Purpose: The purpose of this Bachelor is to analyze how the organizers of a Major event price their tickets and how these strategies consistent and differs from dynamic pricing. The study also aims to analyze whether there are conditions for dynamic pricing in Major events in the future. Theories: Dynamic pricing - Kimes model, Segmentation, price discrimination, variable ticket pricing. Method: The study used a triangulation where a qualitative interview was combined with quantitative data collection. Primary data was collected through a qualitative approach through an interview with General Secretary Tony Wiréhn and Marketing Director Malin Eldh in the local organization of the IIHF World Championship in Sweden. The quantitative data collection was to collect price information from the games played in the Swedish side of the World Championship organization arrangements. Conclusion: The conclusions that can be drawn are that the organizer of Ice Hockey World Championships today largely applies segmentation and also the second and third degree price discrimination. World Cup organization believes that it has something they call "semi-dynamic pricing", which according to this study involves a variable pricing with dynamic tendencies, then prices on some games adapted based on supply and demand during the sales period. The investigation has revealed that changes in pricing almost exclusively done by time-limited campaigns and not through a continuous influence of variables that control supply and demand. According to analysis by Kimes model revealed that future use of dynamic pricing in Major events are very possible, but there are some obstacles that must first be overcome.

In the new residential area, called Stockholm Royal Seaport (SRS), the customers will be living in Active Houses with Load Management. This implies that some balancing of the grid is shifted from the production to the consumption. To give the customer incentives to participate in the Load Management, new more dynamic pricing models needs to be implemented. At the same time, profits for the investors are needed to motivate an implementation of similar residential areas. To achieve this, an analysis of the electricity markets and an implementation of dynamic pricing models in a MATLAB simulation are done. A proposed trading profit for the investors and possible cost reductions for customers have been derived from the modelling. The results show that the difference in costs between utilized and unutilized Load Management are small, only considering the dynamic pricing models, therefore compliments to these are discussed. The conclusion is therefore that the energy for the manageable loads should be charged separately. Another important conclusion is that a change of the Spotmarket is needed in order to create a more beneficial market for retailers with flexible customers. / I den nya stadsdelen, Norra Djurgårdsstaden, ska kunderna bo i aktiva hus med laststyrda vitvaror. På så vis flyttas en del balanskraft från produktionen till konsumtionen. För att kunderna ska få incitament till att delta i laststyrningen krävs nya, mer dynamiska, pristariffer. Samtidigt behöver investerarna hitta möjliga förtjänster för att motivera ett införande av liknande stadsdelar. Detta har gjorts genom en analys av elmarknaden och en implementering av dynamiska prismodeller i en konsumtionssimulering i MATLAB. En föreslagen trading-förtjänst för investerarna samt möjliga kostnadsreduceringar för kunder har utvärderats utifrån modelleringen. Resultaten visar att med endast de dynamiska prismodellerna blir kostnadsskillnaden liten mellan de som utnyttjar laststyrningen och de som inte gör det, varför komplement till dessa diskuterats. Slutsatsen blir därför att den laststyrda elen bör debiteras enligt separat modell. En ytterligare slutsats är att spotmarknaden bör förändras för att skapa en gynnsam marknad för återförsäljare med flexibla kunder.

In this thesis, we study brand choice problem via the following three perspectives: a company's market share management, introduction of customers with different perspectives, and an analysis of an application domain which is illustrative of these issues. Our contributions following these perspectives include: (1) development of a stochastic differential-jump game (SDJG) model for brand competition in a specific situation wherein market share is modeled by a jump-diffusion process, (2) a robust hierarchical logit/probit model for market heterogeneity, and (3) applications of logit/probit model to the dynamic pricing problem occurring in production-inventory systems with jump events. Our research explores the use of quantitative method of operations research to control the dynamics of market share and provides a precise estimation method to integrate more detail information in discrete brand choice models.

