Surge pricing means the app charges more when lots of people want rides at the same time. It’s meant to encourage more drivers to go online so you can still get a car.
A rate card is the taxi’s official price list—often based on how long you ride and how far you go. It’s the older way of pricing compared with what the apps do.
Upfront pricing is when the app tells you what the ride will cost before you get in. Even though it’s shown ahead of time, the number is still calculated by the app and can change.
They’re talking about how the same ride can cost noticeably different amounts. Even if you’re going to the same place at the same time, the price you see can be higher or lower than someone else’s.
The “median” is the middle result when you line up all the price differences from smallest to largest. It helps show a typical difference without being thrown off by a few weirdly high or low cases.
They picked specific trips ahead of time and used the same ones for comparisons. That way, they can tell whether price changes are really due to the booking/pricing system rather than a different destination.
Instead of only checking prices on a phone, they actually got in the car and took the ride. That helps confirm whether the app’s shown price matches what you really pay.
They did part of their real-world testing in Portland, Oregon. The point was to see if the pricing differences still happen when people actually take the rides.
They set it up so volunteers were in the same place and took similar trips at the same time. That makes it easier to compare whether different people see different prices for essentially the same ride.
They’re calling out rides to and from airports. Those trips can be priced differently than normal rides, and they claim the price differences are bigger there.
Uber is a rideshare app that matches you with a driver and charges you for the trip. They’re discussing how the price you see can vary a lot for the same ride.
Fictitious pricing means the app may show a fake “before” price to make the “after” price look cheaper than it really is. The goal is to make you think you’re getting a discount when the comparison isn’t trustworthy.
Historical comparison messaging is when the app says “prices are lower” by comparing today’s fare to an earlier price. It may not mean you’re getting a true discount versus what you would have paid immediately.
A legal gray area means the law isn’t totally clear, so it’s not obvious if the practice is allowed. In this case, it’s whether the “discount” wording is genuinely compliant or misleading.
A bona fide discount is a real, legitimate price cut—not just marketing. It means the “original” price used to show the discount is trustworthy and the lower price is genuinely a reduction.
A fake discount is when the app pretends you’re saving money, but the “deal” price is really just the usual price. The higher crossed-out number is there to trick you into thinking it’s a bargain.
A strike-through is the crossed-out number you see on a screen. It’s meant to make you think the price used to be higher, so the new price looks like a discount.
A “black box” means you can see the inputs and outputs, but you can’t see how the system makes its decisions. In this case, it’s the app’s hidden pricing logic.
GPS signals are the location “pings” from satellites that your phone uses to know where you are. Ride-share apps can use that location info to calculate things like the route and price.
Network latency is basically lag—how long it takes for data to travel over the internet. If the app is calculating prices in real time, that lag can sometimes affect what you’re shown.
Net price is the final total you end up paying. They’re saying what matters is the final number after discounts and promos.
Concept
algorithms are sort of invading our everyday financial lives
The idea is that computer systems are using your data to decide what you’re shown and how much you pay. The worry is that it’s not always clear or fair how those decisions are made.
Promotions and discounts are deals that lower the price of a ride. Even if the starting price is the same for everyone, these deals can make the final price you pay feel totally different.
Base price is the starting cost of a ride before coupons or special deals are applied. The discussion is about whether that starting number is the same for everyone, versus whether discounts are tailored to you.
Effective price is the final price you actually pay after any discounts or deals. The point is that the deals can matter more than the original listed fare.
Differential pricing just means the price changes depending on the situation. For ride apps, it can mean riders see one price while drivers get a smaller cut for the same ride.
Algorithmic pricing means the app uses a computer model to set the fare. That model can raise prices when demand is high, even if driver pay doesn’t rise the same way.
GridWise is a group the host cites for tracking ride-share pricing over time. The episode uses their report to argue that rider fares have been rising faster than inflation.
“Per mile” is pay based on how far the car drives. The point here is that drivers may be getting paid about the same per mile while riders pay more overall.
This is insurance for cars being used to earn money, like ride-share driving. If it gets more expensive, it can change how much of the ride price goes to costs instead of profit.
