Ticker: Dutch Auctions With A Money-Back Guarantee Sandeep Baliga and Jeff Ely

Transcription

1 Results Ticker: Dutch Auctions With A Money-Back Guarantee Sandeep Baliga and Jeff Ely

2 2 Concept The Problem A venue like a stadium, a theatre, or an orchestra has a fixed capacity of seats in different locations. All venues care about revenue. They may also care about attendance. All venues face a fundamental choice: What price should they set for seats in a certain category? What will the market bear? How will the price affect attendance? The most common solution is to set a fixed price before the event or the season starts. This involves guessing what demand will be well before the event and hoping to hit the right spot. A more sophisticated solution is to use dynamic pricing where the price is adjusted daily to reflect the current state of demand and the remaining inventory of seats. Both solutions have their problems. The fixed price can be too low or too high. Also, the most a venue can learn from setting a fixed price is what sales are for that one price. It cannot learn that sales would have been if they charged more or less. So, the data does not give a great deal of help in fine-tuning pricing for the future. In particular, even if the venue sells out, they cannot tell if they could have charged a higher price and still had it sell out. For example, suppose a venue has 5000 seats to sell in some category. They set a price of $70. That price could turn out to be too low if there turns out to be high demand. Then we are in a situation depicted in Figure 1 with a sellout with excess demand for an event. The demand curve shows what sales would be at different prices. It is downward-sloping as sales are higher at lower prices. The supply is simply the vertical line at the stock of 1000 seats the venue has for sale. If the venue had known the demand curve in advance they would have set the market clearing price and sold out all their capacity. But without knowing the demand curve, and having seen a sell out at $70, it is impossible to know the highest sell-out price. 2

3 3 Concept $/Buyer" Figure"1:"Excess"Demand" Market"Clearing"Price" 70" Capacity" "5000" 8000" Sales" 1" The reverse situation is depicted in Figure 2. This is a non-sellout event and there is excess supply at a price of $70. A lower price would definitely increase attendance. Whether it increases revenue depends on the sales response. If sales grow substantially in response to a lower price, revenue will increase. Is sales do change much in response to price changes, then it might actually make sense to raise prices from a revenue standpoint. Attendance will fall but revenue will rise. The best course of action depends on the venues objectives attendance or revenue. But without knowing the demand curve, the venue cannot make that decision in an informed way. 3

4 4 Concept $/Buyer" Figure"2:"Excess"Supply" 70" Market"Clearing"Price" 3000" Capacity" "5000" Sales" 2" Dynamic pricing tries to deal with the fact that information about demand is poor at the time when prices are set by allowing prices to vary over time. The idea is to set prices on the fly as data is revealed about sales at different prices. There are a number of problems associated with dynamic pricing. If buyers think prices might go down, they game the system by waiting. With sales slacking off, dynamic pricing does end up lowering the price and consumers willing to pay high prices end up capturing value. Some sports franchises have dealt with this by declaring that they will only raise prices not lower them. But this creates two further issues. First, consumers willing to pay high prices now just buy immediately and again value is left on the table by the seller. Second, the seller faces the issue of what price at which to start the whole process. This is basically the same issue we discussed before for a seller deciding what fixed price to set. The risk is that the initial price is too high and having committed not to lower prices, the venue is now stuck with low demand. To compensate for this, the obvious strategy is to lower the starting price. But this just leaves even more value on the table because buyers get an even better deal. 4

