In many years of working with business teams, I have seen them spend countless hours making their pricing analyses more and more precise. More and more insights and analytics are demanded, assumptions questioned and tested, and often results are nonetheless doubted in the end. Not to mention that slow action hurts performance in many industries. I propose that the solution to this is not intuitive — to get at a higher quality answer, the prescription is ironically to be less precise and flip the analysis around to spend more of the value-added time of your decision-makers actually discussing the current context of the business and its future. Let me explain with an example.
Let’s assume we manufacture cars. We have a high-end offering priced at $40,000. The margin we earn on each sale is $4,000. Our marketeers are telling us that we are not price-competitive, and that we need to lower price to $38,000. Costs will not change, so our margin would change to $2,000 per vehicle. What’s the right decision here?
The team can spend a lot of time debating what the 5% price drop will mean to sales volume. Hopefully it goes up with the price reduction (does not always happen…that’s a story for another day!). Let’s say some on the team argue volume will increase at least 25%. Others may be more dubious and expect something like 10%. How do we decide? More studies of price elasticity of demand? Conduct a survey? Hire a consultant to help us draw the demand curve? I would advocate for a simpler, faster approach. Instead of determining or pinpointing the new resulting total margin, let’s instead focus on what the decision criteria for this pricing change should be — in this case, assume we want net margin ($, not %) to improve. Since margin is 50% less at the lower price, in order to improve margin, we need to double unit sales. In many cases, as in this one, no one was arguing that would happen. The debate was in the 10–25% range. We just made our decision without any need for “further study” or time-consuming and expensive analytical exercises.
Here is a summary of what we just discussed.
I recognize that, in many cases, management will argue that driving revenue is more important even at the expense of a loss of net margin. Maybe it is to gain market share (which hopefully translates into long-term margin improvement). Maybe it is to “get more of our cars on the road” and hence to drive visibility of our brand. Whatever the reason, decide what your criteria are and focus on the decision, not haggling over the precise result or assumptions.
Note that we have finessed over the issue of fixed costs. In many cases, we have relatively fixed costs for certain items. For example, we don’t need to build a new factory if volume increases only 10%, and then in effect we can “spread” the fixed costs over more units. Or we don’t need to change overhead function staff levels just because volume fluctuates at the margin. That complexity does not break this approach; in fact, it simplifies it by making the analysis much more straightforward than if done in a more traditional way.
What if management wanted to look at raising prices? A similar discussion would follow to answer the question of whether we achieved the required minimum volume change to justify the decision. In this case, it turns out that any volume decrease of less than 33% meets the criterion. In other words, if volume “only” drops by 20% with the price increase, the company makes more money by indeed raising the price. If volume instead drops by 40%, the company earns less total margin with the price change.
Regardless of the specific criteria used, see what we are doing here? We are having our business leaders spend time discussing the market rather than anyone’s opinion about the demand curve (which is often ultimately unknowable with any certainty). We are also able to move much faster. I would argue this is even more valuable and important in fast-moving industries where prices can be tested. It’s harder to drop the price of a car because the decision is not easily reversible, but it is easier, for example, in digital spaces where individual, unique offers can be made to consumers.
What can management then spend time discussing instead? How about why our margins are only 10%? Is that good in this high-end market? What can we do to lower costs? Should we price in a more premium fashion? We learn a lot about what our cost structure means for our pricing flexibility when we look at it this way.
A Multi-Price Presentation
What if we want to present a range or menu of pricing options? After all, we rarely are just evaluating in a binary fashion a single price point like we did above to arrive at a yes/no answer. We can walk through every potential price point and analyze the impact to margin and discuss what we think the demand curve looks like at each point. But there is a radically simpler way to visualize the pricing outcomes across a range of price points.
Let’s look at our series of prices to be analyzed and in each case determine the required minimum volume change.
Now we can look anywhere along the curve and ask the much simpler question: at that price, would the volume increase be above or below the line?
For a future installment of this discussion, we can look at a more complicated example — where we are considering offering a new product, a lower-end option for $30,000. We would source less expensive parts so that the margin could still be positive. Management is faced with the product decision. We will need to consider what happens to demand for our premium product (i.e. the extent to which consumers substitute away from our premium product to our new product) and what levels of demand change we can tolerate there.