A captive insurance company is an insurance business owned by the same company that needs the coverage. Instead of buying insurance from a third party, the parent handles the risk through its own subsidiary.
Self-insured means the company is paying for certain losses itself, rather than relying entirely on an insurance policy. That can change how costs show up in the numbers people compare.
LIVE
Is your rideshare app lying to you?
According to a shocking new study, there's no question that Uber and Lyft are doing something
shady and Consumer Reports has the proof.
We'll dig into what they discovered and why that discount you're offered on your next
ride is probably a lie.
Welcome back, as promised, we have this is Derek Kravitz from Consumer Reports.
Derek, I understand you're an investigative reporter, we got something juicy, but maybe
give us a little detail on what that really means and what does day to day look like for
you?
Sure.
So it sounds like a big scary title or term, but really I'm my official title is Deputy
Editor of Special Projects, basically long term stories and investigations and things
where we look at issues in the marketplace.
And so my beat is typically tech, finance, a little bit of AI thrown in there now with
everything.
So that's pretty much what I do.
AI touches everything now, so I guess you're well positioned.
Exactly.
Cool.
So maybe let's back up a little bit of, you know, just thinking about, you know, I'm thinking
about Uber, you know, Lyft, I remember, you know, Uber, I mean, they onboarded everyone
because it used to be way cheaper than taking a taxi, right?
And based on my recent experiences, that seems to not really be the case anymore at all.
It seems like it's all the same, but Uber basically disrupted the industry so much that
everyone's used to using them.
So those days are kind of gone.
But then I also understand this, I don't know if this led into it, that you guys did a study
sort of investigation into Instacart recently.
And maybe you can provide a little background on that and how that, you know, led into this.
Yeah, they connect with each other, so it's a good segue.
So with Instacart, we want to know more about how they use tech to set prices on the platform.
And if you haven't used Instacart, basically it's a third-party grocery delivery service
that contracts with many of the largest grocery chains in the U.S. and elsewhere to operate
overseas and basically uses independent contractors to pick up those groceries and deliver them
to your home or office.
And what we were seeing were the prices on the Instacart platform didn't match what
grocery shoppers were seeing on either in the grocery aisle themselves in person or on
the e-commerce or at platforms of that particular grocery store.
So we wanted to see what the markup was, sort of the thinking there.
And what we found was the prices differed for different people at the same time,
same product, same store, and that of course raises a lot of questions.
The prices were, you know, we expected maybe some variation depending on, you know, time of day
or day of week or things like that, maybe a few cents off, but we were seeing differences
upwards of 23% for the same product.
So bananas, Cheerios, cereal, milk, things like that.
And so we conducted this, you know, controlled test where we brought in hundreds of volunteers
all shopping for the same products at the same time and then captured the prices that they were
seeing and saw the different price buckets that they were being grouped into across different
shopping trips. And what we found when we talked to Instacart and we shared them with the data that
we, you know, gleaned from our volunteers was that they were conducting a very large, very,
you know, scaled supercharged version of AB testing, trying to figure out the exact price
of a product, maybe mixed with other products that would result in the greatest sales, right,
the greatest margins for them so that the greatest benefit, financial benefit.
The problem with that was people didn't know what was going on.
So even though Instacart had been fairly upfront about this practice online and in marketing
materials and to their investors, a lot of customers had no idea this was happening across
the country with some of the largest grocery chains in the US. Safeway, Albertsons, even Costco,
you know, big chains. And so underpinning all of this is algorithms. So there's these very
sophisticated, you know, tech products, either built in-house or, you know, a third-party vendor
that, you know, essentially they deploy out into the real world and that helps set the prices.
And whether it's a, you know, different little base prices for different people or
maybe different promotions and discounts supplied. Some people get those discounts
sent to them. Some people don't. That's where the algorithm is determining
what is the ideal price point for this particular product or service.
And so that led us to Uber and Lyft. And Uber is essentially the pioneer of this type of tech,
right? They essentially started. Surge pricing, yeah. Yeah, algorithmic pricing. You know, years
ago, 2009 is when they started in earnest and then Lyft three years later in 2012.
