Bruce Springsteen fans were appalled last month, when Ticketmaster sold some seats for his upcoming concert tour for as much as $5,000. The average price was actually around $262, according to Ticketmaster. But for a certain subset of tickets, when demand spiked, so did the prices.
Ticketmaster uses “dynamic pricing algorithms,” which calculate the price of a seat based on real-time demand. Among other things, it’s an attempt to edge out ticket scalpers.
There’s been backlash against this before. Ride-hailing apps like Uber use this kind of algorithm too; it’s called surge pricing. And it has kicked in during emergencies like a terrorist attack and a mass shooting — prices went through the roof as people were trying to flee.
Marielle Segarra of “Marketplace Tech” spoke with Vivek Farias, a professor at the Massachusetts Institute of Technology’s Sloan School of Management, about the concept behind dynamic pricing and how to potentially avoid letting an algorithm create situations like this. The following is an edited transcript of their conversation.
Vivek Farias: Let’s say I’m selling tickets, like the ones for the Boss. I only have so many tickets to go around, I want to get as much as I can, you know, for it. But, you know, I don’t necessarily know, you know, in advance exactly how much I’m gonna sell or how much people are willing to pay. I have a limited supply of something, I want to give it to the people that value it most. And so the quote unquote dynamic pricing algorithm comes in, looking at that initial demand or the demand as we sort of go along, and discerning from it, you know, exactly where demand is going to lie.
Marielle Segarra: Are there interventions in dynamic pricing algorithms like ways a human can intervene using their judgment?
Farias: So given all the noise around the Ticketmaster thing, there is going to be a lot of human oversight over what the algorithm is doing. I think, absent that oversight, the algorithm is going to do what the algorithm does. And frankly, you know, that’s kind of the story of Uber and Lyft. In the early days of price surges, there was a lot of noise around what was happening. And then they stepped in to say, wait a second, like, maybe we need to actually, you know, have some controls over here because there are larger considerations.
Segarra: It’s interesting that the framework for it is that we’re getting this product to people who “value it the most.” I don’t know if that’s exactly right. You know, I bet you somebody who’s, like, a huge Springsteen fan but just simply doesn’t have the money to pay 4 grand because that’s, like, three months’ rent for them or whatever, they might really value that concert more than the person who can afford to just drop the money, like, no big deal. Do you know what I mean?
Farias: I totally agree with you. Maybe one kind of very productive way of approaching this is to sort of look at this from the perspective of maybe we ought to actually be thinking about, you know, the long-term value, right? So I think in Ticketmaster’s case, you’re actively tracking whether this person is a, you know, quote unquote, fan or not. But it’s not clear to me that you’re actually tracking that from the perspective of maximizing long-term value. I think you’re tracking that, you know, from the perspective of, I don’t want to sell tickets to a scalper. I think you turn that around and say, you know, what’s the long-term value of this fan? This might lead to kind of a more productive paradigm where fans don’t feel shortchanged.
Related links: More insight from Marielle Segarra
Farias and I talked about dynamic pricing algorithms as a way to match a good or service — in this case, concert tickets — with the people who value it the most. And like I said, once prices reach a certain point, say, $5,000, I’m not sure that willingness to pay is a good measure of how much a person values something.
But the other question I had was, is that the right rubric anyway? As a society, should we allocate scarce resources based on who values them the most or who needs them the most? What about who would enjoy them the most?
It probably depends on what the scarce resource is, right? Like Farias says, if it’s a kidney, yeah, money shouldn’t come into the equation. And the registries that decide these things weigh other factors.
There is, in fact, a metric called QALYs, quality-adjusted life years, that ends up forming one rubric for thinking about, hey, who should the kidney go to? It’s certainly not the person that’s willing to pay the most for it, right? And then some of the other [metrics] when we talk about kidneys, it feels so obvious. I don’t need to convince you that I shouldn’t be looking at dollars, right? But all of a sudden, I go from kidneys to looking at, you know, tickets to a concert. You know, the story changes.Vivek Farias, MIT
We also talked about the business case for and against this kind of dynamic pricing. It may mean Ticketmaster and Bruce Springsteen make more money on a concert. But it could also alienate fans of Springsteen, whose music does have a pretty working-class ethos.
But if you want to get a larger scope of this issue, Harvard Business Review has an interesting article about the pitfalls of dynamic pricing.
Farias pointed out that these algorithms can be good for consumers. Think about Uber — surge pricing might incentivize drivers to come out during a thunderstorm. That brings more supply to the market. So maybe you’ll get a ride faster or get a ride, period.
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