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Advance Purchasing of Event Tickets. Wendy W. Moe Associate Professor of Marketing University of Maryland February 2008. What is advance purchasing?. “Advance selling occurs when sellers allow buyers to purchase at a time preceding consumption” (Xie and Shugan 2001)

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Advance purchasing of event tickets

Advance Purchasing of Event Tickets

Wendy W. Moe

Associate Professor of Marketing

University of Maryland

February 2008


What is advance purchasing
What is advance purchasing?

  • “Advance selling occurs when sellers allow buyers to purchase at a time preceding consumption” (Xie and Shugan 2001)

  • Three classes of services based on (Desiraju and Shugan 1999):

    • Price sensitivity of buyers

    • Nature of sales arrival



Overview of advance purchasing literature
Overview of Advance Purchasing Literature

  • Marketing

    • How to price in advance markets based on buyer price sensitivity and nature of arrival (Desiraju and Shugan 1999)

    • When and how to advance sell based on capacity constraints, marginal costs, and buyer valuations and risk attitude (Xie and Shugan 2001)

  • Economics

    • Yield management literature (Biyalogorsky et al 1999, Dana 1998, Borenstein and Winston 1990)

    • Assumes underlying consumer behavior and optimizes revenues based on these assumptions

    • Tends to focus on airline industry


Focus on event tickets
Focus on event tickets

  • Tickets start selling months in advance

  • Large arena venues where capacity constraints tend to be non-binding

  • Dynamic pricing is rarely, if ever, employed

  • Price tiers are defined by venue layout and quality of seating


Overview of event ticket market literature
Overview of Event Ticket Market Literature

  • More empirical work than in the advance purchasing literature

  • What are the factors that influence event sales?

    • Event characteristics (Weinberg 1986, Weinberg and Shachmut 1978)

    • Marketing mix (Putler and Lele 2003, Reddy, Swamanathan and Motley 1998)

  • Pricing

    • What influences the value of a bundle of tickets? (Venkatesh and Mahajan 1993)

    • What are the benefits of price discrimination policies? (Leslie 2004)


Research objectives
Research Objectives

Empirically examine consumer purchasing behavior in the advance ticket market

Purchase timing

Response to price and price tiers

Response to scheduling

Findings have implications for policy, but objective is not to derive optimal policies or to forecast.

1. The Role of Price Tiers in Advance Purchasing of Event Tickets

2. The Spatial and Temporal Effects of a Performance Schedule on Ticket Sales




The role of price tiers in advance purchasing of event tickets
“The Role of Price Tiers in Advance Purchasing of Event Tickets”

  • Are there systematic differences in buyer behavior across price tiers?

    • What are the differences in purchase timing across buyers in different price tiers?

    • How do consumers respond to price and price discounting?

  • Can ticket pricing in the advance market affect the size of the spot market?


Literature review yield management
Literature Review: Yield Management Tickets”

  • Focus on airline industry

  • Objective is to set a pricing policy that would maximize revenue assuming patterns in customer behavior but does not actually model the customer response to price.

  • Empirical studies tend to ignore tiers (McGill and van Ryzin 1999 for a review) and pricing effects are confounded with differences across tiers.

  • Choice of tier and when to buy for Broadway show is driven by capacity constraints (Leslie 2004)


Pricing in advance ticket market
Pricing in Advance Ticket Market Tickets”

Price Tiers

Consumers with different valuations self select into different price tiers.

Ticket categories are classified as either high, medium or low

Face Value Price

Set in advance of the selling period and fixed over time

Price Discounting

Promotion codes are offered

May vary from week to week

No dynamic pricing!


Floor plan for allstate arena rosemont il
Floor plan for Allstate Arena (Rosemont, IL) Tickets”

Available Face Values

$100 (LL)

$50 (LL)

$30 (UL & LL corners)

$20 (UL)


Data sample
Data Sample Tickets”


Capacity utilization
Capacity Utilization Tickets”

  • Capacity is rarely a concern for ticket sellers

  • The typical event does not sell out either at the performance level or the tier level.






