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When the Uber he hired drove him to the wrong destination, a professor took his complaints to the top of the company and learned something valuable about the science behind the apology.
In January 2017, John List was due to deliver an important speech at a prestigious meeting of economists.
He picked up the phone and booked a ride with Uber who took him 30 minutes from home. After briefly seeing the car driving along Lake Shore Drive, a road that circles Lake Michigan in Chicago, USA, he leaned back to prepare his speech.
About 20 minutes later, the teacher looked out the window again thinking he would be close to reaching his destination, but it was exactly where the journey began.
Something had gone wrong with the app and ordered the driver to take the teacher back to his home.
List was understandably furious, but What pissed him off the most was that Uber had never apologized to him.
Not everyone who has formally filed a complaint with Uber has had access to Travis Kalanick, the then CEO of the company, but John List did.
That same afternoon he phoned Kalanick, just before the founder of the company resigned from his post after a series of controversy and pressure from shareholders.
After List shared his experience with Kalanick, Kalanick told him: “What I want to know is how Uber should apologize when this kind of situation arises. What is the best way to build customer loyalty even when they have a bad experience?
How to apologize is a question every business wants to know. And John List was in a unique position to answer this question.
Not many people of John List’s origin become reference scholars.
List grew up in a working-class family northeast of Madison, the capital of the US state of Wisconsin.
His father was a truck driver and he hoped his son would join the family business. But John had other ideas.
His dream was to become a professional golfer and he earned a college scholarship in the sport. There he found that he wasn’t as good at golf as he thought and this the economy fascinated him.
Now, List belongs to the economics faculty of the University of Chicago, one of the best in the North American country.
But he’s also been in the moonlight for a few years now, as Uber contacted him to be his chief economist and, after leaving this company, he joined another car travel app, Lyft, where he holds the same position.
Sure, the job at Uber pays well, but List was designed for several reasons.
For data scholars, car applications are gold mines. In the United States alone, before the pandemic, there were two million Uber drivers making tens of millions of trips a week.
List has spent his career studying economic behavior in the real world, so working for Uber was “a dream come true”.
Having access to so much information, could analyze all kinds of consumer preferences: the type of car preferred, the distance traveled, at what time or how they react to a change in fares. He may also learn the best way to apologize.
His first step was to analyze what happened to Uber users after having a bad experience, something that took much longer than expected.
The app could predict, for example, that a journey would take nine minutes and end up taking 23 minutes. By analyzing the numbers, he and his collaborators found out cyclists who have had a bad trip will spend up to 10% less on Uber in the future.
The next step was to come up with a series of excuses and randomly test them with users who have had a bad experience.
It turns out that there is some kind of science behind excuses, and social scientists and psychologists in particular have studied the types of excuses that work best.
List also had the advantage of being able to measure their impact.
The academician defines a type of apology: the “basic” one, which would contain a message similar to this: “We have seen that your journey took longer than expected and we are deeply sorry.”
A more sophisticated justification involves admitting the company’s fault.
And another type of apology would involve more commitment from the company. For example: “We will try to make sure this doesn’t happen again.”
On behalf of Uber, John List has tried all these guys. Also, with some of these messages, Uber has offered a five dollar discount on the next trip.
In the experiment there was also a group of users who received no apologies.
The result was surprising, as all kinds of excuses were ineffective on their own. However, if they were accompanied by a $ 5 coupon, maintained greater loyalty from users.
“In this way we recover millions of dollars by comforting customers with an apology and a discount coupon.”
But looking closely at the statistics, List realized that this strategy would stop working if a second or third negative experience occurred. Indeed, repeating the apology has been shown to alienate consumers.
This is invaluable knowledge for Uber and other companies.
Many economists sit at their desks and create models from there to make predictions. What’s unusual about John List is that he likes to test his theories in the real world.
The scholar has conducted experiments in various parts of the world, from Tanzania to New Zealand and from China to Bangladesh.
The large amount of data that Uber and other travel applications accumulate has made it possible to identify some peculiarities of human behavior.
For example, before booking a ride with Uber, you never know if a man or a woman will be the driver. Therefore, it is likely that you will assume that men and women earn the same.
However, it turns out that men earn around 7% more than women. This caught List’s attention and she found out why.
One is that women have greater responsibilities when it comes to taking care of children and are therefore less available during the most profitable hours, such as in the morning and after work.
But the most important factor was speed. On average, male drivers travel 2.5% faster than women on Uber, allowing them to make more trips per hour.
This is not the only gender gap.
List convinced Uber’s board of directors to enable the hint function, think this would make drivers happier.
After analyzing the effect of this feature, he found that for every US $ 4 donated by women, men donate US $ 5.
It was also found that female drivers are tipped more than men, except when these women are 65 or older.
The study of human behavior through data collected from car apps has been called Ubernomics, although List has now worked for Lyft and continues to produce fascinating results.
By analyzing Lyft user behavior, he recently measured the impact of what he calls “left digit bias.” In other words, reducing a tax rate from $ 15 to $ 14.99 has the same effect on consumer demand as lowering it from $ 15.99 to $ 15.
Other findings at Ubernomics are not surprising. For example, consumers care about the price and the lower it is, the more likely they are to book a trip.
However, Analyzes of how we use these applications also reveal preferences and idiosyncrasies of human economic behavior.
For example, if you decide to become an Uber driver and think that being friendly to customers will have a significant impact on your income, there is bad news.
Even when customers rate a driver 10% better than another for friendliness, both get the same tip, The list ends.
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