What Secondary Market Data Do and Do Not Tell You Rob Stavins - PowerPoint PPT Presentation

Winning Strategies for Ticket Pricing: What Secondary Market Data Do – and Do Not – Tell You Rob Stavins Albert Pratt Professor of Business and Government John F. Kennedy School of Government, Harvard University MIT Sloan Sports Analytics Conference March 3, 2012 BOSTON CHICAGO DALLAS DENVER LOS ANGELES MENLO PARK MONTREAL NEW YORK SAN FRANCISCO WASHINGTON

Agenda Background, Purpose, and Objectives Analytical Methods Outputs and Illustrative Results PAGE 1

Background and Purpose  Every day, in ordinary markets for all sorts of goods and services, the interaction of supply and demand yields market-clearing prices,  which just barely sell inventory – no severe shortages or surpluses  In professional sports, the true market-clearing price (market value) of tickets is in some cases greater than face value , and in some cases less  And the true market value almost always varies more than the face value – according to location, amenities, timing, and other factors  We know this from the secondary market , but very careful analysis is required to develop reliable estimates of true market-clearing (or other revenue-enhancing) prices  In fact, secondary market prices on their own are often highly misleading !  The analysis employs advanced, but well-established statistical methods which we have used successfully for MLB and NFL teams, and can be adapted for other sports PAGE 2

Example of Real World Application: Miami Dolphins  The Miami Dolphins turned to us to help develop a pricing plan for 2010 season tickets and individual game tickets For $5 more, Miami Dolphins fans can have it made in the shade  Using an econometric analysis of secondary market transactions, we evaluated consumer demand, estimated market-clearing prices, and recommended a new pricing approach Miami Dolphins raise some ticket prices, freeze and lower others  As a result, the Dolphins increased ticket prices for 55% of seats, left prices unchanged for 30%, and lowered prices for 15% of seats Miami Dolphins raise 2010 ticket prices  The team also created new seating in many lower-deck sections categories March 4, 2010  Bottom Line: The analysis helped increase both attendance and revenue PAGE 3

Possible Objectives of Ticket Value Analysis I. Increase attendance and/or revenue  What are the right relative prices among existing ticket categories?  Can revenue be increased by adjusting or creating new ticket categories?  How much could be gained by temporally differentiated pricing (variable pricing)? II. Improve the quality of ticket services for fans  Fans dislike sitting next to someone who paid less for the same-quality seat, or paying the same amount as someone who has a better seat  Better pricing reduces price differentiation by source, and increases price differentiation by quality III. Address a wide variety of investment questions  For example, adding new seats, new seating areas, or amenities IV. Improve understanding of the real market value of assets  The present discounted value of net revenue that could be earned from ticket sales is a key component of a team’s real market value PAGE 4

Understanding the True Demand for Tickets  For all of these objectives, one must know the true market demand for tickets  How much people value specific seats to specific games  How much people value individual attributes of seats and games  But true market demand is unknown in advance,  because face values do not reflect true interaction of supply and demand PAGE 5

Revenue Impacts of Inefficient Pricing of Some Tickets Price Section A sells out, but leaves $ on the table Demand due to insufficient differentiation Section B does not sell out, and leaves $ on the table due to: Sec A Limited Price too high for Price differentiation some seats within section Sec B Capacity Price Section A Section B PAGE 6

Revenue Improvement Due to Not Overpricing Tickets Price Depending on demand, it may be possible to increase revenue in Section A by raising price and sacrificing attendance, but here we focus on selling out Section B sells out by creating sub-section C at lower price (but still leaves considerable $ on table X due to limited price differentiation in Sec A Price Section A) Sec B X Capacity Price Sec C Price Section A Section B C PAGE 7

Revenue Improvement Due to Increased Price Differentiation in Section A Price Section A leaves much less $ on the table, due to price differentiation of new sub-sections Sec A Prices Sec B Capacity Price Sec C Price A1 A2 A3 A4 Section B C PAGE 8