And, you know, they upended the generation's old taxi model, you know, the per minute, per mile,
you know, rate card, the meter that you would see in taxis. They completely changed that. And then,
you know, years after they started, they unveiled upfront pricing. So the idea that you would open
your app, you would look for a trip, a route, and you would see a price. And that price was,
you know, a good faith estimate of how much it would cost you once you actually completed the trip.
But it's algorithmically driven. And it's both on the rider side and the drivers also get their own
sort of algorithmic price for that fare, right, that trip that they're going to perform. Same
thing, independent contractors, right? They're not employees of Uber and Lyft. They're operating on
their own and they're contracting with these companies. And same thing, we heard from a lot of
customers and a lot of CR members that they were getting different prices for what essentially
is the same ride at the same time when they tried to, you know, ask their friends or family to look
at the same ride, or they were taking the same trip at the same time, same day a week, every day,
you know, going to work or what have you. And, you know, to us, it was worth digging into. And
when we saw the price gaps, very similar, we actually saw a median price difference there,
even larger than Instacurt, of 42.4% across all trips, 30 routes across the country when we
looked into it. So to us, these things matter and the customers, they matter.
So you're saying just to go back to the median price, that's as big as like a, you know,
you or I could get a 42% difference in what we're going to pay for the exact same ride,
same time of day, same everything. Exactly. Yeah. And we tested, so again, same sort of
methodology, we brought in hundreds of volunteers, we had them price out trips, again, same time,
same route, 30 predetermined routes across the country, 17 different states. We also did in
person testing too, because it's one thing to look virtually and, you know, look at the prices on
your phone or your laptop. It's a different thing to actually take the ride and to see
that trip play out. So we actually went to Portland, Oregon, and we actually matched
volunteer riders with volunteer drivers in the same place. We all took sort of corresponding
trips together, sort of a unique matching experience. And we did, you know, more than 10
routes there. And we saw the same thing. And so, you know, there can be significant price gaps,
especially for long trips, especially for airport trips, which a lot of people use
Uber and Lyft 4. So yeah. Andrew, yeah, I was looking at some of your statistics and showed
that 42% mean in Texas. One of your regions that you looked at, it was actually like 160%
gap between, you know, like the prices. It looked like some of them were quite cheap,
depending on short trips, but some of them were, you know, it was a huge difference.
Yeah, it can be. And, you know, we looked at rural and urban, you know, different times a day,
different days a week, tried to really sort of understand the marketplace. Yeah. And when there's
not a lot of, you know, supply demand, when it's a pretty rural area, when there's not necessarily
a good return trip back, you can expect to see, you know, maybe a pretty sizable fare. And that's
where you can see really big differences too. If you look at some of the larger price gaps,
that they are in more rural or across city or across county trips around the U.S.
Interesting. Now, going back again a little bit, you had looked at the,
I'm just trying to think about the, sort of the methodology, not the methodology, but how you
got into this. Like you said that you had gotten complaints, like I guess, you know, consumer
reports get there. Is that really what led you to this? Or were you thinking, you know, hey,
Instacart's doing this, Uber pioneered this technology? Uber is probably the worst offender.
A little bit of a mix of both. You know, we listen to our members a lot. We actually have
callouts where we sort of pull them, you know, sort of unscientific survey. We also have scientific
surveys too, where we ask them, you know, what they think of a particular product or what's
going on in the marketplace. We have a whole surveys team that pulls people every month,
2000 U.S. adults typically in that sample. And that really informs sort of a lot of what we do
and the framing. But then we, you know, we talk to experts as well, we actually see ours unique
in that sense. We have, you know, the journalism side, which I'm on, and we have the policy side,
which, you know, advocates or pushes particular issues out in the world.
Sounds like they might be connected on this one.
A little bit, yeah. And they tell us, you know, what they're hearing. But, you know,
we decide on our own what we're going to do. There's a little bit of a firewall.
