Visualizing Price Changes
I came across the following visualization from a pricing leader on LinkedIn. Its purpose is to show how a product’s gross profit (GP%) and a customer’s price sensitivity can suggest different pricing movements and their effect on revenue and gross profit.
Good, clear communication is very valuable. I want to make sure pricing practitioners are getting good information. While there are some things I like about this visualization, there are others that are important to highlight.
What Does This Get Right?
Relationship Between Margin, Elasticity, and the Effect of Price Changes
The core relationship shown by this visualization is a good one to recognize: changes in price have asymmetric effects depending on characteristics of the product and customer in question.
In one sense, this visualization is an illustrated summary of the volume hurdle calculation:
Q = Quantity
P = Price
CMi = Initial contribution margin
With this simple tool, we can find the breakeven quantity change required to justify a change in price in terms of profitability. (For more information, see the Volume Hurdle section in Pricing Strategy.)
One of the takeaways of this equation is that products with differing margins require differing levels of quantity change. This is also what the horizontal axis of the visualization shows. To this, the visualization has added price sensitivity (or elasticity) as a vertical axis.
Low Price Sensitivity Suggests Price Increases
If customers are not very sensitive to price, then they are the least likely to object to a price increase. This is somewhat tautological, but still worth pointing out. Such customers are illustrated by the bottom-most row, which is all green.
Compare that to the case of highly sensitive customers in the top-most row, where the option is instead for price decreases. Highly sensitive customers are the most elastic and therefore the most likely to increase their buying with a price decrease.
Recalling the volume hurdle, we know that high-margin products have more wiggle room to decrease price profitably, which the visualization illustrates with the curving orange zone in the upper right.
Outside of the green and orange extremes, there are tradeoffs between profitability and revenue. How to navigate that tradeoff is beyond the scope of the visualization. That is simply a limitation of the format and a concession to the individual and specific nature of such issues in the real world.
There are, however, several more serious limitations in this visualization. It is important to highlight these so that users are not led astray with a false sense of certainty.
Photo by Akshay Chauhan on Unsplash
What Is Missing?
Price Sensitivity is Known
While price sensitivity and elasticity are often the first pricing concepts that an MBA student learns, they are notoriously difficult to determine outside of the classroom. This is especially true for industries where sales are relatively large and intermittent.
Analytical software can help, but the information comes at a substantial cost of time and money. Such informational cost, due to reasons we will turn to now, can often outweigh the benefit of said information in the first place.
Price Sensitivity is Uniform
Price sensitivity in this visualization is a single number. However, sensitivity, even when known, is not a uniform number. Fundamentally, it varies depending on the price range under consideration.
Furthermore, sensitivity depends on the specifics of the customer in question. Customer characteristics affect price sensitivity, but so do the nature of the sales engagement and relationship. Elasticities between distribution and the end customer vary for the same product.
Averages between customers can be somewhat useful but can also disguise materially important differences and therefore lead executives in the wrong direction.
Price Sensitivity is Static
Price sensitivity isn’t static. Many marketing and sales initiatives can affect a customer’s price sensitivity over time.
For just a few examples, marketing communication that relies on price and discounting over defending value increases price sensitivity. Having a transactional relationship with the customer can have the same effect. Conversely, branding that emphasizes value can decrease price sensitivity.
Sensitivity isn’t an immutable number. Executives can take actions that decrease it to their company’s advantage.
No Competitive Reaction
Taken literally, this visualization suggests that companies exist in isolation. As we all know, this is far from the truth. We all operate in industries with multiple and various forms of competition.
A price move by one market participant does not go unnoticed by the competition. What your company does in pricing is contingent, in part, on the anticipated reaction of other parties. And the appropriate way to react to a competitor’s price change depends on your relative position in the industry in terms of pricing power and competitive advantage.
Without taking these critical factors into account, any executive managing a price change is flying blind.
Gross Profit % is Static
While gross profit is a common way to categorize products, practitioners know that even over the course of several months the category in which a product falls may change multiple times. Discretion must be used in how different companies in different industries define their margin categories and deal with the edge cases.
Of course, there is a feedback loop here too between gross profit and prices. Increasing the price of a 39% GP product in January may kick it into the “Medium” category for July. A pricing strategy that treats the product in July as categorically different than in January, because of that strategy’s own action, isn’t a very coherent strategy!
Snapshot in Time
The omission of any competitive reaction and both changing price sensitivity and gross profit highlight that this is a visualization lost in time.
There is no sense of how repeated interactions with customers can shift their perceptions of your products and services. Neither is there acknowledgement of what we know from game theory, which is that your strategy must differ when you are facing repeat interactions with both customers and competition.
That is an issue with simple pricing analysis in general. While such analysis can be a useful input, it is not sufficient for good decision-making and strategy.