Low battery is a key factor when talking about the odds of hailing a more expensive Uber ride.
The company’s head of economic research, Keith Chen, told NPR science correspondent Shankar Vedantam in a recent interview that Uber knows when users’ phones are about to die and that they’re more likely to accept surge-pricing if their batteries are running low. This, out of fear that they wouldn’t be able to afford waiting 15 minutes until the price goes down.
Surge-pricing is designed to push users a higher surge price so they reconsider the idea of hailing an Uber right away, which results in a downward pressure to demand. But those who are running out of battery are less likely to wait 15 minutes and pay less because their think that their phones would probably be dead by that moment.
During the episode on the Hidden Brain podcast, Chen revealed that Uber keeps tabs on smartphones’ batteries so the app realizes when it has to switch into low-power mode. That’s how the company is aware of customers’ desperation for hailing an Uber immediately before their phones die and they lose the chance of getting a safe ride home.
However, Chen clarified that Uber doesn’t take advantage of that behavior to force users pay more even though it could do it if it wanted to.
“We absolutely don’t use that to push you a higher surge price, but it’s an interesting psychological fact of human behavior”, Chen told Vedantam.
Chen, who’s also a behavioral economist at UCLA, explained that the ride-sharing company charges more when it identifies a high demand because that produces an increase in the supply of rides. He admitted that most customers may think surge-pricing is unfair, but he told Vedantam how the strategy has contributed to Uber’s success.
How surge pricing has driven Uber’s success
Uber is different because it focuses on providing a service that’s entirely reliable. The only way to be able to fulfill customers’ expectations of getting a ride exactly when they really need it, Chen said, is to apply dynamic pricing. The idea is to first provide the service to the person who needs to go now while the others who can afford waiting a little longer get the cheapest price. He also noted that the company gives drivers a strong incentive to make them work in high-demand areas.
Uber first started surge-pricing by going from 1X to 1.2X, which caused a drop in demand by 27 percentage points in customers who would request a ride, according to Chen. Further drops in demand occur as the company ticks up the price further and further because they know people don’t like to pay more.
Another interesting fact Chen has noticed regarding surge-pricing is that round numbers freak out people the most. He said in the interview that going from 1.9 to 2.0 results in a six times larger drop in demand compared with going from 1.8 to 1.9. And it’s actually the same amount of money that’s at stake.
Customers feel 2.0 is just too unfair. In fact, people tend to take more rides at 2.1 than at 2. Chen’s theory is that users believe the surge at 2.1 might be calculated by some smart algorithm and end up accepting it more because it doesn’t seem so capricious.
The behavioral economist pointed out that having to wait 15 minutes to get a cheaper ride is better than being trapped in traffic in a taxi while watching how the price is raising again and again. He described it as “the worst possible psychological experience”.
Uber drivers vs. cab drivers
Behavioral anomalies in UBER price surging data. Also? Monkeys! Fun new podcast episode featuring @MKeithChen https://t.co/IV2G153Ftn
— Shankar Vedantam (@HiddenBrain) May 17, 2016
Surge-pricing benefits users because they can actually get a ride exactly when they need it the most even if demand is high, but it also benefits drivers. Because the main goal is to get almost everyone in a crowded city from point A to point B just in time at a cheap price, Uber incentives drivers by giving them the chance to gain twice as much so they can stay a little longer at work when they’re needed the most.
Chen and Vedantam compared New York City cab drivers with Uber drivers and the behavioral economist noted that the first group gets exactly the same amount of money for every trip no matter if it rains or if demand increases for whatever reason. For instance, once they have made $200 dollars, they simply decide it’s time to go home because there’s nothing attractive for them to stay out longer in a rainy day, given that their work is based on a regulated fare.
On the other hand, Chen pointed out, Uber drivers really want to stay out a little longer when they see it starts to surge 2.1 times. It’s a smart decision to stay out if they’re getting that amount for every extra trip they do. This strategy leads to more drivers willing to cover the areas where they’re needed the most and that behavior results in more customers being able to get a ride just in time no matter how high demand is.
Source: The Verge