scholarly journals Capacity Allocation of Game Tickets Using Dynamic Pricing

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 141 ◽  
Author(s):  
Aniruddha Dutta
Keyword(s):  
Dynamic Pricing ◽  
Capacity Allocation ◽  
Secondary Market ◽  
Price Strategy ◽  
Ticket Sales ◽  
Demand Functions ◽  
Fixed Price ◽  
Football Game ◽  
Football Clubs ◽  
Online Retailers

This study examines a pricing approach that is applicable in the field of online ticket sales for game tickets. The mathematical principle of dynamic programing is combined with empirical data analysis to determine demand functions for university football game tickets. Based on the calculated demand functions, the application of DP strategies is found to generate more revenues than a fixed price strategy. The other important result is the capacity distribution of tickets according to the football game intensity. Prior studies have shown that it is sometimes more profitable or football clubs to allocate a share of tickets to a retailer and earn a commission based on the sales, rather than selling the entire capacity of tickets by itself. This paper finds that in a high intensity game, where the demand is generally high, it is optimal for the club to sell all tickets by itself. Whereas, for less popular games, where there is considerable fluctuation in demand, the capacity allocation problem for maximized revenues from ticket sales, becomes a harder optimization challenge for the club. According to DP optimization, when the demand for tickets is relatively low, it is optimal for the club to retain 20–40% of the tickets and the rest of the capacity should be sold to online retailers. In the real world, this pricing technique has been used by football clubs and thus the secondary market online retailers like Ticketmaster and Vivid Seats have become popular in the last decade.

Dynamic Pricing Based on Net Cost for Mobile Content Services

2007 ◽  
pp. 220-226
Author(s):  
N. Srikhutkhao
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Dynamic Pricing ◽  
Market Price ◽  
Actual Cost ◽  
Pricing Model ◽  
Market Growth ◽  
The Past ◽  
Fixed Price ◽  
And Performance

In the past few years, the mobile phone’s performance has increased rapidly. According to IDC’s Worldwide Mobile Phone 2004-2008 Forecast and Analysis, sales of 2.5G mobile phones will drive market growth for the next several years, with sales of 3G mobile phones finally surpassing the 100 million annual unit mark in 2007. Future mobile phones can support more than 20,000 colors. With the advancements in functionality and performance of mobile phones, users will use them for all sorts of activities, and that will increase mobile content service requests. Currently, the pricing of mobile content service is up to each provider; typically they implement a fixed price called a market price because the providers do not have a formula to estimate the price according to the actual cost of their services. This article proposes a dynamic pricing model based on net cost for mobile content services.

scholarly journals Continuously Increasing Price in an Inventory Cycle: An Optimal Strategy for E-Tailers

2008 ◽  
Vol 2008 ◽  
pp. 1-14
Author(s):  
Prafulla Joglekar ◽  
Patrick Lee ◽  
Alireza M. Farahani
Keyword(s):  
Sensitivity Analysis ◽  
Optimal Strategy ◽  
Demand Curve ◽  
Unit Cost ◽  
Price Strategy ◽  
Fixed Price ◽  
Fixed Quantity

Operations researchers have always assumed that when a product's unit cost is constant and its demand curve is known and stationary, a retailer of the product would find it optimal to replenish the inventory with a fixed quantity and to sell the product always at a fixed price. We present, with proof, a model that shows that, in such a case, an e-tailer is better off using a continuously increasing price strategy than using a fixed price strategy within each inventory cycle. Sensitivity analysis shows that this strategy is particularly profitable when demand is highly price sensitive and the inventory ordering and carrying costs are high.

Dynamic pricing for online hotel demand: The case of resort hotels in Majorca

2019 ◽  
Vol 26 (2) ◽  
pp. 268-283 ◽  
Author(s):  
Aldric Vives ◽  
Marta Jacob
Keyword(s):  
Dynamic Pricing ◽  
Dynamic Models ◽  
Customer Behavior ◽  
Optimal Pricing ◽  
Stochastic Dynamic ◽  
Elastic Demand ◽  
Demand Functions ◽  
Elasticity Of Demand ◽  
Price Optimization ◽  
Resort Hotels

Online customer behavior in terms of price elasticity of demand and the effect of time along the booking horizon are key requirements for the price optimization process that allows hotels to maximize their revenues. In this vein, this study adapts the online transient hotel demand functions to deterministic and stochastic dynamic models—two extended optimal pricing methods existing in the literature—in order to determine the prices that maximize the revenues of two resort hotels located in Majorca. The main findings indicate that (1) seasonality, the number of rooms available, the hotel location, and the tourist profile affect dynamic pricing (DP); (2) the booking horizon limitation leads to larger revenue decreases under elastic demand; (3) higher levels in demand elasticities generally produce lower levels of prices; and (4) the distribution of elasticities across the booking horizon and the natural variability of demand have an impact on DP. Implication for industry revenue managers is that they have to consider the booking horizon duration together with the demand price sensitivity in order to maximize the hotel revenues.

