How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons
New York Times, 2 April 2017
The secretive ride-hailing giant Uber rarely discusses internal matters in public. But in March, facing crises on multiple fronts, top officials convened a call for reporters to insist that Uber was changing its culture and would no longer tolerate “brilliant jerks.” Notably, the company also announced that it would fix its troubled relationship with drivers, who have complained for years about falling pay and arbitrary treatment.
“We’ve underinvested in the driver experience,” a senior official said. “We are now re-examining everything we do in order to rebuild that love.”
And yet even as Uber talks up its determination to treat drivers more humanely, it is engaged in an extraordinary behind-the-scenes experiment in behavioral science to manipulate them in the service of its corporate growth — an effort whose dimensions became evident in interviews with several dozen current and former Uber officials, drivers and social scientists, as well as a review of behavioral research.
Uber’s innovations reflect the changing ways companies are managing workers amid the rise of the freelance-based “gig economy.” Its drivers are officially independent business owners rather than traditional employees with set schedules. This allows Uber to minimize labor costs, but means it cannot compel drivers to show up at a specific place and time. And this lack of control can wreak havoc on a service whose goal is to seamlessly transport passengers whenever and wherever they want.
Uber helps solve this fundamental problem by using psychological inducements and other techniques unearthed by social science to influence when, where and how long drivers work. It’s a quest for a perfectly efficient system: a balance between rider demand and driver supply at the lowest cost to passengers and the company.
Employing hundreds of social scientists and data scientists, Uber has experimented with video game techniques, graphics and noncash rewards of little value that can prod drivers into working longer and harder — and sometimes at hours and locations that are less lucrative for them.
To keep drivers on the road, the company has exploited some people’s tendency to set earnings goals — alerting them that they are ever so close to hitting a precious target when they try to log off. It has even concocted an algorithm similar to a Netflix feature that automatically loads the next program, which many experts believe encourages binge-watching. In Uber’s case, this means sending drivers their next fare opportunity before their current ride is even over.
And most of this happens without giving off a whiff of coercion.
“We show drivers areas of high demand or incentivize them to drive more,” said Michael Amodeo, an Uber spokesman. “But any driver can stop work literally at the tap of a button — the decision whether or not to drive is 100 percent theirs.”
Uber’s recent emphasis on drivers is no accident. As problems have mounted at the company, from an allegation of sexual harassment in its offices to revelations that it created a tool to deliberately evade regulatory scrutiny, Uber has made softening its posture toward drivers a litmus test of its ability to become a better corporate citizen. The tension was particularly evident after its chief executive, Travis Kalanick, engaged in a heated argument with a driver that was captured in a viral video obtained by Bloomberg and that prompted an abject apology.
But an examination by The New York Times found that Uber is continuing apace in its struggle to wield the upper hand with drivers. And as so-called platform-mediated work like driving for Uber increasingly becomes the way people make a living, the company’s example illustrates that pulling psychological levers may eventually become the reigning approach to managing the American worker.
While Uber is arguably the biggest and most sophisticated player in inducing workers to serve its corporate goals, other “gig economy” platforms are also involved. Uber’s main competitor, Lyft, and popular delivery services like Postmates rely on similar approaches. So do companies and individuals posting assignments on crowdsourcing sites like Amazon Mechanical Turk, where hundreds of thousands of workers earn piece-rate wages by completing discrete tasks.
Of course, many companies try to nudge consumers into buying their products and services using psychological tricks. But extending these efforts to the work force is potentially transformative.
Though employers have long borrowed insights from social science to get more out of their workers — tech companies like Google have calculated that employees interact more with unfamiliar colleagues when they can graze together at snack bars — they are constrained in doing so. A large body of law and custom in the United States holds that because employers have far more power over their employees than businesses do over their customers, they must provide them with far greater protections — not least, a minimum wage and overtime pay.
Uber exists in a kind of legal and ethical purgatory, however. Because its drivers are independent contractors, they lack most of the protections associated with employment. By mastering their workers’ mental circuitry, Uber and the like may be taking the economy back toward a pre-New Deal era when businesses had enormous power over workers and few checks on their ability to exploit it.
“We’re talking about this kind of manipulation that literally affects people’s income,” said Ryan Calo, a law professor at the University of Washington who studies the way companies use data and algorithms to exploit psychological weaknesses. Uber officials, he said, are “using what they know about drivers, their control over the interface and the terms of transaction to channel the behavior of the driver in the direction they want it to go.”
An Empathy Question
In early 2016, a group of roughly 100 Uber employees responsible for signing up drivers and getting them to drive more voted to change its name — from “supply growth” to “driver growth.”
The vote was not unprompted. For much of the previous year, Uber executives had agonized over how to lower the rate at which drivers were deserting the platform.
Alongside Uber’s already daunting targets for expanding its pool of drivers to meet mounting demand, the high turnover threatened to cap the company’s growth and throw it into crisis.
Uber conducted interviews and focus groups while executives peppered employees with questions like, “What are we doing to have more empathy for the driver side of the equation?”
Underlying the tension was the fact that Uber’s interests and those of drivers are at odds on some level. Drivers, who typically keep what’s left of their gross fare after Uber takes a roughly 25 percent commission, prefer some scarcity in their ranks to keep them busier and push up earnings. For its part, Uber is desperate to avoid shortages, seeking instead to serve every customer quickly, ideally in five minutes or less.
This is particularly true of shortages so pronounced as to create a “surge” — that is, a higher fare than normal. While surges do mitigate shortages, they do so in part by repelling passengers, something directly at odds with Uber’s long-term goal of dominating the industry. “For us, it’s better not to surge,” said Daniel Graf, Uber’s vice president of product. “If we don’t surge, we can produce more rides.”