Yeah, sorry. I mean, even to go the other direction of you've sort of sparked this
investigation. Now it sounds like a policy discussion that somebody might want to stick
through. Yeah, it quickly left our hands and as soon as it hit the, you know,
legislators desk, it changed quite a bit, which is good to see in terms of having,
you know, impact, but, you know, also at the same time, you can see things sort of muddle
or the messaging change, you know, from when you report on something to when it actually
sort of becomes policy or a bill. And then we also have testing, which is great. You know,
we test a lot of cars. We test a lot of products. And so that, that really informs
the methodologies that we employ, you know, even for digital items.
For sure. Now, the sort of seems like the story is it's, it's partially that there's this huge
gap in prices that seems to have no basis in, in fact, right, that they're, they're,
they're, they're like the, the, the, there's sort of the allegation here seems to be that
they're doing something and presumably for gain, right? I found one of the quotes in your article
earlier was just that there's a simple reason companies like this offer different prices to
different customers for the same thing. It enables them to increase sales and profits. So it seems
like, like that's the one thing, but then on the other side, the other part of this seems to be
there's also a, an issue around advertising discounts and that those discounts are,
are, are not accurate. That seems to be like one of the big sort of cruxes of this investigation.
Yeah. And maybe I can explain that, that last point a little bit more, but
so the idea of fictitious pricing is sort of the academic term or fake discounts, right, is what
we described it as. And all that's to say, you know, if you have an $82 trip in say New York City
and, and that's the quoted fare and it's discounted. So it's the original price is $82, but then it's,
there's a strike through and then there's a new price of $62. And then there's a label on top
of it that says prices are lower now. You know, reasonable consumers, many of them that we spoke
to think that that's a discount. When we took that back to Uber, they told us that, you know,
that particular example, which, you know, happens quite a bit in our testing, we saw that come up
a lot. That's not really a discount. That is what they call historical comparison messaging.
And there's a, there's a legal gray area there, you know, is that a bona fide discount, you know,
according to regulators. And, and that really can present, you know, some, there you go.
Yeah. Sorry, I pulled up the slide of, because this was very interesting of this show is what it is
because there are other instances on the app where it actually says like this is a discount,
but this one doesn't say it's a discount. It just sort of shows you that it looks like a discount.
It looks like it, but in their view, it's not. And then when we took this to experts, we took
this to legislators, we took this to truth and advertising, which is a great nonprofit that
really sort of digs into these types of issues, tracks, you know, class action lawsuits across
the country that looks into, you know, false deceptive or misleading, you know, pricing.
And, you know, the threshold is what a reasonable consumer think that this is a discount. And,
you know, in a lot of cases, the answer is no. But, you know, their argument is that, you know,
the phrase fares lower than usual. That means that it's historical that they're comparing,
you know, the price that you're seeing with something in the past. So to us, that was
something that was interesting and worthy of inclusion in an investigation. You know,
what does this all mean? And that actually informed the discounts that we saw. In many cases, we saw,
you know, the discounted price that people were being brought to was the same price that
everyone else had already seen. So it was essentially a true algorithmic baseline price.
And I had one of your charts for that as well. Maybe I didn't include it in my slides. But,
yeah, there was a very interesting one that basically specifically showcased that it was all
the people who got a higher price, but actually what their discounted price was, and all the people
who didn't get a discount, and the price was affected by the same amongst all of them.
Right. And a lot of people have this, you know, sort of maybe instinctual fear, oh,
the discount that I'm seeing is not maybe a real discount, a bona fide discount.
Maybe it's, you know, artificially inflating it and bringing it back down to a price that,
you know, everyone else is seeing. And about 12% of cases across all the trips that we priced out,
that's what we saw. And so, you know, regulators also, you know, have at times taken a hard line
on that as well. But that's also something the FTC, the Federal Trade Commission, does not actively
police anymore and haven't in decades. So it's one of these areas that maybe state AGs sort of
are wrestling with and trying to figure out what they should do.
Interesting. Yeah. So it sounds like, yeah, somewhere else in your statistics, it said how
50% of rides get this advertised discounted price. So sorry, but you said something about 12%
discounted. So what's the difference?
Yeah, I'll give you the sort of nesting doll sort of. So overall across, you know, more than
a thousand different trips we were pricing out, 50% of them had some type of what we would, you
know, call a discount, right, even if it was historical comparison messaging or like a real
discount, something that indicated that there was an original price and then a new price.
