Economic theory postulates that humans are rational beings who always make optimal decisions. In other words, the theory assumes that humans are not human. While economic theory often works well in describing real world behavior, the times when it falls short can have a huge impact. This post discusses the psychological reasons your proposal prices are (almost certainly) below the economically optimal level.
Many of the examples in this post come from a great book called Thinking, Fast and Slow, by Daniel Kahneman. I highly recommend the book, not just for business reasons– I actually started reading it just out of curiosity, and only later realized how applicable it is to what I do in the office. I’ve noted the examples from the book with an asterisk (*).
We assume that as prices go up, demand goes down. In many cases, this is right, although it’s not necessarily so simple. In many cases, businesses are wary of prices that seem to cheap. They cast doubt on the expertise of the provider. After all, you wouldn’t want a heart surgeon who advertised “Open Heart Surgery $9.99”.
Let’s assume for now that you’re in the right ballpark. Let’s also assume that you know your competitor has priced their proposal at $100,000. You feel you are offering at least $125,000 worth of value, but you’re nervous about pricing higher than your competitor. Do you price at:
- $125,000, because, hey, you’re worth it
- $110,000, because you know you’re worth more, but you don’t want to be greedy
- $100,000, just to take price out of the equation
- $90,000, to give you a price advantage
(Naturally, there are other ways to play, but to keep the discussion simple, we’ll focus on these options.)
There are a few psychological and economic factors at play. One of the most important is who owns the decision. If it’s a sales rep on commission, the difference between closing a deal at $100,000 and $125,000 might be $1,250, while the difference between winning at $100,000 and losing is $5,000 (depending on commission rates, obviously). Especially if the rep has to spend a lot of extra time to close the deal, it may not even be in their economic interest to pursue higher prices.
Let’s assume that the business owner is the one in charge of the pricing decision, if not the entire sales process. Therefore, 100% of the extra windfall will go to the business (or the owner). Let’s assume further that it will cost $75,000 to deliver the deal and support the some portion of company overhead. Let’s also assume that the company does about 10 of these deals a year, and loses bids on an additional 10. You can plug in whatever numbers best fit your business– these numbers just help keep the math easy to follow.
The default reference price– both for you and the buyer, is the $100,000 bid from your competitor. Every dollar you gain above that feels like a win, and every dollar below that feels like a loss. Winning feels great, but not as intensely as losing feels bad. In fact, psychological research suggests that losing feels about twice as bad as winning feels good. In economic terms, that means we have “loss aversion.” *
Loss aversion leads to economically counterintuitive preferences. We would generally rather take a bet with a 100% chance of winning $100 than a bet with a 75% chance of winning $150 and a 25% of winning $0. Even though the first option has a lower expected value, the benefit of the possible extra $50 seems less than the potential loss of $100 compared to the sure thing. Loss aversion is a powerful motivator– golfers putt better to save par than to make birdie.* Sales reps often invest more effort trying to save a “bad” deal than to make a good deal extraordinary. Loss aversion is a useful evolutionary development– its inherent conservatism helped our ancestors live and breed another day.
Yet this conservatism can be costly. Let’s just pick between the $125,000 and $100,000 options. To make the math more realistic, let’s also break the $75,000 cost into $25,000 of fixed cost and $50,000 of variable cost. Let’s say you win 50% of deals at $100,000 and 40% at $125,000. Let’s look at both scenarios, assuming 100 proposals per year (just to make the math easy).
|Total Cost ($1,250,000 fixed cost, plus $50,000 variable cost per win)||$3,750,000||$3,250,000|
These scenarios generate the same amount of revenue, but the higher prices yield much more profit, because variable costs are lower. There are a lot of “off the balance sheet” benefits to pricing higher, too. You have fewer customers to support. Fewer status meetings to attend. Fewer late nights at the office. You’re also working for customers who value what you do, where you can make more of a difference.
Perhaps we are being optimistic, however. Let’s lower the win rate for the higher price to 35%.
|Total Cost ($1,250,000 fixed cost, plus $50,000 variable cost per win)||$3,750,000||$3,000,000|
We’re still ahead! At a certain point, of course, we can price ourselves out of the market. Or a competitor may improve their offerings, increasing their value, or lower their costs further, peeling off more price-sensitive customers. But there are huge benefits to delivering and expecting compensation for good value. In fact, in many B2B sales scenarios, increasing price within certain bounds does not diminish your win rate. It can even increase it, as being the higher priced provider often signals quality and reduced risk, in the absence of other objective criteria (we’ll discuss the “Evaluability Hypothesis” in another post*). Clearly, as we get closer to a 50% win rate at the higher price point, our revenue, and more importantly, our profit, vastly outstrips our results at the lower price point.
One natural way to overcome loss aversion is to pool risk. This is the basis of insurance, including those extended warranties that are economically silly to purchase, and are designed to take advantage of loss aversion. Another way to look at risk pooling is to consider not a single proposal in isolation, but your portfolio of deals. If your mind is focused on optimizing a single deal, it’s real hard to avoid loss aversion– and perhaps you shouldn’t, in those circumstances. But optimizing a portfolio of deals leads to a different answer.
Kahneman cites an example of a company retreat with 25 VPs. Each VP is offered the chance to make a risky bet that has a 50% chance of doubling revenue and a 50% chance of failing completely. None of the VPs want to take the bet, because failure means a blemish, or perhaps termination of their career. The CEO wants everyone to take the bet, because the overall outcome for the company will be substantial growth.*
Risk pooling only works properly when the bets are independent. If all your bets are that the housing market will go up forever, you might have a big problem on your hands. If you know that you win 70% of the time when you have a relationship with the decision maker and 30% of the time when you don’t, you need to control for that variable and invest more in building relationships with decision makers. If one industry doesn’t see as much value in your offering, you need to deliver a lower priced, lower cost option to that industry.
Whatever you do, invest in creating a portfolio of bets, a deal pipeline, so your whole business does not depend on a single proposal. That leaves you vulnerable to underpricing due to loss aversion.
(In the next post, we’ll look at how to feel good about value pricing in the context of a single proposal.)