With rapid development of information technologies, especially the Internet technology, people can communicate more flexibly via various media, in which knowledge can be also shared. In gaining knowledge through the Internet, either digital content retrieval or inter-personal interaction, learning activities conducted on the Web are getting popular. This research has two main objectives. One is to develop incentive mechanisms to enhance the quantity and quality of information shared through peer-to-peer (P2P) networks. The other objective is to implement and evaluate the proposed mechanisms for inquiry-based learning on P2P networks. The pricing-like incentive mechanism is embedded on each peer to determine the price to share a document, to issue a question, and respond to a question. Through experiments, this study evaluates the effects on mitigating the free-riding problems and exchanging information through the P2P network. The results show the effectiveness of the incentive mechanisms for inquiry-based learning on P2P networks.

<p>The dissertation consists of three chapters relating to pricing strategies. Chapter 1 studies coupons for prescription drugs and their impacts on consumer welfare, firm profits, and insurance payments. Chapter 2 examines consumer brand loyalty and learning in pharmaceutical demand and discusses implications for marketing and health care policy. Chapter 3 develops a framework for estimating demand and supply in an online market with many competing sellers and frequent price changes and proposes optimal pricing strategies for a large seller.</p><p>The first chapter studies an innovative price strategy in pharmaceuticals. Branded drug manufacturers have recently started to issue copay coupons as part of their strategy to compete with generics when their branded drugs are coming off patent. To explore the welfare implications of copay coupons, I estimate a model of demand and supply using pharmaceutical data on sales, prices, advertising, and copayments for cholesterol-lowering drugs and perform a counterfactual analysis where a branded manufacturer introduces coupons. The model allows flexible substitution patterns and consumer heterogeneity in price sensitivities and preferences for branded drugs. The counterfactuals quantify the effects of copay coupons for different assumptions about the take-up of coupons and the ability of the branded manufacturer to direct them to the most price-sensitive types of consumers. The results show that the agency problem between insurers and patients gives a branded manufacturer a strong incentive to issue copay coupons. Introducing copay coupons benefits the coupon issuer and consumers but raises insurance payments. In equilibrium, insurer spending can increase by as much as 25% even when just 5% of consumers have a coupon, with social welfare falling significantly.</p><p>Medicines for chronic conditions like high cholesterol, heart disease, and diabetes are repeatedly used for a long period of time. Consumer dynamics thus plays an important role in the demand for those drugs. In the second chapter, I estimate a demand model with brand loyalty and learning using micro-level data from cholesterol lowering drug markets in the United States. The estimates suggest high switching costs and strong learning effects at the molecule level in the markets. Switching costs raise the predicted probability of choosing the same drugs in a row and learning largely increases patient stickiness to a molecule in the long run. I discuss pricing implications of the estimation results for drug manufacturers, insurance companies, and policy makers. </p><p>The last chapter, coauthored with Dr. Andrew Sweeting and Dr. James W. Roberts, looks at pricing in a different context. We estimate a model of entry, exit and pricing decisions in an online market for event tickets where there are many competing sellers and prices change frequently. We use the estimates from our model to analyze the optimality of the pricing policy used by the largest seller (broker) in the market. We show that the broker's pricing policies substantially affect the prices set by his competitors. When we compare the broker's pricing policy with the prices that our model predicts are optimal we find that the broker sets approximately correct prices close to the game, when his pricing problem resembles a static one, but that he might be able to gain from using different pricing rules and updating prices more frequently further from the game.</p> / Dissertation

