Showing papers on "Dynamic pricing published in 2009"
TL;DR: The value of quick response to a retailer is generally much greater in the presence of strategic consumers than without them, and provides more value by allowing a retailer to control the negative consequences of strategic consumer behavior.
Abstract: We consider a retailer that sells a product with uncertain demand over a finite selling season. The retailer sets an initial stocking quantity and, at some predetermined point in the season, optimally marks down remaining inventory. We modify this classic setting by introducing three types of consumers: myopic consumers, who always purchase at the initial full price; bargain-hunting consumers, who purchase only if the discounted price is sufficiently low; and strategic consumers, who strategically choose when to make their purchase. A strategic consumer chooses between a purchase at the initial full price and a later purchase at an uncertain markdown price. In equilibrium, strategic consumers and the retailer make optimal decisions given their rational expectations regarding future prices, availability of inventory, and the behavior of other consumers. We find that the retailer stocks less, takes smaller price discounts, and earns lower profit if strategic consumers are present than if there are no strategic consumers. We find that a retailer should generally avoid committing to a price path over the season (assuming such commitment is feasible)---committing to a markdown price (or to not mark down at all) is often too costly (inventory may remain unsold) even in the presence of strategic consumers; the better approach is to be cautious with the initial quantity and then mark down optimally. Furthermore, we discuss the value of quick response (the ability to procure additional inventory after obtaining updated demand information, albeit at a higher unit cost than the initial order). We find that the value of quick response to a retailer is generally much greater in the presence of strategic consumers than without them: on average 67% more valuable and as much as 558% more valuable in our sample. In other words, although it is well established in the literature that quick response provides value by allowing better matching of supply with demand, it provides more value, often substantially more value, by allowing a retailer to control the negative consequences of strategic consumer behavior.
564 citations
TL;DR: In this article, the authors consider a single-product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues.
Abstract: We consider a single-product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve) is not known. We consider two instances of this problem: (i) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and (ii) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function “on the fly,” and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is “close” to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function, manifested as the revenue loss due to model uncertainty.
382 citations
Posted Content•
TL;DR: A single-product revenue management problem where the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues, and proposed algorithms develop policies that learn the demand function “on the fly,” and optimize prices based on that.
Abstract: We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known. We consider two instances of this problem: i.) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and ii.) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function "on the fly," and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is "close" to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function; manifested as the revenue loss due to model uncertainty.
367 citations
TL;DR: In this article, the authors estimate the cost of installing smart meters in the EU to be €51 billion, and that operational savings will be worth between €26 to 41 billion, leaving a gap of €10 to 25 billion between benefits and costs.
Abstract: We estimate the cost of installing smart meters in the EU to be €51 billion, and that operational savings will be worth between €26 to 41 billion, leaving a gap of €10 to 25 billion between benefits and costs Smart meters can fill this gap because they enable the provision of dynamic pricing, which reduces peak demand The present value of savings in peaking infrastructure could be as high as €67 billion for the EU if policy-makers can overcome barriers to consumers adopting dynamic tariffs, but only €14 billion otherwise We outline a number of ways to increase the adoption of dynamic tariffs
317 citations
TL;DR: It is demonstrated that strategic behavior by consumers can have serious impacts on revenues if firms ignore that behavior in their dynamic pricing policies, and ideal equilibrium responses to consumer strategic behavior can recover only a portion of the lost revenues.
Abstract: We present a dynamic pricing model for oligopolistic firms selling differentiated perishable goods to multiple finite segments of strategic consumers who are aware that pricing is dynamic and may time their purchases accordingly. This model encompasses strategic behavior by both firms and consumers in a unified stochastic dynamic game in which each firm's objective is to maximize its total expected revenues, and each consumer responds according to a shopping-intensity-allocation consumer choice model. We prove the existence of a unique subgame-perfect equilibrium, provide equilibrium optimality conditions, and prove monotonicity results for special cases. The model provides insights about equilibrium price dynamics under different levels of competition, asymmetry between firms, and multiple market segments with varying properties. We demonstrate that strategic behavior by consumers can have serious impacts on revenues if firms ignore that behavior in their dynamic pricing policies. Moreover, ideal equilibrium responses to consumer strategic behavior can recover only a portion of the lost revenues. A key conclusion is that firms may benefit more from limiting the information available to consumers than from allowing full information and responding to the resulting strategic behavior in an optimal fashion.