5 5 Concept The Solution To set prices, sellers need more information about the demand curve. One way to discover the demand curve is to progressively lower prices and track sales. This is the first key part of Ticker. This is a variation of the so-called Dutch auction that has been used for hundreds of years to sell flowers in the Netherlands. The potential problem with this approach is the same as with dynamic pricing: buyers will wait to buy till the price falls. Not only will this reduce revenue but also we will not learn the demand curve as buyers purchasing behavior does not reflect their true willingness to pay to attend the game. The second key part of Ticker is the Ticker Pledge: fans are refunded the difference between the price they paid when they purchased and the final price of the event. The Ticker Pledge means that buyers have the incentive to be honest. This helps to simultaneously identify the demand curve and to set prices on the fly. In a Dutch auction with a Ticker Pledge, if a ticket is worth $100 to a fan, he should not wait to buy till the price falls to $50. The auction may simply stop at $70 and he would miss going to the event. Had the fan just bought at $100, he would still only have paid $70 because of the Ticker Pledge. Also, we bundle seats of differing quality into the same category so the earlier someone buys, the better the selection of seats. In other words, because a buyer will only end up paying the lowest winning price anyway, and he gets better seats the earlier he buys, he has no incentive to bid lower than what it is actually worth to him: it s in his best interest to be completely honest. The third part of Ticker is a bidding feature. We allow potential buyers for whom the current price is too high to record bids at lower prices. If the current price is lowered to their bid, it is activated and they are put at the front of the queue to buy tickets and get the best available seats. Bidders are also protected by the Ticker Pledge so if the price falls further they get a refund too. So, the bidders also have a good reason to bid what a ticket is truly worth to them. Once buyers are honest, we can get a much more accurate picture of the demand curve as sales truly reflect willingness to pay. Only two questions need to be answered: What price should Ticker start at? And when should we stop lowering prices? To prime Ticker, we can look at historical information and data from secondary market sales. For sporting events, this is straightforward because of websites like StubHub. Websites like Craigslist are a good source of information for sales in general. Once we have a picture of what sales were like in the past, we can start Ticker at a price that is substantially higher than where we might end up so we do not run the risk of underpricing the event. As we are lowering prices anyway, there is no cost to overpricing to begin Ticker. If no historical information is available we can use the bidding feature to generate data (this is described in more detail in a section that follows on Season ticket sales). To end Ticker, we employ an algorithm 1 that takes into account: The objective of the venue in terms of revenue versus attendance. The target date set by the venue for Ticker to end. This could be at the date of the event or earlier, whatever the venue decides. 1 The Ticker algorithms are patent-pending. 5

6 6 Concept The data that is generated as we lower prices. Importantly, the rate at which sales are being made is a key component in our strategy for lowering prices. The data generated by the bids. Using our algorithm for high demand events, we can find the highest price for which the venue sells out there is no risk of underpricing. For lower demand events, we can find the tradeoff between revenue and attendance so the venue can make an informed decision of how low to go there is no risk of overpricing. 6

7 7 Concept What Can Ticker Be Used For? 1. Season Tickets Before a season begins for a symphony orchestra or a baseball team, they have to decide what price to set for different categories of season tickets. Currently, they just guess the price, perhaps marking it up by some percentage over the previous year. One simple solution is to have a pre-season Ticker auction with the Ticker Pledge in place say a month before the season begins. The only new issue this raises is how to set the opening price since secondary markets for these tickets are not yet in operation. To generate information, we can use the bidding feature in Ticker. A week before the pre-season Ticker auction begins, we allow potential buyers to bid for seats. Once the bids are in, we set the opening season ticket price. The Ticker Pledge applies here too and all winning bidders pay the opening price even if they bid more. All winning bidders are ordered randomly and then given best available seats in that order. Again, this gets rid of gaming effects so everyone has the incentive to bid the maximum price they would be willing to pay to attend the event. We can use this information to determine the opening price. If further price cuts are necessary to sell inventory, the Ticker Pledge still applies and everyone is given the appropriate refund. In fact, even the bids of the losing bidders in the pre-season bidding are activated if the current price should fall to their bid. So, in summary, pre-season bidding for a week followed by a Ticker auction with the Ticker Pledge for a month can be used to sell season tickets. 2. Variable Pricing Some concerts or games are more popular than others. Typically, the venue has set different prices for different events, again by guessing what the market might bear. But this has all the problems we have already discussed. Much the same as for season tickets, we can use a Ticker auction to set prices correctly. Hence, we take this opportunity of explaining another method of implementing the refund this method could also be used for selling season tickets. Again we allow people to bid for tickets but this time we charge them a minimum fee, not their actual bid. The minimum fee would be kept as low as possible to maximize bidding and would be used just to verify the credit card. For example, someone might bid $150 but their card is just charged $1 initially. At the end of the bidding period, suppose the final price ends up being $100. At that point all successful bidders are charged an extra $99. The advantage of this system is that all bidders are charged the same amount in one big batch. So, the bidder in our example gets a refund of $50 but in actuality the refund is achieved via a final charge not a refund. If bidders are charged their initial bid, the refunds have to be personalized so the bidder in our example gets refunded $50 while another who bid $200 gets a refund of $100. This requires more complex programming while the charge at the end method can be implemented by batch processing. So, in summary, a charge at the end method can be used to implement a Ticker auction with the Ticker Pledge without requiring special software. This method can be used for variable pricing or for selling season tickets. 7

