Download or read book Context Based Dynamic Pricing with Separable Demand Models written by Jinzhi Bu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Learning and Pricing with Model Misspecification written by Mila Nambiar and published by . This book was released on 2020 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study a multi-period dynamic pricing problem with contextual information where the seller uses a misspecified demand model. The seller sequentially observes past demand, updates model parameters, and then chooses the price for the next period based on time-varying features. We show that model misspecification leads to correlation between price and prediction error of demand per period, which in turn leads to inconsistent price elasticity estimate and hence suboptimal pricing decisions. We propose a ``random price shock'' (RPS) algorithm that dynamically generates randomized price shocks to estimate price elasticity while maximizing revenue. We show that the RPS algorithm has strong theoretical performance guarantees, that it is robust to model misspecification, and that it can be adapted to a number of business settings, including (1) when the feasible price set is a price ladder, and (2) when the contextual information is not IID. We also perform offline simulations gauging the performance of RPS on a large fashion retail dataset, and find that is expected to earn 8~20% more revenue on average than competing algorithms that do not account for price endogeneity.

Download or read book Behavioral Consequences of Dynamic Pricing written by David Prakash and published by BoD – Books on Demand. This book was released on 2022-08-19 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital technologies are driving the application of dynamic pricing. Today, this pricing strategy is used not only for perishable products such as flights or hotel rooms, but for almost any product or service category. With dynamic pricing, retailers frequently adjust their prices over time to respond to factors such as demand, their supply and that of competitors, or the time of sale. Additionally, dynamic pricing allows retailers to take advantage of a large share of consumers' willingness to pay while avoiding losses from unsold products. Ultimately, this can lead to an increase in revenue and profit. However, the application of dynamic pricing comes with great challenges. In addition to the technological implementation, companies have to take into account that dynamic pricing can cause complex and unintended behavioral consequences on the consumer side. The key objective of this dissertation is to provide a deeper understanding of the impact of dynamic pricing on consumer behavior. To this end, this dissertation presents insights from four perspectives. First, how reference prices as a critical component in purchase decisions are operationalized. Second, how customers search for products priced dynamically, differentiated by business and private customers, as well as by different devices used for the search. Third, whether and how dynamic pricing influences the impact of internal reference prices on purchase decisions. Finally, this dissertation demonstrates that consumers perceive price changes as personalized in different purchase contexts, leading to reduced perceptions of fairness and undesirable behavioral consequences.

Download or read book Pricing and Equilibrium written by Erich Schneider and published by . This book was released on 1952 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Dynamic Pricing with Demand Model Uncertainty written by N. Bora Keskin and published by . This book was released on 2014 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a price-setting firm that sells a product over a continuous time horizon. The firm is uncertain about the sensitivity of demand to price adjustments, and continuously updates its prior belief on an unobservable sensitivity parameter by observing the demand responses to prices. The firm's objective is to minimize the infinite-horizon discounted loss, relative to a clairvoyant that knows the unobservable sensitivity parameter. Using partial differential equations theory, we characterize the optimal pricing policy, and then derive a formula for the optimal learning premium that projects the value of learning onto prices. We compare and contrast the optimal pricing policy with the myopic pricing policy, and quantify the cost of myopically neglecting to charge a learning premium in prices. We show that the optimal learning premium for a firm that looks far into the future is the squared coefficient of variation (SCV) in the firm's posterior belief. Based on this principle, we design a simple variant of the myopic policy, namely the SCV rule, and prove that this policy is long-run average optimal.

Download or read book Dynamic Pricing with an Unknown Demand Model written by N. Bora Keskin and published by . This book was released on 2018 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a monopolist who sells a set of products over a time horizon of T periods. The seller initially does not know the parameters of the products' linear demand curve, but can estimate them based on demand observations. We first assume that the seller knows nothing about the parameters of the demand curve, and then consider the case where the seller knows the expected demand under an incumbent price. It is shown that the smallest achievable revenue loss in T periods, relative to a clairvoyant who knows the underlying demand model, is of order √T in the former case and of order logT in the latter case. To derive pricing policies that are practically implementable, we take as our point of departure the widely used policy called greedy iterated least squares (ILS), which combines sequential estimation and myopic price optimization. It is known that the greedy ILS policy itself suffers from incomplete learning, but we show that certain variants of greedy ILS achieve the minimum asymptotic loss rate. To highlight the essential features of well-performing pricing policies, we derive sufficient conditions for asymptotic optimality.

