The Why Axis: Hidden Motives and the Undiscovered Economics of Everyday Life (33 page)

Once Humana switched to being a benefits provider and McCallister became CEO, he began experimenting with other policies. As an employer, Humana found that its own healthcare costs were out of control, in part because employees weren’t taking care of their own health. McCallister is a big believer in personal responsibility,
so he told his employees they weren’t going to be told what to do. Employees had to work on the problem together. One approach was to run little incentivized experiments. Humana offered a weight-loss program that began and ended with a BMI (body mass index) measurement. Those who lost some of their girth had their names entered in a lottery for a hefty check for $10,000. Not surprisingly, this incentive created quite a bit of buzz around the firm—and, yes, some people lost weight.

The weight-loss experiment is a small one; but consider a large-scale experiment Humana is running today. Although McCallister believes all people should have access to affordable healthcare, he recognizes that the Medicare bureaucracy has very little incentive to invest in preventive care. This, McCallister says, leads to “fraud, abuse, and overuse of services.” In the face of a huge generation of rapidly aging baby boomers and ballooning healthcare costs, he thinks there’s a much better way of delivering patient care—one that focuses on patient wellness, which he believes saves both money and lives.

To that end, the company recently adopted a mantra: help people achieve lifelong well-being. But what works? To find out, he hired a consultant named Judi Israel to build a “behavioral economics consortium.” As part of this consortium, we helped design some field experiments and behavioral interventions. Our common goal was to see what kinds of interventions best helped patients improve or stabilize their health while managing costs.

For example, consider a senior citizen on Medicare who suffers a heart attack. She survives the attack, receives appropriate treatment, and goes home. But then she ends up back in the hospital within a month for some comparatively trivial issue, such as failing to take her prescribed medications. Each hospital readmission averages a $10,000 cost, not including “extras” such as prescriptions, rehabilitative services, and so on. Given that a whopping one in
five patients on Medicare is readmitted to the hospital within a month of his or her first admission,
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these costs can be massive—and readmission is no fun for the patient, either. Humana, which covers the costs Medicare doesn’t, has a vested interest in addressing the situation.

So the firm did a little poking around in its databases and discovered that a substantial number of the two million Medicare-enrolled members it insures were being readmitted. The company chartered its analytic team to build a model to address this problem. Among other insights, the team found that members who suffered from chronic health problems (diabetes, obesity, heart disease, pneumonia, congestive heart failure, and so on) were at the top of the list. Accordingly, Humana made a point of following up with patients after they were released from the hospital. All patients receive an automated phone call offering help or advice via a toll-free number, but patients with chronic problems receive a call from a nurse who walks them through the steps of their rehabilitative care and makes sure they stay on track. And patients who suffer from several chronic problems at once receive a home visit from a nurse who monitors and coaches them along. More than 100,000 Humana Medicare members with multiple chronic illnesses receive this kind of help.

Through controlled tests, Humana has discovered that a proactive, low-cost, and simple intervention, such as sending a nurse to visit the patient, can save significant amounts of money while helping the patient. We continue to work with Humana using simple behavioral interventions that we trust will make significant bottom line advances.

From a business and healthcare industry standpoint, these moves all make sense. “Our industry has not been innovative,” McCallister insists. “This nation is productive on the back of technology, but there is no innovation in insurance or healthcare outside of
products. We are trying to solve a big problem—to control healthcare spending and address deteriorating health at the same time. Maybe what we learn from our experiments here can spread.”

The Price Is Right

Field experiments focusing on products, services, and prices are not just the domain of big companies such as Intuit and Humana. They may, in fact, be even more crucial for smaller businesses, many of which teeter on the brink of bankruptcy daily.

In the summer of 2009, Uri and his wife Ayelet received a call from a fellow we will call “George,” a winery owner in Temecula, California, a lovely, languid town about an hour northeast of San Diego. George asked for their help with pricing his wines—clearly one of the most important business decisions he needed to make. They were delighted to take up the invitation to visit George’s winery, taste some of his products, and possibly help him in the process.
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When Uri and Ayelet asked him how he’d chosen prices in the past, they heard about the usual suspects: George looked at how other wineries price similar wines, intuition, his last year’s prices, and so on. He expected the business professors to come over, look around, do some quick calculations—and come up with the magic numbers that would make him rich. You can imagine how disappointed he was when, after having spent some time with him (and his lovely cabernet), Uri and Ayelet told him they had no idea what the “right” price was, and that the magic number didn’t exist. He almost took away the wine he’d already poured for them.

In an attempt to save their drinks, Uri and Ayelet did offer him help, in the form of a method—no magic, no equations, and no superior knowledge—just a simple experimental design. Pricing wines is a particularly tricky task since quality is not objective. We automatically assume a connection between price and quality; all
else being equal, if a laptop costs more because it weighs less, people think it’s better. And that’s how much of the world works—evidence that runs counter to this basic intuition is hard to find.

Is this also the case with wines? You’d assume so, since the price range for wines is so enormous—you can pay a few bucks for a bottle of rotgut, or $10,000 for a bottle of 1959 Domaine de la Romanée-Conti. Research suggests that even when evaluating the quality of a product is subjective (as is the case with wine, since people have different taste preferences), increasing its price may increases its attractiveness to consumers.

