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  1. Advance Purchasing of Event Tickets Wendy W. Moe Associate Professor of Marketing University of Maryland February 2008

  2. 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

  3. Classes of Services and Pricing Implications

  4. 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

  5. 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

  6. 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)

  7. 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

  8. Price

  9. Scheduling

  10. “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?

  11. Literature Review: Yield Management • 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)

  12. Pricing in Advance Ticket Market 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!

  13. Floor plan for Allstate Arena (Rosemont, IL) Available Face Values $100 (LL) $50 (LL) $30 (UL & LL corners) $20 (UL)

  14. Data Sample

  15. Capacity Utilization • Capacity is rarely a concern for ticket sellers • The typical event does not sell out either at the performance level or the tier level.

  16. Aggregate Sales and Pricing Patterns

  17. Average Price Paid by Tier

  18. Differences across Price Tiers

  19. Sales Timing by Tier

  20. Modeling the Advance Market at the Tier Level • 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

  21. Advance Market Covariates

  22. Modeling the Spot Market • Some proportion of the market (f) are spot buyers • Spot Market Covariates mirror the advance market covariates where

  23. Heterogeneity across Events • Weibull parameters • Covariate effects • Benchmark models • Correlated tiers (largest significant corr. = 0.0089) • Homogeneous tiers and

  24. Model Fit - Aggregate

  25. Model Fit – Tier Specific

  26. Summary of Model Fit across Events

  27. Results: Baseline Weibull Process by Tier

  28. Covariate Effects in Advance Market The earlier the tickets are made available for sale, the later purchases arrive.

  29. Spot Market Pricing

  30. Covariate Effects in Spot Market Discounts in the spot market increase sales in the spot market for the high-tier Higher face values encourage advance purchasing.

  31. Summary of Pricing Effects

  32. Discussion • 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

  33. Research Questions: • 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?

  34. Related Literature • 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

  35. Fotheringham 1988 • 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”

  36. Proposed Model • Agglomeration vs. competition • Spatial and temporal effects • Consumers are allocated across performances and a non-buyer segment • Non-random scheduling decision

  37. Model Development: Ticket Sales • 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

  38. Spatial and Temporal Effects of the Schedule • 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.

  39. Model Development: Timing • Weibull Timing model • Covariate effects Indicator for the week before Christmas Covariate vector that includes intercept, PREWKj, SPTj, TMPj, Vj

  40. Model Development: Scheduling • 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)

  41. Data • 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

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

  43. Results: Timing Performances surrounded by a denser schedule sell later. Attractive and densely scheduled performances get bigger holiday boost.

  44. Results: Scheduling More attractive performances have denser schedules around them.

  45. Next Steps • 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

  46. Correlated Benchmark Model:Covariances between Tiers in Weibull

  47. Homogeneous Tiers Benchmark

  48. Correlated Benchmark Model:Significant Correlations

  49. Model Fit by Tier