Download or read book Practical Process for Estimating the Coefficients of Demand Models for Multi product Dynamic Pricing written by 劉心鈺 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Dynamic Pricing with Demand Model Uncertainty written by Mr. Nuri Bora Keskin and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Pricing decisions often involve a tradeoff between learning about customer behavior to increase long-term revenues, and earning short-term revenues. In this thesis we examine that tradeoff. Whenever a firm is not certain about how its customers will respond to price changes, there is an opportunity to use price as a tool for learning about a demand curve. Most firms try to solve the tradeoff between learning and earning by managing these two goals separately. A common practice is to first estimate the parameters of the demand curve, and then choose the optimal price, assuming the parameter estimates are accurate. In this thesis we show that this conventional approach is far from being optimal, running the risk of incomplete learning--a negative statistical outcome in which the decision maker stops learning prematurely. We also propose several remedies to avoid the incomplete learning problem, and guard against poor performance. In Chapter 1, we model a learn-and-earn problem using a theoretical framework in which a seller has a prior belief about the demand curve for its product, and updates his belief upon observing customer responses to successive sales attempts. We assume that the seller's prior is a binary distribution, i.e. one of two demand curves is known to apply, although our analysis can be extended to any finite prior. In this setting, we first analyze the myopic Bayesian policy (MBP), which is a stylized representative of the estimate-and-then-optimize policies described above. Our analysis makes three contributions to the literature: first, we show that under the MBP the seller's beliefs can get stuck at a confounding value, leading to poor revenue performance. This result elucidates incomplete learning as a consequence of myopic pricing. Our second contribution is the development of a constrained variant of the MBP as a way to tweak the MBP in the binary-prior setting. By forbidding prices that are not sufficiently informative, constrained MBP (CMBP) avoids the incomplete learning problem entirely, and moreover, its expected performance gap relative to a clairvoyant who iv knows the underlying demand curve is bounded by a constant independent of the sales horizon. Finally, we generalize the CMBP family to obtain more flexible pricing policies that are suitable in case the seller has an arbitrary prior on model parameters. The incomplete learning result and the pricing policies we design have a practical significance. Because firms have no means to check whether they are suffering from incomplete learning, the myopic policies used in practice need to be modified with some kind of forced price experimentation, and our policies provide guidelines on how price experimentation can be employed to prevent incomplete learning. In Chapter 2, we consider several research questions: for example, when a seller has been charging an incumbent price for a very long time, how can he make use of the information contained in that incumbent price? Or, when a seller offers multiple products with substitutable demand, can he safely employ an independent price experimentation strategy for each product? More importantly, what if the particular pricing policies in literature are not feasible in a given business setting? To handles such cases, can we derive general principles that identify the essential ingredient of successful price experimentation policies? We address these questions using a fairly general dynamic pricing model, where a monopolist sells a set of products over a given time horizon. The expected demand for products is given by a linear curve, the parameters of which are not known by the seller. The seller's goal is to learn the parameters of the demand curve as he keeps trying to earn revenues. This chapter makes four main contributions to the learning-and-earning literature. First, we formulate an incumbent-price problem, where the seller starts out knowing one point on its demand curve, and show that the value of information contained in the incumbent price is substantial. Second, unlike previous studies that focus on a particular form of price experimentation, we derive general sufficient conditions for accumulating information in a near-optimal manner. We believe that practitioners can use these conditions as guidelines to design successful pricing policies in various settings. Third, we develop a unifying theme to obtain performance bounds in operations management problems with model uncertainty. We employ (i) the concept of Fisher information to derive natural lower bounds on regret, and (ii) martingale theory to analyze the estimation errors and generate well-performing policies. Finally, we analyze the pricing of multiple products with substitutable demand. Our analysis shows that multi-product pricing is not a straightforward repetition of single-product pricing. Learning in a high dimensional price space essentially requires sufficient "variation" in the directions of successive price vectors, which brings forth the idea of orthogonal pricing. In Chapter 3, we extend our analysis to the case where information can become obsolete. The particular dynamic pricing problem we consider includes a seller who tries to simultaneously learn about a time-varying demand curve, and earn sales revenues. We conduct a simulation study to evaluate the revenue performance of several pricing policies in this setting. Our results suggest that policies designed for static demand settings do not perform well in time-varying demand settings. Moreover, if the demand environment is not very noisy and the changes are not very frequent, a simple modification of the estimate-and-then-optimize approach, which is based on a moving time window, performs reasonably well in changing demand environments.

