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Dynamic Pricing with Heterogeneous Patience Levels

Dynamic Pricing with Heterogeneous Patience Levels
Author: Ilan Lobel
Publisher:
Total Pages: 28
Release: 2017
Genre:
ISBN:

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We consider the problem of dynamic pricing in the presence of patient consumers. We call a consumer patient if she is willing to wait a certain number of periods for a lower price and will purchase as soon as the price is equal to or below her valuation. We allow for arbitrary joint distributions of patience levels and valuations. We propose an efficient dynamic programming algorithm for finding optimal pricing policies. The dynamic program requires a larger state space than its counterpart for a strategic consumers model. We find numerically that optimal policies can take the form of incomplete cyclic policies, combining features of both nested sales policies and decreasing cyclic policies.


Dynamic Pricing for Heterogeneous Time-Sensitive Customers

Dynamic Pricing for Heterogeneous Time-Sensitive Customers
Author: Negin Golrezaei
Publisher:
Total Pages: 63
Release: 2018
Genre:
ISBN:

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A core problem in the area of revenue management is pricing goods in the presence of strategic customers. We study this problem when customers are heterogeneous with respect to their initial valuations for the item and their time sensitivities, i.e., the customers differ in both their initial valuations and the rates at which their initial valuation decreases with delay in purchase. We characterize the optimal mechanism for selling durable goods in such environments and show that delayed allocation and dynamic pricing can be effective screening tools for maximizing firm profit. We also investigate the impact of production and holding costs on the optimal mechanism.


Personalized Dynamic Pricing with Machine Learning

Personalized Dynamic Pricing with Machine Learning
Author: Gah-Yi Ban
Publisher:
Total Pages: 53
Release: 2020
Genre:
ISBN:

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We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers' characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of T periods. We prove that the seller's expected regret, i.e., the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s √T under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order s √Tlog T. We extend this policy to a more realistic setting where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s √T(log d+logT), which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then- optimize policies. Furthermore, our policy improves upon the loan company's historical pricing decisions by 47% in expected revenue over a six-month period.


Revenue Management and Pricing Analytics

Revenue Management and Pricing Analytics
Author: Guillermo Gallego
Publisher: Springer
Total Pages: 336
Release: 2019-08-14
Genre: Business & Economics
ISBN: 1493996061

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“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.


The Elements of Joint Learning and Optimization in Operations Management

The Elements of Joint Learning and Optimization in Operations Management
Author: Xi Chen
Publisher: Springer Nature
Total Pages: 444
Release: 2022-09-20
Genre: Business & Economics
ISBN: 3031019261

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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.


The Theory and Practice of Revenue Management

The Theory and Practice of Revenue Management
Author: Kalyan T. Talluri
Publisher: Springer Science & Business Media
Total Pages: 731
Release: 2006-02-21
Genre: Business & Economics
ISBN: 0387273913

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Revenue management (RM) has emerged as one of the most important new business practices in recent times. This book is the first comprehensive reference book to be published in the field of RM. It unifies the field, drawing from industry sources as well as relevant research from disparate disciplines, as well as documenting industry practices and implementation details. Successful hardcover version published in April 2004.


Dynamic Allocation and Pricing

Dynamic Allocation and Pricing
Author: Alex Gershkov
Publisher: MIT Press
Total Pages: 209
Release: 2024-06-11
Genre: Business & Economics
ISBN: 0262552442

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A new approach to dynamic allocation and pricing that blends dynamic paradigms from the operations research and management science literature with classical mechanism design methods. Dynamic allocation and pricing problems occur in numerous frameworks, including the pricing of seasonal goods in retail, the allocation of a fixed inventory in a given period of time, and the assignment of personnel to incoming tasks. Although most of these problems deal with issues treated in the mechanism design literature, the modern revenue management (RM) literature focuses instead on analyzing properties of restricted classes of allocation and pricing schemes. In this book, Alex Gershkov and Benny Moldovanu propose an approach to optimal allocations and prices based on the theory of mechanism design, adapted to dynamic settings. Drawing on their own recent work on the topic, the authors describe a modern theory of RM that blends the elegant dynamic models from the operations research (OR), management science, and computer science literatures with techniques from the classical mechanism design literature. Illustrating this blending of approaches, they start with well-known complete information, nonstrategic dynamic models that yield elegant explicit solutions. They then add strategic agents that are privately informed and then examine the consequences of these changes on the optimization problem of the designer. Their sequential modeling of both nonstrategic and strategic logic allows a clear picture of the delicate interplay between dynamic trade-offs and strategic incentives. Topics include the sequential assignment of heterogeneous objects, dynamic revenue optimization with heterogeneous objects, revenue maximization in the stochastic and dynamic knapsack model, the interaction between learning about demand and dynamic efficiency, and dynamic models with long-lived, strategic agents.


To Queue or Not to Queue

To Queue or Not to Queue
Author: Refael Hassin
Publisher: Springer Science & Business Media
Total Pages: 212
Release: 2003
Genre: Business & Economics
ISBN: 9781402072031

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To Queue Or Not To Queue: Equilibrium Behavior in Queueing Systems focuses on the highly interesting, practical viewpoint of customer behavior and its effect on the performance of the queueing system. The book's objectives are threefold: (1) It is a comprehensive survey of the literature on equilibrium behavior of customers and servers in queueing systems. The literature is rich and considerable, but lacks continuity. This book will provide the needed continuity and cover some issues that have not been adequately treated. (2) In addition, it will examine the known results of the field, classify them and identify where and how they relate to each other. (3) And finally, it seeks to fill a number of the gaps in the literature with new results while explicitly outlining open problems in other areas. With this book, it is the authors' paramount purpose is to motivate further research and to help researchers identify new and interesting open problems.


Smart Data Pricing

Smart Data Pricing
Author: Soumya Sen
Publisher: John Wiley & Sons
Total Pages: 533
Release: 2014-09-09
Genre: Technology & Engineering
ISBN: 1118611667

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A comprehensive text addressing the high demand for network, cloud, and content services through cutting-edge research on data pricing and business strategies Smart Data Pricing tackles the timely issue of surging demand for network, cloud, and content services and corresponding innovations in pricing these services to benefit consumers, operators, and content providers. The pricing of data traffic and other services is central to the core challenges of network monetization, growth sustainability, and bridging the digital divide. In this book, experts from both academia and industry discuss all aspects of smart data pricing research and development, including economic analyses, system development, user behavior evaluation, and business strategies. Smart Data Pricing: • Presents the analysis of leading researchers from industry and academia surrounding the pricing of network services and content. • Discusses current trends in mobile and wired data usage and their economic implications for content providers, network operators, end users, government regulators, and other players in the Internet ecosystem. • Includes new concepts and background technical knowledge that will help researchers and managers effectively monetize their networks and improve user quality-of-experience. • Provides cutting-edge research on business strategies and initiatives through a diverse collection of perspectives. • Combines academic and industry expertise from multiple disciplines and business organizations. The ideas and background of the technologies and economic principles discussed within these chapters are of real value to practitioners, researchers, and managers in identifying trends and deploying new pricing and network management technologies, and will help support managers in identifying new business directions and innovating solutions to challenging business problems.