Download or read book Dynamic Pricing with Demand Learning Under Competition written by Carine Anne Marie Simon and published by . This book was released on 2007 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: (cont.) Finally, we consider closed-loop strategies in a duopoly market when demand is stochastic. Unlike open-loop policies (such policies are computed once and for all at the beginning of the time horizon), closed loop policies are computed at each time period, so that the firm can take advantage of having observed the past random disturbances in the market. In a closed-loop setting, subgame perfect equilibrium is the relevant notion of equilibrium. We investigate the existence and uniqueness of a subgame perfect equilibrium strategy, as well as approximations of the problem in order to be able to compute such policies more efficiently.

Download or read book Dynamic Pricing with Demand Learning and Reference Effects written by Arnoud den Boer and published by . This book was released on 2020 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a seller's dynamic pricing problem with demand learning and reference effects. We first study the case where customers are loss-averse: they have a reference price that can vary over time, and the demand reduction when the selling price exceeds the reference price dominates the demand increase when the selling price falls behind the reference price by the same amount. Thus, the expected demand as a function of price has a time-varying "kink" and is not differentiable everywhere. The seller neither knows the underlying demand function nor observes the time-varying reference prices. In this setting, we design and analyze a policy that (i) changes the selling price very slowly to control the evolution of the reference price, and (ii) gradually accumulates sales data to balance the tradeoff between learning and earning. We prove that, under a variety of reference-price updating mechanisms, our policy is asymptotically optimal; i.e., its T-period revenue loss relative to a clairvoyant who knows the demand function and the reference-price updating mechanism grows at the smallest possible rate in T. We also extend our analysis to the case of a fixed reference price, and show how reference effects increase the complexity of dynamic pricing with demand learning in this case. Moreover, we study the case where customers are gain-seeking and design asymptotically optimal policies for this case. Finally, we design and analyze an asymptotically optimal statistical test for detecting whether customers are loss-averse or gain-seeking.

Download or read book Competitive Multi Product Pricing with Demand Learning and Substitution Effects written by Rainer Schlosser and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Many firms are selling different types of products. Typically sales applications are characterized by competitive settings, limited information and substitution effects. The demand intensities of single types of products are affected by the own products as well as the products of competitors. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic multi-product pricing models under competition for the sale of durable goods. In a first step, a data-driven approach is used to measure substitution effects and to estimate sales probabilities in competitive markets. In a second step, we use a dynamic model to compute powerful heuristic feedback pricing strategies, which are even applicable if the number of competitors' offers is large and their pricing strategies are unknown. Moreover, our approach allows taking additional features, such as customer ratings or shipping times into account. Adaptive estimations are used to update the estimation of sales probabilities and to further improve the strategy.

Download or read book Dynamic Pricing Implications of Uncertainty about Demand written by Eric Gordon Wruck and published by . This book was released on 1989 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing Under Competition written by Rainer Schlosser and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Most sales applications are characterized by competitive settings and limited demand information. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic pricing models under competition for the sale of durable goods. In a first step, a data-driven approach is used to estimate sales probabilities in competitive markets. In a second step, we use a dynamic model to compute powerful feedback pricing strategies efficiently, which are even applicable if the number of competitors is large and their strategies are unknown. In the case of liquid markets, in which competitors frequently adjust their prices, we verify that our heuristic feedback strategy also yields excellent results. To be able to compare expected profits, we compute optimal response strategies in a duopoly market where the competitor's price adjustments can be anticipated. We also show that the lack of information can be (over)compensated by adjusting prices slightly more frequently than the competitor does.

Download or read book Dynamic Pricing and Demand Learning with Limited Price Experimentation written by Wang Chi Cheung and published by . This book was released on 2017 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst case regret, i.e., the expected total revenue loss compared to a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log^(m) T), or m iterations of the logarithm. Furthermore, we describe an implementation at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings.

Download or read book Dynamic Pricing in a Competitive Environment written by Marc Howard Coumeri and published by . This book was released on 2000 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Revenue Management and Pricing Analytics written by Guillermo Gallego and published by Springer. This book was released on 2019-08-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: “There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it." Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.

Download or read book Price Discrimination written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-03-27 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Price Discrimination Price discrimination is a microeconomic pricing strategy where identical or largely similar goods or services are sold at different prices by the same provider in different market segments. Price discrimination is distinguished from product differentiation by the more substantial difference in production cost for the differently priced products involved in the latter strategy. Price differentiation essentially relies on the variation in the customers' willingness to pay and in the elasticity of their demand. For price discrimination to succeed, a firm must have market power, such as a dominant market share, product uniqueness, sole pricing power, etc. All prices under price discrimination are higher than the equilibrium price in a perfectly competitive market. However, some prices under price discrimination may be lower than the price charged by a single-price monopolist. Price discrimination is utilized by the monopolist to recapture some deadweight loss. This Pricing strategy enables firms to capture additional consumer surplus and maximize their profits while benefiting some consumers at lower prices. Price discrimination can take many forms and is prevalent in many industries, from education and telecommunications to healthcare. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Price discrimination Chapter 2: Monopoly Chapter 3: Monopolistic competition Chapter 4: Oligopoly Chapter 5: Perfect competition Chapter 6: Imperfect competition Chapter 7: Deadweight loss Chapter 8: Two-part tariff Chapter 9: Pricing Chapter 10: Barriers to entry Chapter 11: Yield management Chapter 12: Market power Chapter 13: Non-price competition Chapter 14: Market structure Chapter 15: Pricing strategies Chapter 16: Dynamic pricing Chapter 17: Revenue management Chapter 18: Value-based pricing Chapter 19: Rental value Chapter 20: Profit (economics) Chapter 21: Monopoly price (II) Answering the public top questions about price discrimination. (III) Real world examples for the usage of price discrimination in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Price Discrimination.