8 8 Concept 3. Dynamic Pricing Typically Variable Pricing takes place before the season begins. While we can certainly do that too using a Ticker auction as we described above, there is potentially a big advantage to letting prices change right up to game day. In fact, this is what we have done in many circumstances, as we will describe in the Execution section. The charge at the end system could also be used for dynamic pricing. If dynamic pricing is to take place in the final weeks before the event this is fine. But if the dynamic pricing takes place over several months, venues might prefer to charge buyer the price that is in play at the time they purchase. This is simply because the venue may prefer money in the bank to waiting. In this case, the software already employed by the venue to buyers choose seats may have capabilities that can be used to administer the refunds. Or the software used to sell tickets can be customized to incorporate the refund feature. In fact, we have used the existing features of Paciolan software at Northwestern and they are now adding some customization to implement refunds in the next iteration of our Ticker auction. Of course, we can help to customize software if necessary. 8

9 9 Concept Other issues Why give refund? Why not just keep the money and make more money? As we mentioned, if you don t give the refund, buyers have an incentive to wait even if they are willing to purchase at the current price. This gives you a poorer estimate of demand. For example, suppose the true demand is that there are 3000 fans willing to pay $50 or less to go to a game and 1000 fans willing to pay $20 or less. The current price is $50 but everyone expects price to fall to $20 so everyone waits. The realized data will then say there is demand of 4000 at $20 but zero demand at $50. But this does not capture the true demand. With the Ticker Pledge, the buyers who value the ticket at $50 might as well buy as soon as the price hits $50 (or bid $50) they get a better selection of seats and the Ticker Pledge protects them if prices go down more. Then you do get a better picture of demand. More importantly, now you have learned there is a large group of people willing to pay $50. You might even just keep the price $50 and keep the revenue of $150,000. In the previous system you thought they were only willing to pay $20 and made only $80,000 (4000 sales at $20). So economics research gives us good reason to believe that the venue would not make any more money if it did not give refunds. If people have to pay the purchase price at the time they buy, they will just wait longer and pay less. 2 After all, everyone loves to wait for a sale. So the venue does not get them to buy at the true worth of their ticket to them anyway. If you give refunds you will learn demand better and knowing the demand better you can actually make more money. Can you use a regular auction where prices go up rather than the Dutch auction where prices go down? Yes, you could use we call an ascending price auction. For example, if you have 1000 seats to sell, you could increase the price of a ticket till only 1000 people are willing to pay the final price you end up at. While this would work, the main problem with it is that the people who actually manage to get tickets have to wait to discover that till the auction ends. With Ticker, a buyer finds out straight away whether he got in or not. Does Ticker have to have the rules you described? Can some parameters be changed? Yes, some parameters can be changed as long as the Ticker Pledge or a variant of it is kept in place. For example, if there is an unexpectedly large surge in demand for an event, the current price might be too low to capture value. Then, we can change the rules so prices can go up as well as down as long as we change the Ticker Pledge to say that buyers get a refund if the price is ever lowered below the price they paid but they don t get anything back if the price goes up. This still gives them the right incentive to buy as soon as the price hits the true worth of the ticket to them. And the fact that the price can go up gives the seller option value should the event turn out to be hugely popular. 2 Here we are using the work by Roger Myerson, a former Kellogg School economist now at the University of Chicago. 9

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11 Execution The Entry Page We are using Ticker to auction individual basketball and football game tickets for Northwestern Wildcats games. (For promotional purposes, we named the initiative Purple Pricing. Similarly, we can find the appropriate name for any organization we work for.) For the Ohio State football game, we had a stock of tickets in four categories, Sideline, Corner, Endzone and Obscured View. We developed software that interfaces with Paciolan, the software used by Northwestern to sell tickets. All credit card transactions take place trough the Paciolan system. We are happy to interface with any software that implements the actual purchasing process. While our software works with Paciolan, it can be adapted for other programs such as Ticketmaster. Our webpage for Purple Pricing is at A customer who accesses the NuPurplePricing.com webpage sees the games that are being included in the Purple Pricing initiative and the current prices for different categories of seats. The can click on a button that allows them to buy now at the current price. Or they can bid. 11

12 Execution Buying and Bidding If a customer clicks the buy option, they are immediately sent to the Northwestern Paciolan webpage where they can pick seats and purchase tickets. The data is sent to us daily to analyse. Our software can easily be customized so our website also records the data. If a customer clicks the bid option for Sideline for example, they are sent to a webpage in our software where they record their bid, the number of tickets they want, their bid, their name and . This data is recorded by our software. 12