Download or read book Handbook of Pricing Research in Marketing written by Vithala R. Rao and published by Edward Elgar Publishing. This book was released on 2009 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pricing is an essential aspect of the marketing mix for brands and products. Further, pricing research in marketing is interdisciplinary, utilizing economic and psychological concepts with special emphasis on measurement and estimation. This unique Handbook provides current knowledge of pricing in a single, authoritative volume and brings together new cutting-edge research by established marketing scholars on a range of topics in the area. The environment in which pricing decisions and transactions are implemented has changed dramatically, mainly due to the advent of the Internet and the practices of advance selling and yield management. Over the years, marketing scholars have incorporated developments in game theory and microeconomics, behavioral decision theory, psychological and social dimensions and newer market mechanisms of auctions in their contributions to pricing research. These chapters, specifically written for this Handbook, cover these various developments and concepts as applied to tackling pricing problems. Academics and doctoral students in marketing and applied economics, as well as pricing-focused business practitioners and consultants, will appreciate the state-of-the-art research herein.

Download or read book Experience Curves and Dynamic Demand Models written by Robert J. Dolan and published by . This book was released on 1979* with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Personalized Dynamic Pricing with Machine Learning written by Gah-Yi Ban and published by . This book was released on 2020 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book A Dynamic Pricing Model for Coordinated Sales and Operations written by David F. Pyke and published by . This book was released on 2007 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in operations and supply chain management. The interactions between pricing and operations/supply chain performance, however, are not as well understood. In this paper, we examine this linkage by developing a deterministic, finite-horizon dynamic programming model that captures a price/demand effect as well as a stockpiling/consumption effect - price and market stockpile influence demand, demand influences consumption, and consumption influences the market stockpile. The decision variable is the unit sales price in each period. Through the market stockpile, pricing decisions in a given period explicitly depend on decisions in prior periods. Traditional operations models typically assume exogenous demand, thereby ignoring some of the market dynamics. Herein, we model endogenous demand, and we develop analytical insights into the nature of optimal prices and promotions. We develop conditions under which the optimal prices converge to a constant. In other words, price promotion is suboptimal. We also analytically and numerically illustrate cases where the optimal prices vary over time. In particular, we show that both revenue effects, due to nonlinear market responses to prices and/or inventory, and cost effects, due to economies of scale in operations may drive price dynamics. The paper concludes with a discussion of directions for future research.

Download or read book Contemporary Issues in Supply Chain Management and Logistics written by Anthony M. Pagano and published by Business Expert Press. This book was released on 2017-04-26 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of chapters on issues we face today in the world of supply chain management. While there are a number of college textbooks related to specific areas within logistics and supply chain issues, there are very few general supply chain management “trends” books. Contemporary Issues in Supply Chain Management and Logistics consists of seven dynamic, current and informative chapters that cover a variety of cutting-edge supply chain topics of use to both graduate students, and professionals working in the field. The book contains new, original research papers written by academics from the fields of engineering, transportation, marketing, and supply chain management and logistics.

Download or read book Habit Formation in a Discrete Choice Model of Recreation Demand written by Wiktor L. Adamowicz and published by . This book was released on 1991 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in Theory and Practice in Store Brand Operations written by Jiazhen Huo and published by Springer Nature. This book was released on 2021-01-04 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is developed by focusing on the four issues: (1) product strategy of private brand; (2) pricing strategy of private brand; (3) channel strategy with private brand introduction; and (4) supply chain coordination with private brand introduction. Private brand (PB), also known as private label (PL) or store brand (SB), refers to a brand created and controlled by a retailer. In the 1960s and 1970s, private labels began to emerge in France and England. Although private label has grown rapidly worldwide, market share varies greatly from region to region. According to Nielsen's 2018 Global Private Label Report, the largest markets for private-label products are found primarily in the more mature European retail markets. In recent years, many large domestic retail enterprises have launched their own brand products. With the growth of e-commerce, some online retailers have also launched private-label goods. JD started to introduce its private brands in 2010, with annual sales of its private brand products reaching several hundred million yuan. However, at present, the market share of China's private label is only 1-3%, which still has a big gap compared with Europe and America.The main challenges to China's private label lie in private brand operations management. Among them, how to select the correct product categories, how to make pricing decision, how to restructure channels and how to coordinate supply chain after introducing private brands are four operations management problems need to be solved.

Download or read book Handbook of Production Economics written by Subhash C. Ray and published by Springer Nature. This book was released on 2022-06-02 with total page 1797 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume handbook includes state-of-the-art surveys in different areas of neoclassical production economics. Volumes 1 and 2 cover theoretical and methodological issues only. Volume 3 includes surveys of empirical applications in different areas like manufacturing, agriculture, banking, energy and environment, and so forth.