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we're here today with Wharton marketing professor peter fader and wharton professor of operations information and
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decisions senthil Vera Gavin to talk about their new paper which discusses dynamic pricing of major league baseball
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tickets thanks for being here great to be with you Rachel so first off could you guys talk to us a little bit give us
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a brief summary of the paper what did you study and what were you looking at so in a major league baseball in
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professional sports in the entertainment world in general there's been this this
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Great Awakening that the action take care about the business side of the business it's not just a matter putting
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the best players on the field or the best performers on stage and of course a big part of that is pricing so for years
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and years again all these different kinds of companies have just come up with arbitrary prices and they've kind
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of relied on secondary markets in order to kind of reach the white the right equilibrium well it's great to see that
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a lot of major league baseball clubs among others in this this general area are finally getting smart and saying we
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want to take control of this we want to set the right prices and part of that means dynamic pricing part of that means
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adjusting the price is over time some charging different prices different people depending on the nature of the
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game and so on so a lot of clubs have been just trying it out but not a lot of them have actually stepped back to say
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is it working can we do it better what's the incremental impact of one kind of
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pricing policy or another so we were very fortunate to be able to work with a club that was asking those kinds of
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questions and through it just a really clever data set and some pretty clever modeling I think we came up with some
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pretty good answers and some tell you everything dad it's a it's a very interesting problem it's a confluence of
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exciting cutting-edge research with a practical application that goes directly onto the field literally in this case
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right so and now so can you guys talk to us a little bit about you found some interesting key takeaways from this
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paper a bit might surprise a lot of people who are out there buying baseball tickets or maybe not one of the things
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that we found out is people talk a lot about dynamic pricing it has a lot of customer response to dynamic pricing a
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well-chosen price even if it's static just as well that's one thing we learned
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from this project which we had some inkling about but we were really surprised how well a
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well-chosen static price dead that's one of our surprising findings and of course
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there's the flip side to it as well which is dynamic pricing isn't a panacea
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just because you're varying the prices doesn't mean you're necessarily making
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more money and in fact in this particular case if we look at the dynamic pricing policy at this one club
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followed over this one portion of a season they actually they actually lost money I relative to to the the static
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policy that they had at the beginning so it's it's funny that just right balance
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between what should be dynamic and essential said in some cases we can pick the just right prices why even bother
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changing things at all now but i would think that picking the just right price is easier said than done that is true if
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if there is a magic bullet so to speak that's distributable we would be able to
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do that right I think context specific application is very important here it's
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it's a relationship as bit often talks about between the customers you want to
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serve and the team you wanna run and the organization one around and in this case
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that data is useful to understand what kind of policies would work yeah we can improve but it's very specific data has
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that information they can convert the data into good pricing policies and I think that's why it's such a great
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collaboration over here because I do spend my time thinking about the relationships and thinking about all the
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nuances of where demand comes from and sentio and his colleagues in our oh I d department it's been about I'm thinking
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about optimizing and very often each of us doesn't do justice to the other side
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so our build these really great descriptive models but I fall short on the so what and very often folks in the
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optimization world will build overly simplistic models because it enhances the optimization part this is that just
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right combination we have a really rich description of how people are buying tickets when and for what sections and
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what are they willing to pay for so it's a nice story about customer behavior but
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it lends itself pretty well to the optimization as well that's right as I said it's a confluence of how people
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behave and what models you can run in the background and how we can bring it to business applications today yeah and now
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you guys had some pretty interesting ways like one thing that I think really comes out in the papers there's lots of
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different ways that a baseball team in particular and probably really anyone that wants to do this good dynamic price
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I mean so you talked about the seat and you talked about how well the team is doing and a bunch of different ways to
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do this so we talked about it a couple of different levels so as you just mentioned Rachel a lot of it would be
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the factors that should be taken into account when deriving a dynamic pricing policy and you just mentioned a few of
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them there and it's great that and that's not an academic exercise that a
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lot of professional sports teams and and and other kinds of businesses are starting to take those factors into
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account so one is on the input side what factors should we be looking at and how
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to adjust for them but then there's on the kind of on the output side in terms
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of setting the policies and again this is sent those expertise about you know should we be looking ahead or not well