And then of those discounted prices, 12% of them had this, what we would call fictitious
pricing or fake discounts where, you know, the original price was higher and then it was brought
down to something lower, but that lower price was what everyone else in this group of shoppers
had already seen, right? So that original price was not really a true discount. That was a fake
discount. So we saw that in 12% of the discounted offers. Gotcha. So this is the slides I've got
up now was sort of one of your like case study examples in the slides. So it talks about how this
one individual and I, when I screen catch and cut off their names, but it talks about how, yeah,
so somebody was basically advertised that the rate it shows, right? It shows there as it was like,
oh, it's supposed to be, you know, roughly 82 bucks or whatever, but you can get it for,
you know, 65, 95. Right. And then somebody else booking the same place, the same location,
same ride, everything, the flat rate was. Right. And this is fairly straightforward. And I think,
yeah, you see this original price of 82.08, you know, the strike through and then the lower price
of 65.95. And then you already see that someone else Chuck in this case has the 65.95 price that
Tessa is now being quoted at calling it a discount. And so, you know, this, this is what, you know,
both consumers and regulators, when we talk to them about it said, you know, this raises questions
to us. And even if something's not illegal, even if something is, you know, not necessarily defined
in, you know, state or federal law, these are, these are reasonable questions that people raise.
And so that's why we thought it's worthy of a fuller story. For sure. Yeah, it's pretty deep.
So, so I'm looking at that. This was, I think the next slide is, you said 40 other riders on the
same route got, you know, effectively the exact same undiscounted price. That's what they, they
ended up getting paying in the end. And then I thought it was, you know, you're saying there's
like, I always look at the specifics of the words, right. And, you know, you guys sort of,
you didn't pull any punches here of saying that means Tesla, Tessa, Tessa's discount price from
82 bucks to 65 is fake. Like you're saying, this is, this is not a, the advertised
higher price that they were shown in order to give you this idea that you're saving money is,
I mean, it's, it's fake. It's a lot. You know, yeah. And some of the phrasing that is employed
sometimes when we're describing pricing can border on, you know, sort of
difficult to parse or academic or jargony. You know, in this case, we want to be really clear
that, you know, when you see a price that again, everyone else is already saying,
and you're being discounted to that price, that original sort of, you know, much, much higher
fare, you know, is that a true bona fide discount? You know, when we look at this particular data
set, no, it's not. And you sort of have to call it as you see it.
Cool. Yeah, I look at that because I always think, you know, in the publishing world,
we have our lawyers who look over a lot of things. And I bet you have very heavily involved lawyers
there. So, so to make, you know, that's not a, yeah, not a light statement to make, I guess.
No, no. And, you know, it's all, you know, qualified, you know, there's only certain
things we can see as journalists and as consumers, you know, we don't have access to the back end.
We can't see into the black box. So we don't know, you know, whether GPS signals or network
latency or all these other sort of factors are playing a part in some of the prices that we see.
But, you know, again, when you're looking at it from a consumer point of view,
what really matters to people is the end price, right? The net price and the promotions and
discounts that they're being shown. And that's why we did this because, you know, this of course
matters to people, especially when we're dealing with affordability and how algorithms are sort of
invading our everyday financial lives. Sure. So, so this is interesting because I feel like,
I don't know, like there's sort of not that there's another allegation, but like Uber,
I think some of the article talked about how like they don't use other factors to, you know,
basically control or into the pricing. They're basically saying it's it is purely market driven
as nothing to do with who you are or where you're going, etc. And I don't know that consumer reports
is alleging that, but it seemed like there were some articles, references in the
article about other places had how people going to was in Chicago, a nicer hotel ended up paying a
higher rate than those who didn't. I have a was talking to a colleague about this before a sort
of a conspiracy theory, just that if I'm using, you know, I have an Uber account, but I also have
a corporate Uber account. So, you know, based on for anybody in the company's traveling, and I
have to think that, you know, if I do a little test, maybe the corporate one would pay a little more
all the time, because, you know, you're just like, you know, it's like, if I'm coming back from the
airport, I'm using my corporate Uber account, does do I care that it's 10 bucks more, right? Like,
does that really like what would I balk at that? I feel like people are a little bit more relaxed
on those things. Yeah, that's a good point. So, you know, one thing that, you know, both companies
Uber and Lyft told us is that they don't personalize base prices. And that's a fine point that they're
making, right? So that's the core or the underlying price of a particular ride, but that they do
personalize promotions and discounts. And we quote a professor from UNLV, University of
Nevada, Las Vegas, who is analyzing a lot of Uber and Lyft data in that city, because it's so
critical to that city's economy, that shows that these companies are increasingly using
promotions and discounts to such a degree, you know, many multiples increase over the last few
years, that the net or the effective price of a particular service is being fundamentally altered.