As a result, much of Uber’s communication with drivers over the years has aimed at combating shortages by advising drivers to move to areas where they exist, or where they might arise. Uber encouraged its local managers to experiment with ways of achieving this.
“It was all day long, every day — texts, emails, pop-ups: ‘Hey, the morning rush has started. Get to this area, that’s where demand is biggest,’” said Ed Frantzen, a veteran Uber driver in the Chicago area. “It was always, constantly, trying to get you into a certain direction.”
Some local managers who were men went so far as to adopt a female persona for texting drivers, having found that the uptake was higher when they did.
“‘Laura’ would tell drivers: ‘Hey, the concert’s about to let out. You should head over there,’” said John P. Parker, a manager in Uber’s Dallas office in 2014 and 2015, referring to one of the personas. “We have an overwhelmingly male driver population.”
Uber acknowledged that it had experimented with female personas to increase engagement with drivers.
The friction over meeting demand was compounded by complaints about arrangements like aggressive car leases that required many drivers to work upward of 50 or 60 hours each week to eke out a profit. Uber officials began to worry that a driver backlash was putting them at a strategic disadvantage in their competition with Lyft, which had cultivated a reputation for being more driver-friendly.
Uber had long been a reflection of Mr. Kalanick, its charismatic and hard-charging chief, who has often involved himself in corporate minutiae. According to an article in The Information, Mr. Kalanick had complained to subordinates that he was not informed sooner about a glitch with the company’s push notifications and had personally weighed in on the time at which employees could receive free dinner.
Now Uber began a process of, in effect, becoming a little less like Mr. Kalanick, and a little more like Lyft.
It rethought a lease program, softened the hectoring tone of its messages and limited their volume. At times it became positively cheery.
During roughly the same period, Uber was increasingly concerned that many new drivers were leaving the platform before completing the 25 rides that would earn them a signing bonus. To stem that tide, Uber officials in some cities began experimenting with simple encouragement: You’re almost halfway there, congratulations!
While the experiment seemed warm and innocuous, it had in fact been exquisitely calibrated. The company’s data scientists had previously discovered that once drivers reached the 25-ride threshold, their rate of attrition fell sharply.
And psychologists and video game designers have long known that encouragement toward a concrete goal can motivate people to complete a task.
“It’s getting you to internalize the company’s goals,” said Chelsea Howe, a prominent video game designer who has spoken out against coercive psychological techniques deployed in games. “Internalized motivation is the most powerful kind.”
Mr. Amodeo, the Uber spokesman, defended the practice. “We try to make the early experience as good as possible, but also as realistic as possible,” he said. “We want people to decide for themselves if driving is right for them.”
That making drivers feel good could be compatible with treating them as lab subjects was no surprise. None other than Lyft itself had shown as much several years earlier.
In 2013, the company hired a consulting firm to figure out how to encourage more driving during the platform’s busiest hours.
At the time, Lyft drivers could voluntarily sign up in advance for shifts. The consultants devised an experiment in which the company showed one group of inexperienced drivers how much more they would make by moving from a slow period like Tuesday morning to a busy time like Friday night — about $15 more per hour.
For another group, Lyft reversed the calculation, displaying how much drivers were losing by sticking with Tuesdays.
The latter had a more significant effect on increasing the hours drivers scheduled during busy periods.
Kristen Berman, one of the consultants, explained at a presentation in 2014 that the experiment had roots in the field of behavioral economics, which studies the cognitive hang-ups that frequently skew decision-making. Its central finding derived from a concept known as loss aversion, which holds that people “dislike losing more than they like gaining,” Ms. Berman said.
Still, Ms. Berman disclosed in an interview, Lyft eventually decided against using the loss-aversion approach, suggesting that the company has drawn brighter lines when it comes to potential manipulation.
As he tried to log off at 7:13 a.m. on New Year’s Day last year, Josh Streeter, then an Uber driver in the Tampa, Fla., area, received a message on the company’s driver app with the headline “Make it to $330.” The text then explained: “You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted.
“I’ve got screen shots with dozens of these messages,” said Mr. Streeter, who began driving full time for Lyft and then Uber in 2014 but quit last year to invest in real estate.
Mr. Streeter was not alone. For months, when drivers tried to log out, the app would frequently tell them they were only a certain amount away from making a seemingly arbitrary sum for the day, or from matching their earnings from that point one week earlier.
The messages were intended to exploit another relatively widespread behavioral tic — people’s preoccupation with goals — to nudge them into driving longer.
Over the past 20 years, behavioral economists have found evidence for a phenomenon known as income targeting, in which workers who can decide how long to work each day, like cabdrivers, do so with a goal in mind — say, $100 — much the way marathon runners try to get their time below four hours or three hours.
While there is debate among economists as to how widespread the practice is and how strictly cabdrivers follow such targets, top officials at Uber and Lyft have certainly concluded that many of their drivers set income goals. “Others are motivated by an income target for sure,” said Brian Hsu, the Lyft vice president in charge of supply. “You hear stories about people who want to buy that next thing.” He added, “We’ve started to allow drivers to set up those goals as well in the app.”
Uber even published a study last year, using its vast pile of data on drivers’ rides and hours, finding that a “substantial, although not most, fraction of partners” practice an extreme form of income targeting when they start on the platform, though they abandon it as they gain more experience. Strict income targeting is highly inefficient because it leads drivers to work long hours on days when business is slow and their hourly take is low, and to knock off early on days when business is brisk.