Marketing Notes and Communications: Product Perception: An Important Variable in Price Strategy

1970 ◽  
Vol 34 (4) ◽  
pp. 68-71 ◽  
Author(s):  
Zarrel V. Lambert
Keyword(s):  
Important Variable ◽  
Product Line ◽  
Consumer Perception ◽  
Price Strategy ◽  
Demand Functions ◽  
Demand Curves ◽  
Product Perception

Only limited research has focused on developing a taxonomy to distinguish products that may have reverse sloping demand functions from those with conventional demand curves. Findings in this article suggest that preferences for higher prices within a product line are related to consumer perception of product-specific characteristics.

scholarly journals Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport

2020 ◽  
Vol 4 (4) ◽  
pp. 36
Author(s):  
Francesco Branda ◽  
Fabrizio Marozzo ◽  
Domenico Talia
Keyword(s):  
Public Transport ◽  
Dynamic Pricing ◽  
Low Cost ◽  
Important Change ◽  
Pricing Strategy ◽  
Pricing Strategies ◽  
Long Distance ◽  
Ticket Sales ◽  
Transport Services ◽  
Sales Prediction

In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent an important change in Europe, since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train machine learning models for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach.

scholarly journals A Dynamic Pricing Game Investigating The Interaction Of Price And Quality On Sales Response

2010 ◽  
Vol 8 (9) ◽  
Author(s):  
Eric C. Jackson ◽  
Ram Narasimhan
Keyword(s):  
Product Quality ◽  
Dynamic Pricing ◽  
Durable Goods ◽  
Dynamic Game ◽  
High Price ◽  
Price Strategy ◽  
Game Model ◽  
Durable Good ◽  
High Quality ◽  
Dynamic Game Model

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">There is some question as to whether or not consumers use price as an indicator of product quality. In the case of non-durable goods there is some evidence that consumers do equate higher price products with higher quality products. These products are those that the consumer must experience personally before making a judgment on the product quality. In the case of durable goods there is less empirical evidence to support the price-quality connection. This paper develops a dynamic game model to investigate the price-quality connection in the presence of competition.<span style="mso-spacerun: yes;">&nbsp; </span>Specifically, the paper investigates whether or not the optimal pricing strategy in the case of a durable good, where consumers may collect quality information about the product as units diffuse into the market, should be a high quality-high price strategy or a high quality-low price strategy. This question is examined by means of a dynamic game model, which is an extension of the Narasimhan-Ghosh-Mendez (NGM) quality diffusion model. The paper explicitly incorporates competition into the NGM model. Price trajectories for two competing firms are derived so that profits are maximized for the two competitors. It is shown that the price trajectory for the firm using quality as a strategic lever is shown to be lower than that of the firm that was not using a quality strategy. This result strongly suggests that a firm pursuing a quality strategy should couple this strategy with a lower price than its competition and should not couple high prices with high quality in an effort to signal the product&rsquo;s superior quality to consumers.<span style="mso-spacerun: yes;">&nbsp; </span></span></span></p>

scholarly journals Empirical Evidence of Revenue Management in the Cruise Line Industry

2017 ◽  
Vol 58 (1) ◽  
pp. 104-120 ◽  
Author(s):  
Nur Ayvaz-Cavdaroglu ◽  
Dinesh K. Gauri ◽  
Scott Webster
Keyword(s):  
Resource Management ◽  
Revenue Management ◽  
Dynamic Pricing ◽  
Control Mechanism ◽  
Fixed Time ◽  
Capacity Allocation ◽  
Purchase Decision ◽  
Business Professionals ◽  
Price Optimization ◽  
Cruise Line

Revenue management (RM) has received considerable attention from both academic and business professionals. It encompasses several techniques regarding capacity allocation, pricing, and resource management of fixed, time-sensitive capacity. RM can be roughly divided into two categories defined by the control mechanism that increases revenue: capacity allocation or price optimization. Our work falls in the latter category. In our model, we allow for partial substitutability among products (e.g., a customer making a purchase decision may consider multiple alternatives—different departure dates, different destinations, different cabin types). We also include marketing expense in addition to prices as a lever for increasing revenue. These features are relevant to dynamic pricing in practice. The method is illustrated with booking data from a cruise company, yielding optimal advertising and prices for 300 products. The application of the model results in an increase in revenue in the range of 8%–20%.

Dynamic pricing in different Spanish resort hotels

2019 ◽  
pp. 135481661987065
Author(s):  
Aldric Vives ◽  
Marta Jacob
Keyword(s):  
Dynamic Pricing ◽  
Deterministic Model ◽  
Revenue Maximization ◽  
Demand Functions ◽  
Pricing Policies ◽  
General Revenue ◽  
High Season ◽  
Customer Profiles ◽  
Resort Hotels ◽  
Hotel Chain

The present study uses data on seven 4-star hotels belonging to the same multinational hotel chain located in different Spanish regions. The objective is to estimate the dynamic prices that allow the hotel revenue maximization during high season. The study includes the demand functions of seven resort hotels and implements a dynamic pricing deterministic model to estimate the prices that will maximize the hotel revenue for each date of stay. The results point out general revenue management implications, mainly that hotels located in the same destination should follow individualized pricing policies, more focused in the specific hotel and tourists’ characteristics; while in practice, hotel companies apply similar pricing policies to hotels located in the same destination. Furthermore, the deterministic model performs well with the data available on seven different hotels with different customer profiles and hotel characteristics.

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