Does Uber Use Tricks to Push Its Drivers’ Buttons? Thinking about Digital Motivation at Work
After a long history of disputes with drivers, culminating in violence and torrents of bad PR, Uber is trying to turn over a new leaf. Yet, its practices are still under the scrutiny of new media, and today the New York Times ran a story about the company’s experimentation with driving motivation, employing game elements to engage and align drivers with company goals. While the story questions some of the practices (such as ensuring drivers go to “surge” areas and more), the apparent success of their methods teaches us valuable lessons. How do you manage a decentralized workforce that is neither committed nor bound to you by a feeling of community or pride?
Here’s a few things we learned from Uber’s recent foray into gamification as described in NY Time’s recent article How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons.
Loss aversion – As many behavioral economics scholars note, loss aversion is a much stronger driver of motivation than potential gains. We have seen this in our work here at GamEffective. Like the Lyft experiment mentioned in the article, we that see players who accumulated game points, become more calculated in interactions with campaigns, taking extra effort to answer questions correctly or perform required tasks for fear of losing them.
Recognition – Letting people know they are doing well is not just a feel-good mechanism. By providing new drivers with simple encouragement to complete their initial 25 rides, e.g. “You’re almost halfway there, congratulations!” Uber both shows them they are on the right way and, as mentioned in the previous section makes their “gaining” tangible and involved with possibly preventing a potential loss.
Intrinsic motivation – Uber’s use of badges is a great example of intrinsic motivation. Monetary rewards have diminishing value and the added discomfort of costing money. Sometimes they can make motivation drop. In addition to bonuses, Uber drivers can earn badges for performance. This practice has been so successful one driver is quoted as “gushing” over “12 excellent service and nine great-conversation badges” while not being able to turn a profit from his work.
For more information on what motivates people, read our post about What Science Tells Us About Motivation
Goal Setting “Tricks”
Personalization – relying on the vast amounts of data collected on each driver, Uber structures highly personalized goals. These goals are contextual and relevant to the specific motivations and status of each driver. Personalized goals drive much higher participation and engagement than one size fits all. Since they are more relevant to the driver, they are more engaging.
Attainability – “Only 10$ away from reaching 330$” is how Uber goads a driver to extend his shift. This type of prompt is likely to push a driver to work harder as success is just around the corner. Success seems so achievable and attainable the idea this prompt seems almost like a no-brainer.
Competition – While using their app drivers are encouraged to break their last weeks’ earnings record and compete against their own benchmarks. Doing this, Uber maximizes the positives in competition without the risk – It fosters competitive instincts without the risk of pitting drivers in challenges they can’t win against colleagues with different capabilities and conditions
Collaborative Goal setting – After seeing many drivers set themselves revenue targets, Uber hardwired this practice into their driver app. Drivers can now set goals for themselves on the app and see what they need to do to hit their goal. Doing so, Uber aligns driver’s motivations with its own goals and makes them much more likely to engage with their messages.
Cues and KPIs
Visualized KPIs – You can’t navigate anywhere without knowing where you are. Dashboards are not just for managers. In Uber’s case drivers can constantly monitor KPIs such as how many rides they took, how much money they made etc. All KPIs are visualized making them simple to read, understand and compare with benchmarks. These dashboards motivate drivers to try to break their records, know where they need to focus attention and, most importantly, keep on playing.
Cuing – Casinos are notoriously filled with symbolisms of affluence. Sounds of slot machine wins are constantly played in loop. These cues are designed to push gamblers to stay at the table hinting great wealth is at your fingertips. Uber uses similar mechanisms for drivers – The visuals of engine gauges approaching $ signs when drivers are about to sign off or the box pop up suggesting navigating the driver towards areas of high demand. All these, same as GamEffective’s “Next Best Action Feature” tell the player what he needs to do next.
Trust – The Secret Sauce for Making Gamification Work Well
With its vast amounts of collected data, technological edge and highly involved workforce, Uber is well positioned to maximize the benefits of gamification. All this is hindered though by one problem. A lack of trust between the company and its drivers can turn any effort to generate intrinsic motivations on its head. That’s why gamification (or digital motivation) can only work when trust is present. (read this article about the marshmallow test for a great example on why trust is important).
Going forward Uber is working on bridging the trust gap with its drivers. We should keep an open eye and learn from Uber’s experiments with gamification, to see what works and what doesn’t. Yet, we need to make sure we keep the trust with employees and use gamification to forward the mutual benefits of the employee and the organization, not the organization alone.
One group of Lyft drivers were shown how much money they could make if they work in undesirable hours. A second group was shown the same data only this time displayed as what they are losing by working the same number of hours in regular shifts. The group shown the loss were substantially more likely to take the unwanted shifts