather, starting lineup, etc.) to determine how to price to the masses, instead of focusing on – and pricing to – each customer’s specific needs. Let’s say I want to go to a baseball game on my birthday. Will the dynamic pricing system offer me a discounted ticket (or should it predict that I am more spendthrift on my birthday)? If my favorite pitcher is starting will the system recognize my willingness to pay more and increase my ticket price? If I regularly attend games throughout the season will the system consider my loyalty and offer me discounts to other games? The respective answers are no, no and no. The advantage here clearly goes to scalpers, as they can still adjust and negotiate prices with each customer they interact with directly. However, where I see the limitations of dynamic pricing end, the benefit of revenue optimization begins.

Revenue optimization technology can provide baseball franchises an accurate picture of demand down to the single customer-level, where the software can codify each customer’s preferences and adjust prices according to their needs, total amount spent and even longevity as a fan (i.e. brand loyalty). Baseball teams already capture tons of customer data through the MLB web portal, where fans can upload and track their favorite teams/players and purchase tickets and merchandise. All of this data can be mined to figure out each customer’s specific price point for every seat of every game! The technology enables baseball franchises to increase ticket sales volume for less popular games, reduce the number of tickets resold in the secondary market, and increase profits for every game. In addition, baseball teams can begin to cross-sell other items like concessions and merchandise to these loyal fans, or even optimize the sale of bundled tickets and/or merchandise. With this increased ability to effectively market to each fan, baseball franchises will become more adept at selling tickets than the scalpers and can soon “steal” their profits back – forcing scalpers to buy tickets if they want to see Major League Baseball’s most prolific stealers.