Modeling the advance market at the tier level
Modeling the Advance Market at the Tier Level Tickets”

  • For each event (i), advance purchase timing is modeled as a Weibull hazard process with covariates at the tier (j) level

  • The performance at time T right censors the selling period

if t < T

if t = T



Modeling the spot market
Modeling the Spot Market Tickets”

  • Some proportion of the market (f) are spot buyers

  • Spot Market Covariates mirror the advance market covariates

where


Heterogeneity across events
Heterogeneity across Events Tickets”

  • Weibull parameters

  • Covariate effects

  • Benchmark models

    • Correlated tiers (largest significant corr. = 0.0089)

    • Homogeneous tiers

and





Results baseline weibull process by tier
Results: Tickets” Baseline Weibull Process by Tier


Covariate effects in advance market
Covariate Effects in Advance Market Tickets”

The earlier the tickets are made available for sale, the later purchases arrive.



Covariate effects in spot market
Covariate Effects in Spot Market Tickets”

Discounts in the spot market increase sales in the spot market for the high-tier

Higher face values encourage advance purchasing.



Discussion
Discussion Tickets”

  • Empirically, low value buyers purchase later than high value buyers

    • Not a result of capacity constraints

    • Possible behavioral driver: cost of commitment (Desiraju and Shugan 1999)

  • Behavioral differences across price tiers are much greater than other effects of price

    • Face value price and price discounting has no effect on behavior in the advance market

    • Spot market discounting benefits the high-priced tier only


Research Questions: Tickets”

  • How does the performance schedule affect demand for individual performances?

  • Are there agglomeration effects or do the performances simply cannibalize each other?

  • How does the performance schedule affect advance purchase timing?


Related literature
Related Literature Tickets”

  • Retail location

    • Competition effects (Nakanishi and Cooper 1974, Danthu and Rust 1989, Zhu and Singh 2007)

    • Agglomeration effects result from… (Vitorino 2007, Datta, Sudhir and Talukdar 2007))

      • Minimizing consumer search costs

      • Offering complimentary products in one-stop

    • Fotheringham 1988

  • Highway hotels (Mazzeo 2002)

    • Competition benefits from capacity constraints

    • Differentiated product offerings


Fotheringham 1988
Fotheringham 1988 Tickets”

  • Model of consumer store choice (not empirically tested)

  • Vij = underlying value for retailer j

  • dj‘j = distance between the two retailers

  • Limitation: Assumes all consumers are “in the market”


Proposed model
Proposed Model Tickets”

  • Agglomeration vs. competition

  • Spatial and temporal effects

  • Consumers are allocated across performances and a non-buyer segment

  • Non-random scheduling decision


Model development ticket sales
Model Development: Ticket Sales Tickets”

  • Each performance j has underlying value Vj

  • The attractiveness of each performance j is adjusted by the interest in the combination of spatial and temporal qualities of the performance, P(INTj).

where

Intercept and performance specific characteristics


Spatial and temporal effects of the schedule
Spatial and Temporal Effects of the Schedule Tickets”

  • P(INTj) can be conceptualized as the relative preference for the spatial-temporal qualities of j.

  • Non-buyer segment: Those not interested in any of the spatial-temporal combinations available.


Model development timing
Model Development: Timing Tickets”

  • Weibull Timing model

  • Covariate effects

Indicator for the week before Christmas

Covariate vector that includes intercept, PREWKj, SPTj, TMPj, Vj


Model development scheduling
Model Development: Scheduling Tickets”

  • The schedule is not necessarily random or independent of the expected demand

  • Model the effects of baseline value (a0) and potential agglomeration/substitution effects (q, f) on the SPT and TMP measures (Manchanda, Rossi and Chintagunta 2004)


Data Tickets”

  • Weekly ticket sales for 458 events across 42 metro areas.

  • In many metro areas, performances are scheduled across multiple venues.

  • Initial empirical analysis: greater NY metro area

    • 4 venues

    • 70 events

    • Performances scheduled from March – June 2004


Results ticket sales
Results: Ticket Sales Tickets”

More scheduled performances in and around the same venue increases sales while closely scheduled performances (in time) compete against one another.


Results timing
Results: Timing Tickets”

Performances surrounded by a denser schedule sell later.

Attractive and densely scheduled performances get bigger holiday boost.


Results scheduling
Results: Scheduling Tickets”

More attractive performances have denser schedules around them.


Next steps
Next Steps Tickets”

  • Estimate across 42 metro markets across the US

  • How do we set a national schedule?

    • How many total performances to schedule in each metro area?

    • Need to consider constraints such as available capacity and travel costs


Correlated benchmark model covariances between tiers in weibull
Correlated Benchmark Model: Tickets” Covariances between Tiers in Weibull



Correlated benchmark model significant correlations
Correlated Benchmark Model: Tickets” Significant Correlations


Model fit by tier
Model Fit by Tier Tickets”


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