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anyway you can talk more about the nature of the different kinds of pricing policies and to me it's been an
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education just to think about the different ways that we can go out there with policies I mean we're we're all
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learning different things from this problem as we talked about it's very class disciplinary and one of the things
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is how far do you look ahead when you said your dynamic pricing policies do you look ahead ten games three games how
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often do you change how do you communicate that and these things matter and these things are just customer
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responses and that's going to feed back into the policies that you're going to
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come up with so people think of dynamic pricing as an evil or a panacea we talked about it the truth is somewhere
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in the middle it's you can go to the same solution and do it poorly because of how you use the information so in
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this case before example found out if there is more people in the group that's
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buying tickets they are more likely to buy from certain sections of the stadium and in my batter sections of this cherry
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that are surprising to me as a marketing person maybe that's not immediately
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surprising but that's not what the models usually assume so it becomes relevant to understand your customers
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to take the data from your from your understanding of the problem and apply it and start thinking about dynamic
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pricing so how many games you should look at should you look at your opponents should you look at the day of
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the game the weather all these things models did you revisit if they go into a bad streak yeah when you go into a bad
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streak then you cannot be charging too high so you have to think about revising prices downloads I mean just kind of
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think about sometimes there's a team that could be you know they could tell they could say in April well this is the
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team that's going to the World Series or the Super Bowl or whatever you want to
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say but then by September they've tanked well that's actually one of the issues
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that happened in this case so actually the whole setting of the model is kind of interesting that this particular
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major league baseball team didn't use dynamic pricing for the first half of the season so we were able to calibrate
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all of our demand and response models based on what we see for you know the first 40 games or so and then we can
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project what would happen in the latter part of the season if they didn't introduce dynamic pricing so we have
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this really good baseline about what sales would have been and we look at the difference in order to say how well did
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the dynamic pricing work well we didn't know it at the time but the team did not
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do well in the second part of the season they they went way way way down and so it's not so much that we can blame the
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dynamic pricing policy but the team went into an area that we hadn't observed in
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the first part of the data set so you know that's just the nature of building
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a model and kind of rolling with it that sometimes things will happen out there that you can control you can anticipate
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right now tell me a little bit so if im a im another major league baseball team or even a minor league baseball team or
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anyone where this could be anytime soon this could be applied any number of ways
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i know you've done something with some things with like concert tickets things
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like that Peter my boys are now nonprofits which I looked at it too that's exactly I mean you you are a
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baseball team you know mindedly clean you're an international team you know a
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soccer team cricket team or or even a non-profit arts organization you have already the data that can tell you what
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how you can think about this problem where we come in and tell you is this information is very key it's important
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and this is how we can use this information and I just shared policies so on one
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hand the I think the the framework that we've built is fairly general because
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we're using pretty common set of factors and data structures and so on but the
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specific results I'm not sure that we'd want to generalize from those I think
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the particular weight that different factors might have the relative differences between different kinds of
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pricing policies that is going to be context specific but I am pretty confident that if we took the overall
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model and then recalibrate it in another setting it would continue to work well maybe with just slightly different
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results what I'm wondering so if I'm REE if I'm an organization i'm looking at
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this how might i practically apply the research part of it is just gaining the overall understanding so before we even
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worry about specific estimated coefficients or any of the numbers that are in the paper as we've said before
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just thinking about what are the factors that you'd want to take into account and
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operationally can you create a situation again that we were fortunate to have here where we have this control
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condition where there was no dynamic pricing going on so we get a clean read so once we kind of press that button and
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start it we can understand what's happening what's attributable to it so
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there's things both in terms of what we put into the model as well as just the
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overall way that it played out in practice that that can be generalized and I hope that folks would focus more
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on on those kind of operational factors more than specific results arising from it that's very important point actually
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a lot of times people say yeah you could do this but what if it happens what if something else happens and by doing this
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kind of experimental data base study you're able to answer those questions using counterfactual ti what if the team
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got on a hot streak this is what you should be doing what if the show is so successful or what if the lead actor
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leaves what do you do and we can have answers to these kind of questions using the model applications and as pitted the