Download or read book Mathematical and Computational Models for Congestion Charging written by Siriphong Lawphongpanich and published by Springer Science & Business Media. This book was released on 2006-06-05 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rigorous treatments of issues related to congestion pricing are described in this book. It examines recent advances in areas such as mathematical and computational models for predicting traffic congestion, determining when, where, and how much to levy tolls, and analyzing the impact on transportation systems. The book follows recent schemes judged to be successful in London, Singapore, Norway, as well as a number of projects in the United States.

Download or read book Operationalizing Dynamic Pricing Models written by Steffen Christ and published by Springer Science & Business Media. This book was released on 2011-04-02 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.

Download or read book On the Surprising Sufficiency of Linear Models for Dynamic Pricing with Demand Learning written by Omar Besbes and published by . This book was released on 2014 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a multi-period single product pricing problem with an unknown demand curve. The seller's objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon; in doing so, the seller needs to resolve the tension between learning the unknown demand curve and maximizing earned revenues. The main question that we investigate is the following: how large of a revenue loss is incurred if the seller uses a simple parametric model which differs significantly (i.e., is misspecified) relative to the underlying demand curve. This "price of misspecification'' is expected to be significant if the parametric model is overly restrictive. Somewhat surprisingly, we show (under reasonably general conditions) that this may not be the case.

Download or read book Introduction to Business written by Lawrence J. Gitman and published by . This book was released on 2024-09-16 with total page 1455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Business covers the scope and sequence of most introductory business courses. The book provides detailed explanations in the context of core themes such as customer satisfaction, ethics, entrepreneurship, global business, and managing change. Introduction to Business includes hundreds of current business examples from a range of industries and geographic locations, which feature a variety of individuals. The outcome is a balanced approach to the theory and application of business concepts, with attention to the knowledge and skills necessary for student success in this course and beyond. This is an adaptation of Introduction to Business by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.

Download or read book Sequential Decision Making in Dynamic Systems written by Yixuan Zhai and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We study sequential decision-making problems in the presence of uncertainty in dynamic pricing, intrusion detection, and routing in communication networks. A decision maker is usually able to learn from the feedback (observations) in sequential decision-making problems. We consider designing optimal strategies and analyze their performance. In the first part, we consider a dynamic pricing problem under unknown demand models. We start with a monopoly dynamic pricing problem. In this problem, a seller offers prices to a stream of customers and observes either success or failure in each sale attempt. The underlying demand model is unknown to the seller and can take one of M possible forms. We show that this problem can be formulated as a multi-armed bandit with dependent arms. We propose a dynamic pricing policy based on the likelihood ratio test. It is shown that the proposed policy achieves complete learning, i.e. it offers a bounded regret where regret is defined as the revenue loss with respect to the case with a known demand model. This is in sharp contrast with the logarithmic growing regret in multi-armed bandit with independent arms. Later, we consider an oligopoly dynamic pricing problem with a finite uncertainty of demand models. Besides just considering the learning efficiency, we assume that sellers are individually rational and consider strategies within the set of certain kind of equilibria. We formulate the oligopoly problem as a repeated Bertrand game with incomplete information. Two scenarios are investigated, sellers with equal marginal costs or asymmetric marginal cost. For the scenarios with equal marginal costs, we developed a dynamic pricing strategy called Competitive and Cooperative Demand Learning (CCDL). Under CCDL, all sellers would collude and obtain the same average total profit as a monopoly. The strategy is shown to be a subgame perfect Nash equilibrium and Pareto efficient. We further show that the proposed competitive pricing strategy achieves a bounded regret, where regret is defined as the total expected loss in profit with respect to the ideal scenario of a known demand model. For the scenarios with asymmetric marginal costs, a dynamic pricing strategy called Demand Learning under Collusion (DLC) is developed. If sellers are patient enough, a tactic collusion of a subset of sellers may be formed depending on the marginal costs and underlying demand model. Using the limit of means criterion, DLC is shown to be a subgame-perfect and Pareto-efficient equilibrium. The dynamic pricing strategy offers a bounded regret over an infinite horizon. Using discounting criterion, DLC is shown to be subgame-perfect [epsilon]-equilibrium, [epsilon]-efficient and with an arbitrarily small regret. The dual problem as an infinitely repeated Cournot competition is formulated and the economic efficiency measured by the social welfare is discussed between Bertrand and Cournot formulations. In the second part, we consider an intrusion detection problem and formulate it as a dynamic search of a target located in one of K cells with any fixed number of searches. At each time, one cell is searched, and the search result is subject to false alarms. The objective is a policy that governs the sequential selection of the cells to minimize the error probability of detecting the whereabouts of the target within a fixed time horizon. We show that the optimal search policy is myopic in nature with a simple structure. In the third part, we consider the shortest path routing problem in a communication network with random link costs drawn from unknown distributions. A realization of the total end-to-end cost is obtained when a path is selected for communication. The objective is an online learning algorithm that minimizes the total expected communication cost in the long run. The problem is formulated as a multi-armed bandit problem with dependent arms, and an algorithm based on basis-based learning integrated with a Best Linear Unbiased Estimator (BLUE) is developed.

Download or read book Pricing and Revenue Optimization written by Robert Phillips and published by Stanford University Press. This book was released on 2005-08-05 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of "yield management" in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact [email protected].

Download or read book Operations Research and Enterprise Systems written by Greg H. Parlier and published by Springer. This book was released on 2019-03-14 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the 7th International Conference on Operations Research and Enterprise Systems, ICORES 2018, held in Funchal, Madeira, Portugal, in January 2018. The 12 papers presented in this volume were carefully reviewed and selected from a total of 59 submissions. They are organized in topical sections named: methodologies and technologies; and applications.

Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.