And promotions and discounts are essentially becoming the new price, right? And people see a
promotion or discount, and they think, oh, I'm getting a deal. But in many cases, we're seeing
it employed at such an increasing frequency that rendering it not as important or significant
as it used to be in terms of the novelty or the true value of it. So that's all to say,
personalizing promotion and discounts, the decision between offering you one and not
offering you a promotion or discount for a given ride, that is what can be personalized based on
pretty much anything about you, right? So where you live, your billing zip code or, you know,
anything related to your ride history or your account history or things that sort of define
your account and make you different from, you know, the next person. And that's sort of where
you see the personalization aspect. And when you mentioned the other studies, yeah, we have seen
and quoted a few other studies that looked at, you know, higher prices to more expensive hotels
coming from airports or more expensive rides in non white neighborhoods in Chicago,
those are peer reviewed studies that have looked at the same issue. But that could be the net
effect of this type of personalization. Gotcha. Do you think that there's any, like, do you think
that they're potentially paying fast and loose with the idea of like, so it's not personalized,
right? Like a lot of just like, say, you know, if you're using Google or Facebook to do ad
targeting or something like that, they're like, Oh, well, it's not, you know, it's not,
we're not sharing your personal data, but they're just bulking my data into a million other people
with similar, you know, so it's like, I do think that they're actually, they're still doing it,
they're it's just not quote, personalized. So then that's another layer up, right? So, you know,
they're both acknowledging personalization on promotions and discounts saying they don't
personalize base prices. But then what do they do with base prices? Do they what you're getting at
is do they segment, do they group people, maybe small groups to 10, 15, or 100 or 1000. But do
they group people into buckets and then offer those people prices versus B or Group C or however
many groups? That is a legitimate question. And that's not something that we got a clear answer on.
When we speak to experts about it, they point the segmentation as truly being what tech companies
do. It's also, I mean, it makes a lot of, you know, both business and algorithmic sense to group,
as opposed to personalized. Personalized, it can be very expensive and can, you know,
rely on way too many factors that might not necessarily correlate to, you know, true,
you know, better margins or more sales. The segmentation, you know, you know, big picture
brute forcing it, you can actually glean a lot from the folks that buy more on Friday versus the
folks that buy more on Monday. And you might want to offer people, you know, bigger discounts on
Friday, the buy more on Friday folks. And if you can see the sales margins increase at such a,
you know, a rate, that makes good business sense, right? So that's why these companies do that.
Interesting. So what are, are Uber and Lyft saying to you about this? Like,
what, have they jumped into defend? It sounds like there was, there was some, I don't know,
seem like a bit of a weak defense on that maybe. I don't know. So they're, and we engage them
early and often on this one, we actually shared our underlying data, excuse me, with them to,
you know, really get a meaningful thoughtful response to, because it's one thing that, you know,
say or all these things, another thing to actually see the underlying data. And so when we did that,
what they said to us is that, you know, really at the end of the day, no true,
no two trips can be the same on their platforms, that when you're pricing things out, your request
or your fare that you see in the app is completely different and unique than what someone else would
see. And so it's unfair for us. Of course. And, you know, they said that our methodology was
fundamentally flawed, was their direct quote there. And they pointed to some of the backend
things that I mentioned earlier, like GPS signal or latency, things that might be, might influence
the final price that people see. But when we looked at it, when we shared that with experts,
including, you know, computer science, you know, subject matter experts who have audited Uber and
Lyft in the past, on behalf of, say, the city of San Francisco, or, you know, even former employees
of those companies, what they told us is that, you know, you are seeing differential pricing and
you are seeing what, you know, is a true price for a given route. And, you know, a lot of the
defenses are artful wording in their view. And at the end of the day, you know, nothing is barring
them from this, nothing we found ran afoul directly of federal or state law, from our
view of it or our interpretation or what we, when we, when we told that to experts and
shared our findings with them, they didn't point to a specific law that it violated.
