Marketers have long relied on willingness to pay as a way to gauge consumer preference for a product, and rightly so. At the height of the Cabbage Patch Kids doll craze in the 1980s, sales passed the $600 million mark, according to Bloomberg News. Now the toy line has an estimated revenue of $50 million a year, indicating a much lower consumer preference. But new research from Alice Moon, Wharton professor of operations, information and decisions, shows that willingness to pay isn’t always a clear indicator of preference. The paper is titled, “The Uncertain Value of Uncertainty: When Consumers are Unwilling to Pay for What They Like,” and was coauthored with Leif D. Nelson from the University of California, Berkeley. She spoke to Knowledge at Wharton about other factors that should be taken into consideration when marketers are trying to price their products.

An edited transcript of the conversation follows.

Knowledge at Wharton: Tell us about your research.

Alice Moon: One of the most critical issues for marketers is how to forecast consumer product interest and consumer preference. One way they frequently do this is by asking consumers how much they’re willing to pay as a measure of their interest in, or value for, that product. I study when that measure insufficiently captures how much a consumer values that product. I find that willingness to pay is informed by many factors, such as what price they think the market is setting for this product. Sometimes those types of factors overshadow the part of willingness to pay that signals preference. Because of that, when you’re trying to understand people’s preferences by looking at how much people are willing to pay for products, you’ll make the wrong assumption about how much they like it.

“[When] you’re trying to understand people’s preferences by looking at how much people are willing to pay for products, you’ll make the wrong assumption about how much they like it.”

Knowledge at Wharton: If companies are not looking at willingness to pay, what are other ways to figure out product pricing?

Moon: One of the things that they can use is simple rating scales. People think those types of rating scales maybe won’t translate to dollar amounts or preference. Although there is some truth to that, if you’re actually trying to understand consumer preference, you’re better off using a rating scale because it gets more at whether people like the product.

Knowledge at Wharton: It’s not just about how much a consumer would pay, it’s about how much they would enjoy it. Is that correct?

Moon: Exactly. We’re finding that even though you think of these two measures as both being able to capture consumer preferences, enjoyment will more likely get at what people feel about that product, whereas willingness to pay is dominated by other factors, such as what price the market is setting for that product.

Knowledge at Wharton: When people are looking at price, there are all these factors coming to bear because of the market or their personal situation that are beyond preference.

Moon: It’s definitely difficult to measure preference. When you’re inferring preference from a measure like payment, you have to be careful because there are other inputs that can dominate any signal that you would get from preference. Willingness to pay doesn’t always tell you what you think it does. For example, in my paper I look in particular at uncertainty. I find that consumers are willing to pay less for an uncertain prospect than for its worst possible outcome, while simultaneously expecting to enjoy the uncertain prospect as much as the best possible outcome. If you thought the amount people are willing to pay for a risky product tells you how much they like it, you’d assume that people really hate risk. But people like certain types of risk, and you wouldn’t be able to tell that from their willingness to pay alone.

Knowledge at Wharton: What do you mean by best and worst outcomes?

Moon: In a lot of the research, we look at these uncertain lotteries where you could get one outcome or another. One of the examples we use in our paper is a lottery between two different gift cards. This helps illustrate our point because the lottery is between a $50 gift card and a $100 gift card to Barnes & Noble. This is kind of an artificial lottery because it gives you a 50-50 chance at each card, so you will get one of these two outcomes for sure. What we’re finding is people pay less for that type of lottery than even its worst outcome of the certain $50 gift card. At the same time, they will expect to enjoy the lottery as much as its better outcome — in this case, a $100 Barnes & Noble gift card.

“Being aware of the shortcomings of different measures is essential to understanding consumer preferences.”

Knowledge at Wharton: Armed with your research, what can companies do differently to understand consumer preferences?

Moon: I think the idea here is that for companies, depending on which outcomes they care about or inferences they want to make, different measures will be appropriate to use. And being aware of the shortcomings of different measures is essential to understanding consumer preferences. If you are wanting to understand how much people will pay for things, then willingness to pay might be useful. But if you’re interested in how much people like or prefer a product, rating measures are a way to go.

Knowledge at Wharton: What’s next for this research?

Moon: One of the avenues I’m exploring for future research is investigating the right way to measure valuation in different contexts. Our research shows that willingness to pay isn’t always the right measure, but it’s probably not always the wrong one. For example, when trying to estimate people’s actual payments, willingness to pay is probably a great measure to use to approximate that. I’m trying to develop a deeper understanding of what each of these measures are tapping into so that we can make better predictions about consumer preference and valuation.