Lyft challenged CR\u2019s findings, citing an \u201cobserver effect,\u201d meaning that by having dozens of people checking prices for the same route at the same time, CR may have artificially inflated demand for that ride and influenced the final prices our volunteers saw. Uber said that because its ride prices change \u201cnearly every second,\u201d it was \u201cimpossible\u201d for us to ensure that trip requests happened at exactly the same time.\u00a0<\/p>\n
In short, Uber and Lyft argue that no two trips on their platforms\u2014no matter how seemingly close in time and location they are to each other\u2014can ever truly be the same.<\/p>\n
\u201cIn an open, dynamic marketplace like ours, with nearly 1.7 million mobility and delivery trips per hour, a trip is defined just as much by when it is requested and what\u2019s happening nearby as where it is going,\u201d Uber said in a statement to CR.<\/p>\n
But several experts we shared our findings with dispute that argument. On nearly every route we tested, they noted, we found that at least some of our volunteers converged on the same price for the same ride at almost the same time. And it would be difficult for CR\u2019s tests alone to create artificial spikes in demand, given the relatively small number of volunteers we used and the mostly large and densely populated places we chose for our test rides, experts said.<\/p>\n
\u201cYou\u2019re saying that a few dozen people caused such a dramatic effect? Maybe if it was the heat of rush hour, from the airport to downtown, a truly hot surge area, but that doesn\u2019t apply here,\u201d says Christo Wilson, a computer science professor and associate dean at Northeastern University in Boston who previously audited Uber and Lyft\u2019s pricing models for the city of San Francisco.<\/p>\n
So what explains the different prices our volunteers saw, according to the companies? Uber and Lyft said that a wide variety of factors\u2014rider demand, the supply of available drivers, location, time, estimated trip time and distance, weather, promotional offers, and traffic patterns, among them\u2014all play a part in both original and final prices.<\/p>\n
\u201cPrice differences reflect real marketplace dynamics,\u201d Lyft\u2019s Sid Patil, executive vice president of the company\u2019s marketplace division, said in a statement. \u201cAt any given moment, more drivers may be available in a specific area, different demand levels, or different promotional activity. All in all, our marketplace ebbs and flows, depending on locations, times, events, weather, and other factors.\u201d<\/p>\n
Uber and Lyft said the only truly personalized pricing on their platforms is through their promotional offers, such as new-rider discounts and \u201cre-engagement offers,\u201d which they use to entice back customers who haven\u2019t used the app in a while. Neither company provided a complete list of all the factors they use to personalize promotional offerings.<\/p>\n
But elsewhere, both Uber and Lyft have detailed the types of data they collect and how it could be used, in their U.S. patent filings and company privacy policies.<\/p>\n
Lyft said in a statement that it doesn\u2019t group, or \u201csegment,\u201d its customers or use behavioral data to set base prices. But the company acknowledged that it uses a \u201cbroad set of signals\u201d for its promotions and discounts. Lyft\u2019s privacy policy<\/a> goes into detail about some of the customer data it collects: how you interact with the Lyft app; your address book and calendar, if you consent; and the creation of inferences about who you are. Lyft provides two examples in its privacy policy: If you frequently ride to and from airports, you may be identified as a frequent traveler. Lyft says it may also infer your gender based on your first name.<\/p>\n Lyft\u2019s patents go much further, outlining \u201csensitivity\u201d scores and models, which can be used to predict the \u201cimportance\u201d or \u201cpriority\u201d of a given trip, arrival, or drop-off location; an \u201cintent\u201d model, which is capable of using your demographic info to predict a ride before it is even requested; and a willingness-to-pay score, defined as the \u201cwillingness by the mobile requestor device to pay a higher transportation service amount.\u201d<\/p>\n Uber said in a statement that it, too, doesn\u2019t use \u201cprotected characteristics,\u201d such as race, gender, ethnicity, and disability status, for base prices or promotions; nor does it use \u201crider-specific behavioral characteristics.