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actual results may vary from case to case but the fundamental application remains the same now is there a story in
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the media that you guys think really relates to this research that really kind of puts it into a new light door
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relates to it in general I think there's a not only a current story but an ongoing
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story as I said at the outset justice all this this consternation among professional sports organizations about
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the existence of and the profitability of secondary markets so a lot of this project arose from a large-scale project
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I had done with Major League Baseball I was very pleased that one of the clubs wanted to go further with it in that
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project with MLB the questioners should they continue to use stubhub as their exclusive secondary ticket provider and
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most of the teams agree to do so some of the teams opted out including the New York Yankees and just this week the
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Yankees change their policy on that and they're they're coming back to step up
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albeit with conditions but it's but the point is that if this this whole situation is far from settled so you
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have a lot of teams that are doing the kinds of modeling experiments like we see going on over here and as a whole
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it's it's going to be just a lot more changes taking place we hope that it's
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not just trial and error we hope that it's going to be the club's include and
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the secondary ticket providers are just getting smarter and and and actually in some sense reducing the opportunities
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for arbitrage and some of the game playing that goes on just by anticipating what the right prices will
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be and hallo very under different kinds of circumstances yeah pretty much an ugly bunted that I mean do you feel like
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the secondary markets have really put a lot of pressure on the teams or on concert I Ranh Bay Aaron concert
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promoters or anybody just to really look more closely at this from their perspective just because they know the
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secondary market is out there and it's something that's very relevant to anyone
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who's buying tickets in a way the existence of the secondary markets has and the success of them has been a great
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wake-up call to these organizations the fact that they're now putting so many
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resources internally into doing the kind of modeling and experimentation that were endorsing here it used to be that
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they would take all the analytic talent and save it for the money ball people let's go out there and just find the
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best players but now they want to find the best analysts who can help them sell tickets at the right prices to the right
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people it's good to see that there dare I say leveling the playing field exactly
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i mean at some level they owned the content as we speak right they own the content they can control
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the content they can they understood that concert better so who else right can benefit more from these kind of
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pricing adjustments than the content providers themselves right i mean you can you can get the best players but you
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still have to get butts on the seat now do you feel like there are i think i mean i think there's a lot of views
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about Dana epidemic pricing out there you've got people to think it's the
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worst thing in the world and people think that's the best thing ever I mean do you think there's other
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misconceptions that this research kind of dispels one of the things that again we going back to what we got surprised
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by is you could do very well with the well set static pricing again well set that is a hard problem so dynamic
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pricing gives you flexibility to adjust and this is very important it'll actually can help a team to reach out as
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fans and consumers to it's not necessarily the bad thing for both the team and the facts under consider that
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might be the biggest revelation not just from this one study but from this this broader project that I was involved with
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and when I first started talking to Major League Baseball they were very reluctant a lot of clouds are very
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reluctant to try dynamic pricing it seemed first of all scary it's unknown a
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second they worried about some kind of backlash that fans would feel that they're being gouged and so on it's been
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great to see how well-received dynamic pricing has been the clubs have been careful about it and so they have an
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overreach there's been very few stories about any kind of backlash and from the
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fan side we're getting used to it we're understanding that the what we see
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whether it's with with airplanes now with professional sports when it comes to a ride-sharing services that that
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dynamic pricing is here to stay and as long as companies are doing it in a smart way and as long as they have the
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long run in mind and not trying to squeeze as many dollars they can right now it really can be in everywhere in
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everybody's best interest now what's next for this research where are you
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going to go next with it lots of direction is actually I mean we we have interest from many places so we're
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actually we just hope there is more time to do all this stuff and and better data too we were very
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fortunate to get that the data that this one club provided but there's so much
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more than they could provide so there's other sources of revenue such as concessions and merchandise that they're
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selling there can be other sources of customer behavior in this case we didn't
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have full granularity to track exactly which household was buying which ticket at which time so we could link that
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together to come up with a better notion of what the true demand is so in many ways this is the this is the tip of the
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iceberg this is the top of the first inning I think it's going to be a long game ahead for understanding the role of
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dynamic pricing in this setting and so many others well I guess we'll see you
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back for your next to your next base hit so sex L Peter thank for thank you so much for being to Magnus
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you