Burning Man sold out for the first time ever this year. Every year, people put off their ticket purchases until the last minute because going to The Burn is such a significant investment of time and resources.
Those who could not commit early were left out, including some of the prominent DJ’s who are known for performing on the playa. Many are upset, blaming scalpers who violate the Burning Man ethos with their unabashedly exploitative profit motive.
The Burning Man organization insists it has taken sufficient precautions to prevent scalpers from cornering the market, but why should they have to implement such special measures (whatever they are) to begin with?
Scalpers are a sign of an inefficient market. Instead of limiting the number of tickets a person can purchase at one time based on their address, credit card, or retinal scan (yes, I’m being hyperbolic) can’t we design a more efficient market for event tickets that will remove any profit opportunity for the scalpers?
Let’s be honest, scalpers are adding no value to the transaction. These aren’t brokers facilitating price discovery and connecting disparate pools of liquidity the way Wall Street is supposed to function. They are exploiting the system for personal gain (the way Wall Street frequently actually does function).
Ticketmaster is catching on, but I don’t like the notion of prices actually going up. Other examples seem like people are trying to apply the airline model to a new context, and I think that’s well intentioned but wrongheaded. Prices start low, then gradually increase. That makes sense when you are trying to differentiate between business travelers, who tend to book last minute with a higher willingness to pay, and casual or recreational travelers.
Allow me to offer what I think would be a better approach, better for fans and better at undermining the scalper’s business model. I’ll describe my approach in the context of music concerts because those are the kinds of events nearest and dearest to me and happen to be in an industry going through a significant period of disruption.
First of all, start prices high and work your way down, not the other way around, which only encourages scalpers to scoop up tickets fast and early. When tickets first go on sale, diehard fans are only other ones looking to buy as quickly as possible. They don’t want to wait to coordinate with a bunch of people or see what else is going on that weekend. Diehard fans will have the highest willingness to pay, and they are willing to accept the risk that plans will fall through for the option value of having a ticket in hand if the event sells out. I’ll address why starting high and working your way down is NOT exploiting your biggest fans.
Scalpers are only going to be willing to pay up to the highest price they can charge a fan. By starting high, you actually take those fans out of the market so the scalper has little to no opportunity to make a profit from reselling tickets. Let me explain why starting with how this dutch auction style of dynamic pricing would work.
Assume for a minute that the music industry has good data on concert ticket sales. You want to chart ticket sales over the time period they are on sale and run some statistical analysis to construct a model that will predict how many tickets you are likely to have sold on any given day prior to the actual concert. (Note: I only know enough statistics to be dangerous. I also know enough from working with algorithmic traders and casually reading books like Supercrunchers to realize that there are quantitative minds out there much better equipped to work out the actual model that I am about to propose. What follows is an elementary model and easy enough for me to describe without working out the actual details of a quantitative model, but I am entirely convinced it is possible to model ticket sales and surely far less complex than the modeling done for fluid dynamics and climatology.)
The model will ultimately be a function of a number of variables, including the artist album sales in the last year, lifetime album sales, current place in the charts, population density of the area, venue capacity, time remaining until the concert, date and ticket price, and so on. A better model will take into account demographic information about the artist’s fans since the effects of these variable is likely to bias the other variables if omitted.
In any event, the model will be an approximation of the expected behavior of people, an element of complexity that will render even the best model incapable of perfect prediction. The model merely provides guide posts, some estimates for how many tickets you can expect to sell over a given time period and in total at a given price point (controlling for other explanatory variables of course).
Total estimated ticket sales over a series of price points will allow you to perform some portfolio optimization calculations about the right number of tickets to offer in each price tranche to ultimately sell out the venue while maximizing revenue (everyone prefers performing to a full house). These values will be used to decide when to adjust prices downward under the dynamic pricing scheme I’ll continue to describe now.
Starting at a price of P1, you should expect to sell a quantity of Q1 tickets by day T1 (all the value coming from the model). If day T1 arrives and you have not sold the quantity expected (within some range of tolerance), decrease the price by a predetermined increment, based on the demand elasticity predicted by the model, to get back on your “sell out” trajectory. Hold the prices there until day T2, at which time you should have sold Q2 tickets (according to the model). If still you have not, decrease the price by a larger increment to accelerate sales.
Backing up now, if on T1 you have sold significantly more than Q1 (outside the band of tolerance) then you keep the price where it is until you reach a time, Tn, when the quantity sold comes back into line with what the model predicted.
The purchasing decision of each fan is going to be a combination of their willingness to pay (“How much do I really want to see this show?”), their disposable income (which actually has an indirect effect by way of willingness to pay), and (this one is important) their estimation of everyone else’s willingness to pay. I may be willing to pay $50, but if I think everyone else is only interested in paying $25, I will wait until prices get closer to this sell out price point before buying. In effect, you are using the wisdom of crowds to set price. All the elements are there: diversity of opinion, independence (outside of your small coterie of friends), decentralization, and aggregation.
A few people will buy high because their willingness to pay outweighs their estimates of others or because they just got their estimates wrong, but most are going to buy as soon as it hits the market clearing price – a sold out concert. A few people may wait until it is too late to buy because they have a lower willingness to pay or they are just trying to game the system for a lower price; it won’t be because scalpers have artificially constrained supply. For the majority, the porridge will be just right.
A scalper’s ability to make a profit would depend wholly on the ability to predict that equilibrium price, an impressive feat. Buy too high, and the scalper will have to sell at a loss in most cases since the only buyers left are the ones that just waited too long (lower willingness to pay). Buy too low, well, you can’t. The concert has sold out and there are no tickets left for the scalper unless he wants to stand outside with a sign saying “Tickets needed.” (The argument could be made those “scalpers” actually are providing a service for people left holding unused tickets.)
A more efficient market means not only do fans get a better price, but artists, athletes, and performers, along with the hosting venues, promoters and ticketing agents, all get a better return on their contribution to the value chain. No one is being exploited because when you aren’t dealing with life’s essentials, a fair price is whatever the buyer and seller agree to (it’s called capitalism). Without the scalpers taking a cut, there is more left value to go around. The total revenues can be pooled and then split according to an agreed payment schedule amongst everyone so everyone is similarly incentivized to maximize total revenues. (The design of the payment schedule is another lengthy discussion and will be subject to lengthier negotiations, but the last point about incentives is what’s most important.)
For the fans that just don’t have the funds to buy early, this is an opportunity for the artist to build brand loyalty. Allocate a tranche of tickets for your biggest fans. Give them a redemption code with each album purchase worth one ticket pre-sale at a discounted price. Forget the stick; offer a carrot for rejecting piracy. The discount offered does not have to be extreme; just whatever seems reasonable to the artist for rewarding fans.
If you are a quant and think you can improve on the above, I’d love to hear about it. Reach out on my about.me page or comment below.
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