Our series on value pricing continues with a detailed look at how to set prices that benefit both you and your customers. Now that you understand the risks of other pricing models and how value pricing leverages the value your offering creates for customers to maximize earnings for your company, let’s look at one way to actually calculate the optimal price and the data you would need to populate the formula.
The goal of pricing should be to maximize the earnings impact of your offering on your business. In a perfect world, it can be calculated based upon a set of variables over time. If you have taken any calculus, or at least some algebra, it is theoretically possible to figure it out.
Keep in mind that many of these variables are difficult to estimate, so it’s not always practical to use this formula. You will rarely have the perfect information to enter into the equation, but that shouldn’t stop you from trying. What’s important is that you understand why these variables are important, and how they factor into value pricing decisions.
Grappling with Value Pricing Variables
Settle in with your team and start discussing the inputs that go into the equation. Although it’s difficult and may be even a little overwhelming, it’s your responsibility. Taking shortcuts is sometimes successful in the short term but to succeed in the long run, you need to monitor these variables all the time.

The price of the next best alternative: This is often a competitor, but it could be any alternative way that the customer can solve the problem. If their next best alternative is to continue doing what they are doing today, then the price of that alternative is $0.

The value that you create for the customer: Quantify value in terms of cost savings, improved efficiency, sales growth, and any other way that your offering can impact your customer’s business.

The value that the next best alternative creates for the customer: This is crucial. You can’t price to capture more than the incremental value you create for your customer compared to their next best alternative.

The size of the market: How big is the total market in terms of value created across all potential customers?

The market share that you expect to achieve at a given price: This is dependent on your market reach, your competitor’s likely response, and your customer’s price sensitivity.

How long the offering will be in the market: What is the expected life of this offering in years?

The profitability you are already receiving from customers that could buy this offering: Finally, you need to take into account how much current profit could be at risk into those same customers. If you’re launching a new offering in a market where you’re already making a lot of money from those customers, you need to understand how your pricing decisions on the new offering could impact their buying decision on your other offerings.
As you work through these factors, keep in mind that they all change over time. Your goal isn’t to maximize profitability for a single year, but over the life of the offering.
Now let’s add another level for consideration by pointing out that many of these variables are interdependent. For example, the price you charge could impact the price of the next best alternative. And the value you create over time can change based on other alternatives. How long you can maintain your value and price depends on your ability to protect your intellectual property. Bottom line, there are a lot of factors that go into calculating your optimal price.
An Equation for the Ages
In a nutshell, here’s what the value pricing equation accomplishes:
 It sets a price to maximize profitability over N years,
 based on setting a price (which can vary over time) that captures a portion of the value delivered relative to the next best alternative, and
 achieves a share of demand that delivers marginal contribution to the business when considering any product line cross interactions.
If you think that was rough, here’s what it looks like using mathematical symbols:
Maximize (y=1 to N Profit = ∑(Pn(y) + V(y) * Cv(y)  CoGS(y)) * M(y) * Sd(y) + Eo(y))
And here’s how to interpret the variables in the equation where “y” represents year:
N 
The life of the offering 
Pn 
Price of the Next Best Alternative in each year 
V 
Value your offering creates for the customer in each year relative to the Next Best Alternative 
Cv 
Percentage (%) of the value that is captured in the price (this is what you are solving for) 
M 
Unitbased market size in each year 
Sd 
Share of demand in each year (%) 
Eo 
Earnings impact on other offerings based on the price in each year 
Then you solve for Cv by year to maximize profit. Simple, right?
The problem is that all of the variables are interdependent on both the price (share of value that you choose to capture) and each other. For instance, choosing a high percentage of value to capture (and thus a high price) could encourage new competing offerings (including customers choosing to do it themselves), which will in turn lower your share of demand and potentially the value that your offering creates vs. the next best alternative.
And, if you are selling other things to the same customers, that high price could cause those customers to stop buying other things from you, thus lowering the profit received from your other offerings.
In reality, these factors are virtually impossible to know with any level of confidence. But if you don’t try to estimate the likely impact of each of them for various pricing decisions, you are likely to suboptimize the earnings that your company will gain from your offering.
Conclusion
Is it any wonder why most people opt for simpler pricing strategies? Determining a value price is complicated, but not impossible. And the good news is that you don’t really have to use this formula and all of its unreliable factors to determine a price that’s closer to optimal than other methods.
So put away your calculus books and stay tuned. Our next blog post will provide practical tips and methods for considering the impact and likely market response to your value pricing decisions.