Dynamic Ticket Pricing Research Articles

Ski Economics: Using Dynamic Ticket Pricing to Increase Ski Slope Revenues

Ski Economics: Using Dynamic Ticket Pricing to Increase Ski Slope Revenues

Learning-based dynamic ticket pricing for passenger railway service providers

This article proposes a data-driven ticket dynamic pricing methodology for passenger railway service providers. There is a finite purchasing horizon, and the ticket prices should be set under varying conditions to affect the customer booking behaviour. A three-step process including machine learning and optimization tools is employed to maximize the revenue under a constrained train capacity. First, a multi-layer perceptron artificial neural network (MLP-ANN) model is proposed to predict the demand intensity due to seasonal situations using the ticket reservation data. Then, some regression models as price elasticity functions are used to quantify the effects of price, seasonal conditions and competition on the company’s sales. Finally, a nonlinear integer programming model is proposed to maximize the total revenue in the purchasing horizon. The results of the numerical studies on the Fadak Five-Star Trains’ reservation data indicate that the proposed methodology has high-grade potential to improve the service provider’s revenue.

The impact of dynamic ticket pricing on Major League Baseball attendance

The impact of dynamic ticket pricing on Major League Baseball attendance

The impact of dynamic ticket pricing on Major League Baseball attendance

The impact of dynamic ticket pricing on Major League Baseball attendance

A Conceptual Model of Ticketing Pricing in Intercollegiate Sports

As college athletics have become a big business, ticket pricing strategies are considered crucial to the operations of athletics programs. However, relatively little research has integrated and explained the process of ticket pricing in the context of intercollegiate sports. To fill the existing gap, the current study articulates a conceptual model of what factors are important for ticket pricing in intercollegiate sports. The model provides multiple theoretical frameworks whereby determinants (stakeholder-related, institution-related) influence ticket pricing (managerial pricing, dynamic ticket pricing) with two moderators (the nature of non-profit organizations, involvement of ticket outsourcing companies). The model proposed in the current study contributes to integrating both factors of the decision-making process on ticket pricing for intercollegiate sports.

Optimization of dynamic ticket pricing parameters

This paper proposes a joint pricing model that combines the advantages of variable and dynamic ticket pricing models, where optimal dynamic prices are calculated for sporting events based on game-, time-, and inventory-related factors. These prices are based on a reference price and several different multipliers. Three different scenarios are investigated to reveal the most effective pricing model, together with corresponding simulation models. For the first time, a fuzzy logic model is used to predict the game multiplier, which reflects the characteristics of each individual game. The required demand information is predicted by an adaptive neuro-fuzzy inference system (ANFIS) model, and the price multiplier parameters are optimized to maximize the expected total revenue. Results based on real sporting data show that the new dynamic strategies were able to increase the expected revenue compared with a traditional static pricing strategy, indicating that all three joint pricing model scenarios could be utilized effectively to price sporting event tickets.

Dynamic Ticket Pricing: Model and Application

Dynamic Ticket Pricing: Model and Application

A Dynamic Ticket Pricing Approach for Soccer Games

This study proposes a mathematical model of dynamic pricing for soccer game tickets. The logic behind the dynamic ticket pricing model is price change based on multipliers which reflect the effects of time and inventory. Functions are formed for the time and inventory multipliers. The optimization algorithm attempts to find optimal values of these multipliers in order to maximize revenue. By multiplying the mean season ticket price (used as the reference price) by the multipliers, dynamic ticket prices are obtained. Demand rates at different prices are needed for the model, and they are provided by a unique fuzzy logic model. The results of this model are compared with real data to test the model’s effectiveness. According to the results of the dynamic pricing model, the total revenue generated is increased by 8.95% and 0.76% compared with the static pricing strategy in the first and second cases, respectively. The results of the fuzzy logic model are also found to be competitive and effective. This is the first time a fuzzy logic model has been designed to forecast the attendance of soccer games. It is also the first time this type of mathematical model of dynamic pricing for soccer game tickets has been designed.

NBA Primary Market Ticket Consumers: Ex Ante Expectations and Consumer Market Origination

We add to the recent ticket market literature by using a unique, disaggregated, and proprietary data set of primary market ticket sales transactions from a National Basketball Association team that includes previously unavailable information on date of purchase, customer location, and other consumer demographics. We find that local and out-of-market fans differ in their total purchase amounts, with out-of-market fans spending more than local consumers, on average, and differential spending effects based on the home team win probability. In particular, this differential behavior has important implications for Rottenberg’s uncertainty of outcome hypothesis. We find evidence that interest in visiting team quality dominates interest in perceived contest uncertainty, fitting the reference-dependent preference model in the context of low local team quality. Further, these findings also have important implications related to market segmentation and dynamic ticket pricing in professional sport.

Dynamic ticket pricing and the impact of time – an analysis of price paths of the English soccer club Derby County

ABSTRACTResearch question: Empirical studies about dynamic ticket pricing (DTP) in sports to date have focused exclusively on the Major League Baseball and on analyzing dynamic ticket prices based on a limited number of points in time. The current study extends the literature by examining the impact of time over the whole selling period. Furthermore, the presented study is the first to analyze a dynamic pricing system of a football club in Europe.Research methods: Ticket prices for selected games of the English football club Derby County were collected daily during the 2013/2014 season. A data set of 5862 price points was analyzed by means of a hedonic pricing regression.Results and findings: The model suggests that time has a significant effect on dynamic ticket prices. Ticket prices increased monotonously over time. This finding is consistent with previous research on dynamic pricing in sports. However, the pricing system can be differentiated from models applied in the airline or the hotel industry.Implications: The current study extends the existing literature by analyzing the effect of time in the context of DTP. Sport managers can apply these findings concerning the specification of a more sophisticated pricing approach.