13 Execution Once a bidder has registered a bid at they are sent to a Paciolan webpage where they record the same information and enter credit card details. They are charged a small administrative fee. This is to ensure their credit card is actually valid and to prevent fake bidding. The credit card is automatically charged should the price fall to the bid: 13

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15 Execution The Back-End The software has an interface that allows the administrator to set prices that are displayed in the consumer s screen. The software also allows a user to see a summary of bids, see bids in detail, import bids from the software used to sell tickets (Paciolan in this case). These options are displayed in the first page shown to the administrator: The Set Pricing link lead to the following screen: 15

16 Execution The Bid Report link leads to a detailed list of bids and who made them (we do not show a screenshot as t contains confidential information). The Upload.. link leads to: 16

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18 Results Revenue Before we got involved, Northwestern Sports planned to price Sideline and Obscured View tickets for $70, Corner for $60 and Endzone for $50. We started off Purple Pricing at high price points. For example, the opening price for Sideline for the Michigan game was $183. It ended at $115. For the Ohio State, the final prices were $195-$185-$151-$126 for Sideline-Obscured View-Corner-Endzone respectively. We were allotted a number of seats in each category to sell via Purple Pricing the rest were sold as season tickets or group sales. The OSU game was a sellout game. We are left with the additional revenue for the OSU game from Purple Pricing below: Purple Pricing Seats Original Price Purple Price Percentage Change In Revenue Sideline Sold Out $70 $ % Obscured View Sold Out $70 $ % Corner Sold Out $60 $ % Endzone Sold Out $50 $ % Weighted Average 162% The Michigan game was not a sell out game. For this game, we employed our algorithms to achieve the balance between attendance and revenue requested by Northwestern Sports. The results are shown in the following table: Purple Pricing Seats Original Price Purple Price Percentage Change In Revenue Sideline Confidential $70 $115 64% Corner Confidential $60 $99 65% Endzone Confidential $50 $91 82% Weighted Average 67% So, Ticker works on both sellout and non-sellout games. We can make target revenue and attendance and achieve the balance between the two objectives requested by our client. Also, with the use of our software, Northwestern incurred little additional cost beyond those of the design of the customized web interface for Ticker. So the revenue is really pure profit coming from setting prices appropriately. The result hundreds of thousands of dollars in extra profit from just two games! Season Tickets Many venues want to maximize season ticket sales, believing they lead to more repeat business. Northwestern is no exception. They priced season tickets for $262-$196-$152 for Sideline-Corner-Endzone respectively. While we did not use Ticker for season tickets, since the individual game prices for the Ohio State and Michigan games were high, season tickets were an excellent bargain. So, season ticket sales skyrocketed to record numbers and Northwestern set a season ticket record in Season tickets sold out. 18

19 Results The Secondary Market Venues often see secondary market prices that are much higher than the primary market prices they set. Northwestern observes this for some of their basketball and football games against popular opponents like Indiana and Nebraska. But if primary market prices have been set correctly by Ticker, the secondary market prices should be quite similar. This is because, if it is effective, Ticker should discover the underlying demand curve. Since the same demand curve obtains in the secondary market, re-sellers should not see any revenue gain. They may even see a loss if there is a premium attached to purchasing tickets on the primary market because of convenience of printing tickets at home or because secondary market sellers may be unreliable or selling counterfeit tickets. In fact, an article at Crain s Chicago reports: 3 Sales so far show the school was effective in its experimental Purple Pricing offer for about 5,000 single-game seats for the game. The modified Dutch auction system, which guarantees that buyers don t pay any more for tickets than anyone else in their section, ended up selling out at $195, $151 and $126 for seats on the sideline, corner and end zones, respectively. On the secondary market, sideline seats have sold for an average of $190, corner seats for $135 and end-zone seats for $127. That suggests that fans haven t been able to flip them for a profit at least, not yet. So, Purple Pricing did an excellent job of setting prices. It captured the revenue that is usually lost to the secondary market. Ticker Founders Sandeep Baliga sandeep@theticker.biz Jeff Ely jeff@theticker.biz Director of Marketing Shawn Sullivan shawn@theticker.biz See Red-Hot Market for Northwestern-Ohio State Tickets, by Danny Ecker * Ticker is a product of Cheap Talk LLC. 19