HOW UBER GAMES ITS DRIVERS
Fake female dispatchers, free badges and tease target goals are among psychological tricks the ride-hailing company uses to nudge drivers into unwittingly working harder
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 behavioural 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 behavioural research.
Uber’s innovations reflect the changing ways companies are managing workers amid the rise of the freelancebased “gig economy.”
Its drivers are officially independent business owners rather than traditional employees.
This allows Uber to minimize labour costs, but means it cannot compel drivers to show up at a specific place and time.
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 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.
“We show drivers areas of high demand or incentivize them to drive more,” said Michael Amodeo, an Uber spokesperson. “But any driver can stop work literally at the tap of a button — the decision whether or not to drive is 100-per-cent 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.
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 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 North American worker.
“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 behaviour of the driver in the direction they want it to go.” An empathy question Alongside Uber’s daunting targets for expanding its pool of drivers to meet mounting demand, a high turnover threatened to cap the company’s growth and throw it into crisis.
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-per-cent 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 to serve every customer quickly.
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. “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.
“It was all day long, every day — texts, emails, popups: ‘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.
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 disadvantage in their competition with Lyft, which had cultivated a reputation for being more driver-friendly.
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.”
Of course, managers have been borrowing from the logic of games for generations, as when they set up contests and competition among workers.
But Uber can go much further. Because it mediates its drivers’ entire work experience through an app, there are few limits to the elements it can gamify.
Kevin Werbach, a business professor who has written extensively on the subject, said that while gamification could be a force for good in the gig economy — for example, by creating bonds among workers who do not share a physical space — there was a danger of abuse. “If what you’re doing is basically saying, ‘We’ve found a cheap way to get you to do work without paying you for it, we’ll pay you in badges that don’t cost anything,’ that’s a manipulative way to go about it,” he said.
For some drivers, that is precisely the effect. Scott Weber said he drove full time most weeks last year, picking up passengers in the Tampa, Fla., area for both Uber and Lyft, yet made less than $20,000 before expenses like gas and maintenance. “I was a business that had a loss,” said Weber, who is looking for another job. “I’m using payday loans.”
Still, when asked about the badges he earns while driving for Uber, Weber practically gushed. “I’ve got currently12 excellent-service and nine great-conversation badges,” he said in an interview in early March. “It tells me where I’m at.” ‘Constantly busy’ When asked whether Uber’s product managers and data scientists were akin to developers at a social gaming company such as Zynga, Jonathan Hall, Uber’s head of economic and policy research, accepted the analogy but rejected the implication.
He argued that exploiting people’s psychological tics was unlikely to have more than a marginal effect on how long they played Zynga’s games or drove for Uber. It is “icing on the cake,” he said.
Hall is clearly right about the effects of certain techniques, like those pitched at drivers’ tendency to set income targets or to focus more on losses than gains. On the other hand, even features that produce relatively small changes in driving patterns can become quite important to a company the size of Uber.
Consider an algorithm called forward dispatch — Lyft has a similar one — that dispatches a new ride to a driver before the current one ends.
Forward dispatch shortens waiting times for passengers. Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods — a key goal for both companies.
Uber and Lyft explain this in essentially the same way. “Drivers keep telling us the worst thing is when they’re idle for a long time,” said Kevin Fan, director of product at Lyft. “If it’s slow, they’re going to go sign off. We want to make sure they’re constantly busy.”
While this is true, there is another way to think of the logic of forward dispatch: It overrides self-control.
Perhaps the most prominent example is that such automatic queuing appears to have fostered the rise of binge-watching on Netflix. “When one program is nearing the end of its running time, Netflix will automatically cue up the next episode in that series for you,” the scholars Matthew Pittman and Kim Sheehan wrote in a 2015 study of the phenomenon. “It requires very little effort to binge on Netflix; in fact, it takes more effort to stop than to keep going.”
As with viewers and binge-watching, many drivers appear to enjoy the forward-dispatch feature, which can increase earnings by keeping them busier. But it can work against their interests by increasing the number of drivers on the road and defusing fare surges. And whether they enjoy it is separate from the question of agency — whether they have it, or whether the company does.
Uber officials say the feature initially produced so many rides at times that drivers began to experience a chronic Netflix ailment — the inability to stop for a bathroom break. Amid the uproar, Uber introduced a pause button.
“Drivers were saying: ‘I can never go offline. I’m on just continuous trips. This is a problem.’ So we redesigned it,” said Maya Choksi, a senior Uber official in charge of building products that help drivers. “In the middle of the trip, you can say, ‘Stop giving me requests.’ So you can have more control over when you want to stop driving.” Imagining the Future There are aspects of the platforms that genuinely do increase drivers’ control over their work lives, as Uber frequently points out. Unlike most workers, an Uber driver can put in a few hours each day between dropping children off at school and picking them up in the afternoon.
Uber is even in the process of developing a feature that allows drivers to tell the app in advance that they need to arrive at a given location at a given time. “If you need to pick up your kids at soccer practice at 6 p.m.,” said Nundu Janakiram, the Uber official in charge of products that improve drivers’ experiences, “it will start to give you trips to take you in the general direction to get to a specific place in time.”
Individual features aside, the broader question of how much Uber seeks to influence drivers through behavioural science may come down to how much its business model requires it.
The contractor model provides a strong impetus for companies like Uber to grow. Many companies in the gig economy simply do not have enough workers, or rich enough data about their workers’ behaviour, to navigate busy periods using nudges and the like. To avoid chronic understaffing, they have switched to an employee model that allows them to compel workers to log in when the companies most need them.
Once companies achieve a certain scale, on the other hand, they enter a virtuous cycle: The risk of understaffing drops with a big enough pool of workers, and the cost savings of using contractors begins to outweigh the inefficiencies.
It is, as a result, not too hard to imagine a future in which massive digital platforms such as Uber have an appetite for tens of millions of workers.
In such an economy, experts say, using big data and algorithms to manage workers will not simply be a niche phenomenon. It may become one of the most common ways of managing the North American labour force.
“You have all these players entering into this space, and the assumption is they’ll do it through vast armies of underemployed people looking for extra hours, and we can control every nuance about what they do but not have to pay them,” said David Weil, the top wage-and-hour official under former U.S. president Barack Obama.
When you stop to consider the enormous cost advantages, Weil said, “it says to me this is an area that will grow fast.”
“We’re talking about this kind of manipulation that literally affects people’s income.” RYAN CALO UNIVERSITY OF WASHINGTON