\u201d But that did not address the use of behavioral data of larger customer groups. Uber did acknowledge that it uses personal data for promotions and discounts. Uber\u2019s patents show it can use a phone\u2019s sensor data and your past behavior for its models. That data can include how quickly and accurately you type an address; your gait and walking speed, which can be used to infer your height, weight, and body type; and even the precise angle at which you hold your phone, to spot any deviation from the norm. Your ride history is also a powerful predictor of both who you are and where you\u2019re likely to go. Uber outlines one such example in one of its advertising patents: If someone routinely requests an Uber to a day care center or school before going to a workplace or university, they could be identified as a single working parent. From there, the age, gender, and sex of the rider, and the rough ages of the rider\u2019s children, can be determined through the ride history alone, Uber says.<\/p>\n \u201cEarlier generations of these pricing systems really focused on time, supply and demand, and price elasticities and efficiencies. But now, many companies actively use behavioral and context data to inform their models. They don\u2019t even necessarily need your personal data,\u201d says M. Keith Chen, a behavioral economist and professor at the University of California, Los Angeles, who previously worked as Uber\u2019s head of economic research and helped create its surge pricing algorithm.<\/p>\n Both Uber and Lyft also denied offering their customers fictitious discounts. Lyft attributed our findings on these discounts to the fact that \u201cprices change constantly based on real-time marketplace conditions.\u201d Uber called our testing \u201cfundamentally flawed\u201d because, in its view, you can\u2019t establish a true baseline price on its platform.<\/p>\n \u201cIf one user\u2019s undiscounted price matches another user\u2019s discounted price, that\u2019s simply because these prices were different to start with, due to changes in real-time marketplace conditions,\u201d Uber said in its statement.<\/p>\n Experts also dispute those arguments, saying the fact that many volunteers saw exactly the same final price for many of the routes we chose suggested that there was, in at least some cases, a true algorithmically determined starting price.<\/p>\n \u201cI don\u2019t agree that there is no baseline price, even with ride-share,\u201d says Chen at UCLA. \u201cWhat you\u2019re seeing with your data is indeed a baseline.\u201d<\/p>\n Uber also took issue with our fake discount analysis. We counted fares as discounted when what appeared to be an original price had a strikethrough and a lower price was displayed. Uber said in a statement that when these prices are accompanied by labels such as \u201cFares lower than usual,\u201d they are not meant to suggest a discount but instead a \u201chistorical\u201d or \u201cinformational\u201d comparison.<\/p>\n Lastly, Uber and Lyft said the percentage of each fare they take is much lower than what CR calculated. They put their U.S. \u201ctake rates\u201d at \u201caround 20%\u201d and \u201csignificantly lower than 30%,\u201d respectively.\u00a0<\/p>\n The disagreement largely comes down to different accounting practices: Uber and Lyft say our calculations don\u2019t acknowledge the large and growing amounts they spend on auto insurance to cover drivers when they\u2019re en route to and during rides.\u00a0<\/p>\n But experts we spoke to say the companies\u2019 insurance expenses are simply a cost of doing business that, under standard accounting practices, shouldn\u2019t be excluded. (Sherman at Columbia says excluding them is \u201cvery misleading.\u201d) And they note that both companies have created their own in-house insurance subsidiaries, with billions in reserves available for claims.<\/p>\n Uber also argued that because the driver and riders were in the same location, the experiment minimized pickup distance and \u201ccreated an artificial scenario that isn\u2019t representative of reality.\u201d While our tests were designed to have riders and volunteers near one another, our analysis of how much Uber and Lyft take from each fare is similar to other studies. Experts say Uber and Lyft do, in fact, take nearly half of customer fares. An analysis of rides<\/a> provided by three Uber drivers with over 50,000 rides between them, by Sherman of Columbia, found that Uber\u2019s share has now risen to more than 50 percent in many cities. In a separate analysis conducted for CR using ride-hail trip data from Oregon, Princeton\u2019s Workers Algorithm Observatory calculated that, on average, Uber took 44 percent and Lyft took 52 percent of the amount a rider paid for a trip.<\/p>\n Technical accounting issues aside, Uber and Lyft drivers we spoke to say their take-home pay is an ever-shrinking portion of what their riders actually pay\u2014and far less than what they\u2019ve been led to believe they would make. In 2024, for example, Lyft announced<\/a> it would guarantee drivers 70 percent or more of rider payments each week, \u201cafter external fees.\u201d (Later that year, the company settled with the Federal Trade Commission and paid a $2.1 million fine for what the agency described as \u201cdeceptive earnings claims<\/a>\u201d about how much drivers could expect to make, and this year announced it would cap its fee at 30 percent<\/a>.)\u00a0<\/p>\n