Revenue Potential Due to Higher Demand for Specific Games (e.g., Key Opponent) Revenue Potential Due to Higher Demand Price Due to Temporal Effect (Key Game) Sections A, B, & C sell out, but A & B leave more $ on table due to higher demand for key game Sec A Prices X Sec B Capacity Price Sec C Price A1 A2 A3 A4 Section B C PAGE 9

Revenue Improvement with Higher Demand and Temporal Price Differentiation for Key Game (Variable Pricing) Revenue Potential Due to Higher Demand Price Due to Temporal Effect (Key Game) Variable Pricing with higher prices for “A sections” for key game leave less $ on the table Not to be confused Sec A with dynamic pricing Prices X Sec B Capacity Price Sec C Price A1 A2 A3 A4 Section B C PAGE 10

Agenda Background, Purpose, and Objectives Analytical Methods Outputs and Illustrative Results PAGE 11

Hedonic Analysis of Secondary Market Data  Hedonic analysis is a well-accepted statistical method that reveals the relationship between a product’s attributes and its market price  Has been extensively applied to value many products, including automobiles, computers, and airline tickets PAGE 12

Hedonic Model Inputs Temporal Seating Secondary Attributes Attributes Market Prices Hedonic Price Model Value of Individual Attributes Estimated Ticket Values (in Secondary Market) Additional Team & Estimated Market-Clearing Stadium Information Prices Analysis Outputs  Price-value ratios by ticket category  Recommended ticket categories  Estimates of incremental revenues PAGE 13

Basic Data for Analysis Secondary Market Sale Price Ticket Sale Attributes  Number of seats together  Time from sale to game day Temporal Attributes  Opponent Location (Seating) Attributes  Day of week/time of game  Seating section  Expected weather (climate)  Distance to field, home plate  Playoff contention  Angle of view  Elevation and viewing pitch = secondary market  Other amenities – club seats, ticket sale parking privileges, etc. PAGE 14

Collection of Market Price Data  Data on secondary market transactions (sales) can be obtained directly through relationships with secondary market ticket sellers  Alternatively, ticket price data can be obtained through automatically harvesting, systematically organizing, and interpreting observations from numerous secondary-market websites that resell tickets PAGE 15

Execution of Hedonic Model Temporal Seating Secondary Attributes Attributes Market Prices Hedonic Price Model Value of Individual Attributes Estimated Ticket Values (in Secondary Market) Additional Team & Estimated Market-Clearing Stadium Information Prices Analysis Outputs  Price-value ratios by ticket category  Recommended ticket categories  Estimates of incremental revenues PAGE 16

Execution of Hedonic Model  Econometric (statistical) analysis  Here’s a simple example of the contribution of each attribute… $60 $50 $40 20 -5 $30 15 $20 Note: Contribution of each attribute $10 20 affects contributions of others $0 Field box Behind Tuesday Opponent is home plate night Red Sox PAGE 17

Execution of Hedonic Model  Econometric analysis is based on secondary market activity  While the sample is large (statistically precise), it is not representative (biased)  Tickets on the secondary market are not necessarily representative of all tickets  We use the statistical analysis to adjust for this  Then, we simulate the value of tickets for specific games and/or combinations of attributes Unadjusted Ticket Prices Estimated Ticket Values Secondary Estimated Ticket Face Market Sample Regression- Market Category Value Average Adjusted Value Clearing Price Section A $40 $185 $120 PAGE 18

Moving from Secondary Market to Market-Clearing Prices Temporal Seating Secondary Attributes Attributes Market Prices Hedonic Price Model Value of Individual Attributes Estimated Ticket Values (in Secondary Market) Additional Team & Estimated Market-Clearing Stadium Information Prices Analysis Outputs  Price-value ratios by ticket category  Recommended ticket categories  Estimates of incremental revenues PAGE 19

Moving from Secondary Market to Market-Clearing Prices  Buyers on the secondary market may not be representative of the larger fan base  We incorporate additional information from the team, and use a proprietary methodology to correct for this bias, and thus estimate the true market clearing price in the primary market … Unadjusted Ticket Prices Estimated Ticket Values Secondary Estimated Ticket Face Market Sample Regression- Market Category Value Average Adjusted Value Clearing Price Section A $40 $185 $120 $70 Section B $250 $120 $160 $175 PAGE 20