Visitors to George’s winery, as with other wineries in this region, can taste different wines and subsequently choose to buy from the selection. Consumers typically come to Temecula for wine trips, going from one winery to another, sampling, and buying wine. The wine with which Uri and Ayelet experimented was a 2005 cabernet sauvignon, a “wine with complex notes of blueberry, black currant liqueur, and a hint of citrus.” The price George had previously chosen for it was $10, and it sold well.

For the experiment, we manipulated the price of the cabernet to be $10, $20, or $40 on different days over the course of a few weeks. Each experimental day, George greeted the visitors and told them about the tasting. Then visitors went to the counter, where they met the person who administered the tasting and handed them a single printed page containing the names and prices of the nine sample wines, ranging from $8 to $60, of which visitors could try six of their choice. As in most wineries, the list was constructed from “light to heavy,” starting with white wines, moving to red wines, and concluding with dessert wines. Visitors typically chose wines going down the list, and the cabernet sauvignon was always number seven. Tastings took between fifteen and thirty minutes, after which visitors could decide whether to buy any of the wines.

The results shocked George. Visitors were almost 50 percent more likely to buy the cabernet when he priced it at $20 than when he priced it at $10! That is, when we increased the price, the wine became more popular.

Using an almost cost-free experiment, and adopting prices accordingly, George increased the winery’s total profits by 11 percent. Following this experiment, he happily adopted the results and changed the price of this wine to $20. Since the vast majority of the winery’s clients are one-time visitors (this winery sells most of its wines in its store), very few people noticed the change in price.

Be Creative

Finding the “right” price is important. But sometimes you need more. It’s not just about the price, but also about how it’s collected.

A few years ago, a graduate student at University of California, San Diego, Amber Brown, went to work for Disney Research—a to-die-for kind of job for a young psychologist. Disney has an in-house, interdisciplinary group of researchers that uses science to try to improve the company’s performance and explore new technologies, marketing, and economics. As is the case with Humana, this group understands the importance of using behavioral research to simultaneously improve both the customers’ experience and the company’s bottom line.

At about the same time Amber nabbed her job, we were becoming interested in an emerging behavioral pricing approach: pay-what-you-want. A famous example of this pricing is from the British band Radiohead. In 2007, the band released a CD as a digital download. It encouraged fans to log on to its website and download the album for any price they chose. Fans could get the album for free or pay as little as 65 cents (the cost of handling by
the credit card company) or more. But would the fans pay for something they could get for free? And did they pay? Interestingly, hundreds of thousands of people downloaded the album from the band’s website, and many of them (around 50 percent) paid something for the CD. (By the way, as our friend, the recent Nobelist Al Roth likes to say, “Columbus wasn’t the first to discover America; he was the last.” After Columbus, everyone knew about the “new” continent. The same is true here. Radiohead wasn’t the first to discover this pricing strategy, but the group is famous enough to be the “last”—no one will ever need to “rediscover” it.)

This example shows that even in markets, people are not completely selfish. But the data from Radiohead’s model, and other companies who had used it, left many questions open. Clearly people paid more than they had to, but whether the pricing strategy had positive or negative consequences for the band remained unclear. Did the band make or lose money relative to a standard pricing scheme?

We decided to study the pay-what-you-want scheme in a field experiment.
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We thought a combination of a pay-what-you-want pricing strategy
and
charity might be an interesting way to go. We called this combination Shared Social Responsibility (SSR) because instead of the company alone deciding how much to give to the charity, customers could share in the donations, too. If people could pay what they wanted for an item, would they pay more if we appealed to the “better angels of their nature”?

So together with Disney Research, we designed a large field experiment that included over 100,000 participants to test the effect of pay-what-you-want pricing combined with charity. We set up our experiment at a roller coaster–like ride at a Disney park where people go on the ride and can afterwards buy a snapshot of themselves screaming and laughing.

We offered the photo either for its regular price of $12.95 or under a pay-what-you-want scheme. We also added treatments in which half of the revenue from selling the picture went to a well-known and well-liked charity. This experimental design resulted in four different treatments that we ran over different days during a month-long period.

The figure below shows the profits per rider:

As you can see, we found that at the standard fixed price of $12.95, the charitable component only slightly increased demand—raising the revenue per rider by just a few cents. But what happened when participants could choose their own price? The
demand rates went through the roof. Sixteen times more people (8 percent instead of 0.5 percent) bought the photo. But since they only paid about a dollar on average, Disney didn’t make any money from them. (Remember: we are interested in running experiments in which we can find a win-win solution for both companies and their customers. That’s the best way to make changes that stick.)

And what, in the experiment results, were we most interested in? When we mixed the pay-what-you-want scheme with charity, 4 percent of the people bought the picture, but they paid much more (roughly $5) for it. Adding the charity option proved very profitable. In fact, the amusement park stood to make an additional $600,000 a year by offering the pay-what-you-want/charity combination just in this one location in the park. More generally, making this change also increased the benefit to the charity—and presumably to the customers, who felt they were doing something good.