Is Dynamic Pricing Viable for Small Market Collegiate Athletics?

Sport organizations use dynamic ticket pricing (DTP) strategies to generate extra revenues. DTP is based on demand and occasions. This exploratory study reported the results of the online survey data collected from 48 mid-west college/university athletic directors and marketing managers. The participants shared responses regarding their perceptions of the use of DTP by regional and private smallmarket collegiate athletic programs. Specifically, the study examined the perceived benefits and shortcomings of DTP. Past experiences implementing the strategy are also discussed. The results concluded that the majority of respondents were receptive to DTP although they questioned whether the strategy could deliver excessive financial benefits. They also noted that the practice of frequent ticket price change may be cumbersome and challenged.

An examination of dynamic ticket pricing and secondary market price determinants in Major League Baseball

Over the past several years, professional sport organizations have started to shift from cost-based ticket pricing strategies to a demand-based focus, where price considerations are driven, in part, by market demand. Dynamic ticket pricing (DTP), where prices fluctuate daily based on market factors, evolved from this transition. The motivation for DTP stems from the significant growth of the secondary ticket market, where ticket prices are almost completely demand driven. One issue with this strategy is the limited understanding of specific factors that influence dynamically priced tickets and secondary market tickets. The current study examined price determinants in the primary market where DTP has been implemented and comparable tickets in the secondary market. Four regression models were developed for this purpose. The first two models examined factors influencing dynamic ticket price. Both DTP models were found to be significant, explaining 91.4% and 70.8% of the variance in dynamic price, respectively. The second two models examined factors influencing secondary market ticket price. Both secondary market models were found to be significant, explaining 82.7% and 79.7% of the variance in secondary market price, respectively. There were many consistencies between models, including an emphasis on team and individual performance factors, ticket-related factors, and time-related factors. However, there were a number of unique aspects to each model, which may help sport managers develop pricing strategies that better reflect market demand for sport

A New Age of Demand-Based Pricing: An Examination of Dynamic Ticket Pricing and Secondary Market Prices in Major League Baseball

In 2010, the San Francisco Giants became the first professional team to implement a comprehensive demand-based ticket pricing strategy called dynamic ticket pricing (DTP). In an effort to understand DTP as a price setting strategy, the current investigation explored Giants’ ticket prices during the 2010 season. First, the relationship between fixed ticket prices, dynamic ticket prices, and secondary market ticket prices for comparable seats were examined. In addition, seat location and price changes over time were examined to identify potential effects on ticket price in the primary and secondary market. Giants’ ticket price data were collected for various games throughout the 2010 season. A purposive selection of 12 games, which included (N = 1,316) ticket price observations, were chosen in an effort to include a multitude of game settings. Two ANOVA models were developed to examine price differences based on pricing structure, market, section, and time. Findings showed significant differences between fixed ticket prices, dynamic ticket prices, and secondary market ticket prices, with fixed ticket prices on the low end and secondary market ticket prices on the high end of the pricing spectrum. Furthermore, time was found to have a significant influence on ticket price; however, the influence of time varied by market and seat location. These findings are discussed and both theoretical and practical implications are considered.

Predicting future popularity trend of events in microblogging platforms

AbstractThe fast information sharing on Twitter from millions of users all over the world leads to almost real‐time reporting of events. It is extremely important for business and administrative decision makers to learn events' popularity as quickly as possible, as it can buy extra precious time for them to make informed decisions. Therefore, we introduce the problem of predicting future popularity trend of events on microblogging platforms. Traditionally, trend prediction has been performed by using time series analysis of past popularity to forecast the future popularity changes. As we can encode the rich Twitter dynamics using a rich variety of features from microblogging data, we explore regression, classification and hybrid approaches, using a large set of popularity, social and event features, to predict event popularity. Experimental results on two real datasets of 18382 events extracted from ∼133 million tweets show the effectiveness of the extracted features and learning approaches. The predicted popularity trend of events can be directly used for a variety of applications including recommendation systems, ad keywords bidding price decisions, stock trading decisions, dynamic ticket pricing for sports events, etc.

An examination into the factors that influence consumers’ perceptions of value

Recent trends in ticketing, such as dynamic ticket pricing, indicate a fundamental shift towards demand-based pricing strategies. As such, research is needed to understand the factors that may influence the price that consumers are willing to pay for tickets. Using the contingent valuation method (CVM), the current study examined the value that consumers placed on a ticket to a National Basketball Association game. Previous CVM research suggests that valuations differ based on whether the participant is on the buy side or the sell side of the transaction. Therefore, the current study examined the difference between buyer and seller valuations in addition to identifying factors that influenced these valuations. Results indicated a strong difference between buyer and seller valuations, which is consistent with previous CVM studies. Further, the factors that influenced these valuations, including the printed price on the ticket, also differed based on whether the participant was buying or selling the ticket.