In a recent HBR article, “Linear Thinking in a Non-Linear World,” the authors explain how our minds are naturally biased to assume every relationship between two variables is linear. We are trained to think that the impact on Y with a change in x will be in a fixed/constant proportion.
Often, this is not the case and can lead to unintended consequences, especially with pricing decisions.
We know that price has a great impact on profitability, yet executives often focus on volumes and costs to meet organizational goals and objectives. There is a commonly held fallacy in B2B management that reducing prices will significantly bring in enough volume to make up for the lower price.
In practice, its usually the margin (price minus incremental cost) that contributes to profits, the increase in volume required for a price decrease to breakeven can be astronomical for goods with low-contribution margin. And since we often deal with derived demand in B2B, that volume may be hard to come by, or worse, the price decrease could start a price war.
In a previous role at a large industrial tool manufacturer, every end of quarter management enacted promotions and discounts to get inventory moving and adding dollars to the top line. The executives operated with the same linear bias; so long as they add to the top line they assumed overall profitability would follow. This strategy backfired. During one price promotion, the company doubled volume from the previous year but profitability was down by 25%.
The implications of strategies related to linear bias are huge in the pricing world:
- In many B2B markets, demand is inelastic; hence reducing prices leads to a drop in revenue
- End of quarter discounts and promotions train your customers to delay purchases
- Once a price reduction is in effect, it is very difficult to raise the price to previous levels
These are just a few examples of how assuming linearity can lead to poor decision making. What challenges have you faced with linear-bias and what ways have you tried to minimize the bias?