The Role of Price Tiers in Advance Purchasing of Event Tickets
30 Pages Posted: 13 Aug 2007 Last revised: 4 Apr 2012
Date Written: January 2008
Abstract
Advance purchasing is common in several product markets (e.g., concerts, air travel, etc.) but has generally been understudied in the marketing literature. Research to date has focused primarily on the analytical modeling of optimal pricing policies. Our work complements this literature by focusing on the empirical modeling of advance purchasing and the effects of price on consumer purchasing behavior. Since pricing strategies in practice are typically more complex than simply setting a single price point, we consider multiple aspects of price: (1) the existence and use of multiple price tiers (generally based on seat quality), (2) the face value of each ticket, and (3) discounts that result in week-to-week variations in price. We show that failure to account for price tiers can lead to exaggerated inferences about the role of price over time. To sort out these effects, we develop a tier-specific Weibull timing model to describe sales arrivals for event tickets in the advance selling period, using a proportional hazards framework for the time-varying covariates. Additionally, we include a component that captures the role of similar covariates to explain spot market purchasing. Our empirical findings reflect substantial differences across tiers. Purchasers in the high-priced tier tend to buy earlier in the selling period and are influenced by price discounts/premiums in the spot market. Purchasers in the low- and mid-priced tiers tend to delay purchasing and are influenced only by face value prices in the spot market. Advance purchasers are not influenced by any aspect of price (after accounting for differences across price-tiers), and this holds true across all price tiers for our dataset of 22 family events. We discuss the implications of these empirical observations for future modelers.
Keywords: Advance Selling, Advance Pricing, Event Tickets, Price Tiers, Spot Market, Entertainment Marketing, Weibull Hazard Model, Hierarchical Bayesian Analysis
JEL Classification: M31, L82, C41, C11
Suggested Citation: Suggested Citation
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