Buying Tickets: Capturing the Dynamic Factors that Drive Consumer Purchase Decisions for Sporting Events

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2011 5th MIT Sloan Sports Analytics Conference

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Buying Tickets: Capturing the Dynamic Factors that Drive Consumer Purchase Decisions for Sporting Events

  1. 1. Buying tickets: Capturing the dynamic factors that drive consumer purchase decisions for sporting events<br />Wendy W. Moe, University of Maryland<br />Peter S. Fader, University of Pennsylvania<br />Barry S. Kahn, Qcue, Inc.<br />March 4, 2011<br />
  2. 2. Introduction<br />Sports teams predict sales based largely on hunches about historical patterns and comparable events.<br />While teams have increased price differentiation across seats, little is known how relative price changes affect a customers’ choice amongst seating options.<br />Variable and dynamic pricing require an understanding of how customer willingness to pay relates to perceived event quality and timeuntil the event.<br />
  3. 3. Buying game tickets<br />Fans choose from hundreds of game-seat options (if they choose to buy anything at all)<br />Factors that influence the buyer’s decision for a future game?<br />How well is the home team currently performing?<br />How strong are the visitors we will be facing in that game?<br />How many days/weeks until the game?<br />What seats can I get?<br />How much do the tickets cost?<br />
  4. 4. Staged decision process<br />Consideration vs. choice stages:<br />Consideration stage<br />Objective is to identify a reduced set of options from the universal set of game-seat combinations available.<br />Because of the large set of available options, consumers tend to use simplifying decision rules in this stage.<br />“Elimination by Aspects” (EBA) eliminates options from consideration if one or more threshold criteria are not met.<br />Choice stage<br />Objective is to identify a single option from the consideration set<br />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.<br />Compensatorydecision rules consider the contribution of all attributes to overall consumer utility.<br />A “no-purchase” option is always a possibility.<br />
  5. 5. In the context of ticket buying…<br />In the consideration stage, future game-seat options are eliminated from consideration if:<br />The attractiveness of the game itself is below some threshold.<br />The attractiveness of the seat (net effect of location and price) is outside of some acceptable range.<br />The game day is either too far or too near in the future.<br />In the choice stage, each “surviving” ticket option from the consideration stage is fully evaluated in terms of:<br />Game attractiveness<br />Attractiveness of the seat (including price)<br />Time until game<br />
  6. 6. Modeling the consideration stage<br />Game-seat combination must be available for purchase (i.e., it is not sold out)<br />Game attractiveness is greater than a minimum threshold (i.e., Agt>A*)<br />Seating tier value must be greater than a minimum while not exceeding a maximum threshold (i.e., d1* < ds < d2*)<br />The game day is within a certain future time period to allow for adequate planning (i.e., T1* < Tgt < T2*)<br />
  7. 7. Choosing among alternatives in the consideration set<br />Multinomial logit choice among ticket options that survived the consideration stage plus a no-purchase option.<br />Total value of game g with seat s at time t<br />No-purchase option<br />Time until game<br />Attractiveness of seat<br />Attractiveness of game g at time t<br />
  8. 8. Game attractiveness<br />All games become more attractive as the home team’s record improves<br />Attractiveness of each game is also driven by the visitor’s record<br />The attractiveness of games changes over time as the season progresses and records evolve.<br />
  9. 9. Attractiveness of seat<br />Each seating option has an inherent value, C.<br />The buyer trades off the inherent value of the seat against the price for that seat.<br />Net attractiveness is a result of this tradeoff.<br />where RFV is the relative face value for each seating tier compared<br />to a chosen baseline<br />
  10. 10. Results<br />A winning visiting <br />team increases<br />game attractiveness.<br />A winning home<br />team increases<br />sales of all games.<br />Increased prices reduce<br />attractiveness of seat<br />S1 buys within 2 wks<br />S2 buys within 1 mo.<br />
  11. 11. Value of seats<br />
  12. 12. Initial attractiveness of games<br />
  13. 13. Attractiveness of game 39 over time<br />
  14. 14. Thoughts about ticket pricing<br />Face value vs. prices paid (i.e., with discounts, etc.)<br />Price differences across seating options<br />Variable pricing across games<br />Dynamic pricing over time<br />
  15. 15. Conclusions<br />Customer segmentation through EBA implies:<br />Only relative pricing needs be considered within a segment.<br />Pricing changes are more impactful during the relevant purchase period.<br />Customers exploring trade-offs amongst games are less prevalent than across seating categories.<br />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.<br />Quantifying time-varying changes to event attractiveness sets the foundation for dynamic pricing<br />
  16. 16. <ul><li> Wendy Moe: wmoe@umd.edu
  17. 17. Peter Fader: faderp@wharton.upenn.edu
  18. 18. Barry Kahn: bk@qcue.net</li>

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