Is Dynamic Pricing A Hit? by [email protected]

Wharton’s Peter Fader and Senthil Veeraraghavan discuss their research on dynamic pricing.

Dynamic pricing, a strategy that allows businesses to change their prices based on demand for their products, earned notoriety among consumers when ride-sharing platform Uber used it to dramatically increase prices during surge times. Despite the customer dislike for dynamic pricing, researchers are finding out that it can be an effective tool for many businesses that do not experience equilibrium when it comes to demand.

Peter Fader, marketing professor at Wharton, and Senthil Veeraraghavan, a Wharton professor of operations, information and decisions, stopped by [email protected] to talk about their new paper on the topic,The Revenue Impact of Dynamic Pricing Policies in Major League Baseball Ticket Sales.”

[email protected]: Could you give us a brief summary of the paper?

Peter Fader: In Major League Baseball, in professional sports, in the entertainment world, in general, there’s been this great awakening that you actually have to care about the business side of the business. It’s not just a matter of putting the best players on the field or the best performers onstage. A big part of that is pricing. For years and years, all these different companies have come up with arbitrary prices and relied on secondary markets to reach the right equilibrium. It’s great to see that a lot of Major League Baseball clubs, among others in this general area, are finally getting smart and saying, “We want to take control of this. We want to set the right prices.” Part of that means dynamic pricing. Part of that means adjusting the prices over time, some charging different prices to different people, depending on the nature of the game and so on. A lot of clubs have been just trying it out, but not a lot of them have stepped back to say, “Is it working? Can we do it better? What’s the incremental impact of one kind of pricing policy or another?”

We were very fortunate to be able to work with a club that was asking those kinds of questions. Through a really clever data set and some pretty clever modeling, I think we came up with some pretty clever answers.

Senthil Veeraraghavan: It’s a very interesting problem. It’s a confluence of exciting, cutting-edge research with a practical application that goes directly onto the field, literally in this case.

[email protected]: What are the key takeaways from this paper?

Veeraraghavan: One of the things that we found out is people talk a lot about dynamic pricing [in terms of] customer response to dynamic pricing. We were really surprised how well a well-chosen static price did. That’s one of our surprising findings.

Fader: Of course, there’s the flip side to it, which is dynamic pricing isn’t a panacea. Just because you are varying the prices doesn’t mean you’re necessarily making more money. In this particular case, if we look at the dynamic pricing policy that this one club followed, at this one portion of a season, they actually lost money relative to the static policy that they had the beginning. In some cases, if we can pick the “just right” prices, why even bother changing things at all?

[email protected]: But isn’t picking the “just right” price easier said than done?

Veeraraghavan: That is true. If there is a magic bullet, so to speak, that’s distributable, we would be able to do that, right? I think context-specific application is very important here. It’s a relationship between the customers you want to serve and the team you want to run and the organization you want to run. In this case, that data is useful to understand what kind of policies would work. Yeah, we can improve, but it’s very specific. Data has that information. They can come with the data into good pricing policies.

“It’s been an education just to think about the different ways that we can go out there with dynamic pricing policies.”–Peter Fader

Fader: I think that’s why it’s such a great collaboration over here, because I do spend my time thinking about the relationships and thinking about all the nuances of where demand comes from. And Senthil and his colleagues spend a lot of time thinking about optimizing. Very often, each of us doesn’t do justice to the other side. I will build these really great, descriptive models, but then I fall short on the, “so what?” And very often, folks in the optimization world will build overly simplistic models because it enhances the optimization part. This is that just-right combination where we have really a rich description of how people are buying tickets, when and for what sections, and what are they willing to pay for it. It’s a nice story about customer behavior, but it lends itself pretty well to the optimization, as well.

[email protected]: The paper shows that there are a lot of different ways that a baseball team could do dynamic pricing.

Fader: We talk about it at a couple of different levels. A lot of it would be the factors that should be taken into account when deriving a dynamic pricing policy. It’s great that this is not an academic exercise, that a lot of professional sports teams and other kinds of businesses are starting to take those factors into account. One is on the input side: What factors should we be looking at and how do we adjust for them? On the output side in terms of setting the policies — and this is Senthil’s expertise — should we be looking ahead or not? To me, it’s been an education just to think about the different ways that we can go out there with policies.

Veeraraghavan: We are all learning different things from this problem. As we talked about, it’s very cross-disciplinary. One of the things is how far do you look ahead when you set your dynamic pricing policies? Do you look ahead 10 games, three games? How often do you change? How do you communicate that? These things matter, and these things are just customer responses, and that’s going to feed back into the policies that you’re going to come up with.

People think of dynamic pricing as an evil or a panacea. The truth is somewhere in the middle. You can implement the same solution and do it poorly because of how you use information. For example, we found out if there are more people in the group that is buying tickets, they are more likely to buy from certain sections of the stadium, better sections of the stadium. That is surprising to me. As a marketing person, maybe that’s not immediately surprising. But that’s not what the model usually assumes…. How many games should you look at? Should you look at your opponents? Should you look at the day of the game, the weather? All of these things matter.

[email protected]: Should you revisit if the team has a bad streak?

Veeraraghavan: Yes. And when you go into a bad streak, you cannot be charging too much, so you have to think about revising prices downwards.

[email protected]: Often in April, the media or others will predict can that this or that team is going to the World Series. And sometimes that happens, but

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