2011 5th MIT Sloan Sports Analytics Conference

1. Buying tickets: Capturing the dynamic factors that drive consumer purchase decisions for sporting events Wendy W. Moe, University of Maryland Peter S. Fader, University of Pennsylvania Barry S. Kahn, Qcue, Inc. March 4, 2011

2. Introduction Sports teams predict sales based largely on hunches about historical patterns and comparable events. While teams have increased price differentiation across seats, little is known how relative price changes affect a customers’ choice amongst seating options. Variable and dynamic pricing require an understanding of how customer willingness to pay relates to perceived event quality and timeuntil the event.

3. Buying game tickets Fans choose from hundreds of game-seat options (if they choose to buy anything at all) Factors that influence the buyer’s decision for a future game? How well is the home team currently performing? How strong are the visitors we will be facing in that game? How many days/weeks until the game? What seats can I get? How much do the tickets cost?

4. Staged decision process Consideration vs. choice stages: Consideration stage Objective is to identify a reduced set of options from the universal set of game-seat combinations available. Because of the large set of available options, consumers tend to use simplifying decision rules in this stage. “Elimination by Aspects” (EBA) eliminates options from consideration if one or more threshold criteria are not met. Choice stage Objective is to identify a single option from the consideration set Because of the smaller and more manageable number of options in this stage, consumers use a more complex decision rule to ensure that they make the optimal choice. Compensatorydecision rules consider the contribution of all attributes to overall consumer utility. A “no-purchase” option is always a possibility.

5. In the context of ticket buying… In the consideration stage, future game-seat options are eliminated from consideration if: The attractiveness of the game itself is below some threshold. The attractiveness of the seat (net effect of location and price) is outside of some acceptable range. The game day is either too far or too near in the future. In the choice stage, each “surviving” ticket option from the consideration stage is fully evaluated in terms of: Game attractiveness Attractiveness of the seat (including price) Time until game

6. Modeling the consideration stage Game-seat combination must be available for purchase (i.e., it is not sold out) Game attractiveness is greater than a minimum threshold (i.e., Agt>A*) Seating tier value must be greater than a minimum while not exceeding a maximum threshold (i.e., d1* < ds < d2*) The game day is within a certain future time period to allow for adequate planning (i.e., T1* < Tgt < T2*)

7. Choosing among alternatives in the consideration set Multinomial logit choice among ticket options that survived the consideration stage plus a no-purchase option. Total value of game g with seat s at time t No-purchase option Time until game Attractiveness of seat Attractiveness of game g at time t

8. Game attractiveness All games become more attractive as the home team’s record improves Attractiveness of each game is also driven by the visitor’s record The attractiveness of games changes over time as the season progresses and records evolve.

9. Attractiveness of seat Each seating option has an inherent value, C. The buyer trades off the inherent value of the seat against the price for that seat. Net attractiveness is a result of this tradeoff. where RFV is the relative face value for each seating tier compared to a chosen baseline

10. Results A winning visiting team increases game attractiveness. A winning home team increases sales of all games. Increased prices reduce attractiveness of seat S1 buys within 2 wks S2 buys within 1 mo.

11. Value of seats

12. Initial attractiveness of games

13. Attractiveness of game 39 over time

14. Thoughts about ticket pricing Face value vs. prices paid (i.e., with discounts, etc.) Price differences across seating options Variable pricing across games Dynamic pricing over time

15. Conclusions Customer segmentation through EBA implies: Only relative pricing needs be considered within a segment. Pricing changes are more impactful during the relevant purchase period. Customers exploring trade-offs amongst games are less prevalent than across seating categories. Accurately modeling ticket purchases across a season enables price elasticity at an event and seat level to be studied and can be used to set optimal variable pricing. Quantifying time-varying changes to event attractiveness sets the foundation for dynamic pricing

17. Peter Fader: faderp@wharton.upenn.edu

18. Barry Kahn: bk@qcue.net