So, you know, that's an important point. We're not alleging any sort of illegality here.
That said, you know, the differential pricing is pretty clear from what we saw.
Yeah, maybe that will be a future law, at least from some state or something, to cover some of
this, because I'm sure it will come into effect. And yeah, I mean, it was kind of
sort of unrelated in the article, but there was some discussion about how,
you know, these companies, they've profits have gone through the roof, Uber and Lyft,
and that's also due to squeezing their drivers who make less and less out of each ride, right?
So, yeah, that's a great point. You know, since 2022, both companies, their fortunes have changed
markedly. You know, Ubers, you know, profits, when you look at Cashflow or Ibida, their net profits
have increased, you know, quadrupled in six years, and Lyft has gone from a net loss to a
net profit. And that corresponds with their shift to upfront pricing, so this algorithmic
pricing that they use in the marketplace. And, you know, the net result is that our consumer
prices for Uber and Lyft have been increasing above the rate of inflation over the last several
years, according to GridWise, which is a good sort of price tracking group that releases an annual
report looking at this data, whereas the pay, you know, per mile or per fare pay for drivers has
not kept up with the increase in customer fare and is, you know, sort of around the rate of
inflation. So that means, yes, we're paying more, drivers are being paid, you know, roughly the
same. And both of these companies are profiting off of it. Yeah, I'm sure. It was funny. Actually,
one of my last rides, my Uber driver, when I got in almost right away asked me, what are you paying
for this? Like, what's your, what are you paying? And then he told me what he was getting for the
ride. And it was weird because it was, it was a tiny fraction, like it was 35% of the ride is what
he was making. But the ride also involved like a lot of like highway tolls and stuff like that. So
those presumably go to nobody other than the toll company, right? Like, I don't know if Uber charges
a bounty on top of that or not. But yeah, you know, Uber and Lyft, both, they really focus on
this calculation of how much they take in, how much the riders pay or drivers paid, and then how
much goes to government fees, tolls, airports or charges, things like that. And, you know, when we
calculate and when other, you know, academics and researchers look at this, they separate out the
government fees, and they really look at how much the platforms make and how much the drivers make
from each fare. And if you look at that, if you sort of calculate it in that way, what you're seeing
is over time, Uber and Lyft are taking greater and greater percentages of each fare. Now it's
roughly between 43 and 50% of each fare. And, you know, the driver making the rest, you know,
56 years ago, drivers were making, you know, 75 to 80% of each fare. So it is a market
change over time. And what both companies will say in defense is that, you know, at the end of the day,
you know, our net take hasn't changed. Commercial auto insurance has changed. It's an increasing
percentage of how much we're paying in order to keep the platform going. And, you know, we're not
realizing that when we get their profits go up. So clearly, yeah, and when we research, you know,
a lot of their risk is also self-insured. They own captive insurance companies that
are subsidiaries that basically, you know, price out the risk of, you know, liability and other
claims. And so, you know, but at the end of the day, Uber is bringing in this money and then paying
the expenses, right? So when we calculate, that's what we did. And we showed our work there. But
Uber and Lyft dispute that. They say that it's unfair for us or other researchers to, you know,
calculate commercial auto insurance against them. Interesting. Interesting. So I guess, I mean,
the really big question, like, where do we go from here, right? I think I saw in the article,
there's a petition to sign on the Consumer Reports website. Yeah. So we, you know,
as I mentioned earlier, we try to divide or firewall the journalism side with the policy side.