This dissertation studies asymmetric pricing in the small (APIS), where small price increases outnumber small price decreases, the asymmetry disappearing for larger price changes; and the corresponding reversed phenomenon (APIS-R). Current evidence suggests retailers deploy these pricing practices despite menu costs and potential consumer concerns. There is also evidence that inflation is only a partial contributor to the phenomena. These point to possible strategic intent driving these retail pricing practices. However, there are only a few papers in the domain, and none specifically address the cross-sectional and longitudinal variations. Further, existing results are mostly based on a single retailer, limited products, short time span, and legacy datasets dating back to the 1980s and 1990s, leaving their current relevance unsettled. Recent papers also question if small price changes are measurement artifacts. This dissertation addresses these gaps by analyzing several large contemporary datasets – a scanner dataset with more than 79 billion price observations and a matching consumer panel dataset with more than 50,000 participating panelists. Our key results imply the pricing practices can be retailers’ strategic responses to the cognitive tasks faced by consumers. Chapter 1 is a general introduction to the thesis. Chapter 2 sets up the fundamentals of the phenomena and reports robust evidence of APIS and APIS-R across the retail price spectrum. Chapter 3 examines the cross-sectional variations of the phenomena and finds that APIS and APIS-R are associated with product characteristics such as purchase frequency and category price level, as well as retail format such as HILO or EDLP. Chapter 4 explores the longitudinal variations and finds that business cycles are a major time-varying factor influencing retail practices of APIS and APIS-R. Chapter 5 concludes with reflections on the findings, implications for theory and practice, limitations, and suggestions for future studies. / Dissertation / Candidate in Philosophy / This dissertation studies asymmetric pricing in the small (APIS), where small price increases outnumber small price decreases, the asymmetry disappearing for larger price changes; and the corresponding reversed phenomenon (APIS-R). There are only a few papers in the domain, and none explain their cross-sectional and longitudinal variations. Existing results are mostly based on a single retailer, limited products, short time span, and legacy datasets dating back to the 1980s and 1990s, leaving their current relevance unsettled. Recent papers also question if small price changes are measurement artifacts. This dissertation addresses these gaps by analyzing several large contemporary datasets. The research finds robust evidence of both APIS and APIS-R in the retail price spectrum, and provides explanations for their cross-sectional variation, across products and retailers, as well as longitudinal variations, across business cycles. The results indicate the pricing practices can be retailers’ strategic responses to the cognitive tasks faced by consumers.

No description available.

Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.

The Bertrand Duopoly model for demand in economics is a well-used model. Although this model has important insights towards pricing strategy, it does not accurately depict true market behaviors. In this paper, we will examine the advantages and disadvantages of the current model and its assumptions.We then take a whole new approach towards modeling this phenomena, using Poisson processes to model the demand of goods. We will discuss why this is a better approach and explain how we can extend this to better understand pricing strategies and market dynamics. We then apply our findings to the newsvendor problem, a commonly used problem in inventory management. Using non-homogeneous Poisson processes we explain how to find an optimal pricing strategy and an optimal inventory level for the newsvendor problem.In this paper we explain how to extend the newsvendor problem to a newsvendor duopoly problem. Again we show how to find the optimal pricing strategies and inventory levels for multiple goods in a market. Having found the optimal pricing strategy and inventory level, we then examine the market dynamics in more details. We explore monopolistic and duopolistic markets where the goods range from complements to substitutes and homogeneous to differentiated goods. We discuss how to model the progression of the inventory probabilities and then explain how to price inventory options.

Food waste has become a disaster of global proportion that the world can no longer turn a blind eye to. This paper aims to reduce food waste at the retail level of the food supply chain by recommending and quantifying the effects of current technology that can be implemented in traditional supermarkets. This research recommends that retailers implement electronic shelf labels in stores and employ dynamic pricing of perishable products, leading to reduction of food waste. No prior research had considered the primary goal of reducing food waste while preserving retailer profit through technological implementation. This paper quantifies the effects of implementing this technology and provides economic justification of the required investment through the calculation of profitability metrics and discussion of environmental regulations retailers will soon have to abide by. Our results indicate, even in the most conservative of scenarios, that the payback period for full implementation of electronic shelf labels will be less than or slightly over one year and the return on investment is high in all situations discussed. Sensitivity analyses of labor costs, revenue, and profitability ratios are illustrated to provide a full breadth of these results.