But, you know, the policy side does have, you know, active work looking at, you know, what is fair
in the marketplace and what should legislators consider when they're, you know, looking at
legislation in the space. And so what we've been told from legislators, and we'll have a forthcoming
story about this, is that they are looking at this type of issue, algorithmic pricing, and trying to
determine, you know, how to police it or how to put guardrails up. It is difficult. It is a constantly
shifting landscape. And, you know, a lot of our, you know, state consumer protection laws weren't
built for this. They were built for a time and place where you would go to a store or you would,
you know, take a ride. And it was a fixed price. And it just didn't change, you know, even with
weather and other factors, you know, at play. And now, you know, we, a lot of us can't see in the
black box, and we don't know all the factors that are being used or to calculate these things,
including things for, like, I now pay later or earned wage access, which is paid to cash advance
apps or, you know, a lot of other, you know, financial products that we use. So that's all to
say, regulators are really taking a close look at this. And we've seen now surveillance pricing
bans in two states signed into law, Maryland and Connecticut, and then two other states now
considering it in New York and New Jersey. It's passed both of their legislatures, and now it's
at their governor's desk to sign. Interesting. So is surveillance pricing, is that different
than what's happening here? Like, clearly, it's that there's a variation there. Yeah. So a quick
sort of, you know, definitions of all this algorithmic pricing is like the big umbrella of
basically any time you use an algorithm to help set a price or promotion discount that,
you know, influences the net effective price. So within that algorithmic pricing, you have dynamic
pricing, and you have surveillance pricing. Dynamic pricing is what we've seen for decades now,
which is essentially supply and demand really shifting prices and in near real time or real
time. Think about concert tickets or airline tickets or hotel reservations, things that
can quickly change depending on how many weeks or how many days out, you're buying something,
think about the World Cup, for example, those are examples of dynamic pricing. Surveillance
pricing is a term that the FTC has recently been using. And that's when a company uses your personal
data to influence the net effective price of something, right? So who you are as a customer,
your account history, your ride history, the best predictor of your future buying behavior is what
you've done in the past, right? So if you, you know, a column have been, you know, buying the same
ride on Monday at 9am for the last several weeks and taking the same route, that's a very good
predictor that you're going to maybe keep doing that or at least do that at some regular interval.
And so they might design, the algorithm might respond to that and offer you either promotions
or discounts or not for that particular ride. So that's-
You know, I reject my rates of a whole lot because I know I'm going to do it anyway.
Right. So that's an example of surveillance pricing and that's what a lot of state houses are
considering. Very interesting. Well, thanks. I will make sure to put a link to that, the article and
and also that, you know, link in the description. So we, the petition, some people can sign that
if they want to. I feel like anybody who would be listening to this would probably be interested
enough that they want to sign a petition to at least, you know, look a little bit more into this
and what the, you know, the powers that be and their algorithms are doing to control our lives
and our pocket books. Yeah, fair enough. Well, thank you for having me and talking through all
these things. No problem. Have a great day. Thanks, YouTube. Bye.
About this episode
Ride-share discounts may be misleading, and the show walks through Consumer Reports’ investigation into “fictitious pricing” and “fake discounts.” Hosts connect that to how Uber and Lyft use algorithmic, upfront, and surge pricing—sometimes varying fares for the same trip at the same time. They also discuss controlled testing, targeted promotions, and the “black box” behind app pricing. The conversation closes with how regulators are responding, including “surveillance pricing” bans.
Ever feel like Uber or Lyft is charging you way more than your friend for the exact same trip? You’re not crazy and we have the data to prove it.On this episode of The AutoGuide Show, we sit down with Consumer Reports investigative reporter Derek Kravitz to unpack a groundbreaking study on rideshare pricing. We dive into how AI might be personalizing your fares, why those "advertised discounts" might actually be fake, and why two people standing side-by-side can see a 50% price difference for the same ride.If you’re tired of feeling ripped off, Consumer Reports is pushing regulators to step in and stop these tactics.SIGN THE PETITION & READ THE STUDSign Consumer Reports' Petition: https://action.consumerreports.org/cro-20260616-mtpr-uberlyftRead the full investigative report: https://www.consumerreports.org/money/questionable-business-practices/uber-lyft-different-prices-for-same-ride-and-fake-discounts-a1093538909/?EXTKEY=YSOCIAL_LI
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