304 citations
TL;DR: It is proved that dynamic pricing converges to static pricing as inventory levels of all variates approach the number of remaining selling periods, and a computationally efficient approach to the initial inventory decision is proposed, which delivers close-to-optimal inventory levels for all testing cases.
Abstract: We study dynamic pricing and inventory control of substitute products for a retailer who faces a long supply lead time and a short selling season. Within a multinomial logit model of consumer choice over substitutes, we develop a stochastic dynamic programming formulation and derive the optimal dynamic pricing policy. We prove that dynamic pricing converges to static pricing as inventory levels of all variates approach the number of remaining selling periods (assuming at most one customer arrival within each period). Our extensive numerical study of the effects of time and inventory depletion on the optimal pricing reveals two fundamental underlying driving forces of the complex price behavior: the level of inventory scarcity and the quality difference among products. We also compare the performance of three restricted pricing strategies: static, unified dynamic, and mixed dynamic pricing. We find that full-scale dynamic pricing is of great value in the presence of inventory scarcity, and initial inventory decisions are quite robust in the pricing scheme employed in the selling season. Based on the above insights, we propose a computationally efficient approach to the initial inventory decision, which delivers close-to-optimal inventory levels for all testing cases.
301 citations
TL;DR: This paper shows how these well-studied revenue management problems can be reduced to a common formulation in which the firm controls the aggregate rate at which all products jointly consume resource capacity, highlighting their common structure, and leading to algorithmic simplifications through the reduction in the control dimension of the associated optimization problems.
Abstract: This chapter reviews multi-product dynamic pricing models for a revenue maximizing monopolist firm. The baseline model studied in this chapter is of a seller that owns a fixed capacity of a resource that is consumed in the production or delivery of some type of product. The seller selects a dynamic pricing strategy for the offered product so as to maximize its total expected revenues over a finite time horizon. We then review how this model can be extended to settings where the firm is selling multiple products that consume this firm's capacity, and finally highlight a connection between these dynamic pricing models and the closely related model where prices are fixed, and the seller dynamically controls how to allocate capacity to requests for the different products. Methodologically, this chapter reviews the dynamic programming formulations of the above problems, as well as their associated deterministic (fluid) analogues. It highlights some of the key insights and pricing heuristics that are known for these problems, and briefly mentions possible extensions and areas of current interest.
239 citations
TL;DR: In this article, a retailer is endowed with a finite inventory of a nonperishable product and demand for this product is driven by a price sensitive Poisson process that depends on an unknown parameter that is a proxy for the market size.
Abstract: A retailer is endowed with a finite inventory of a nonperishable product Demand for this product is driven by a price-sensitive Poisson process that depends on an unknown parameter that is a proxy for the market size The retailer has a prior belief on the value of this parameter that he updates as time and available information (prices and sales) evolve The retailer's objective is to maximize the discounted long-term average profits of his operation using dynamic pricing policies We consider two cases In the first case, the retailer is constrained to sell the entire initial stock of the nonperishable product before a different assortment is considered In the second case, the retailer is able to stop selling the nonperishable product at any time and switch to a different menu of products For both cases, we formulate the retailer's problem as a (Poisson) intensity control problem and derive structural properties of an optimal solution, and suggest a simple and efficient approximated solution We use numerical computations, together with asymptotic analysis, to evaluate the performance of our proposed policy
188 citations
TL;DR: In this paper, a game-theoretical model of a retailer who sells a limited inventory of a product over a finite selling season by using one of two inventory display formats: display all (DA) and display one (DO).
Abstract: We propose a game-theoretical model of a retailer who sells a limited inventory of a product over a finite selling season by using one of two inventory display formats: display all (DA) and display one (DO). Under DA, the retailer displays all available units so that each arriving customer has perfect information about the actual inventory level. Under DO, the retailer displays only one unit at a time so that each customer knows about product availability but not the actual inventory level. Recent research suggests that when faced with strategic consumers, the retailer could increase expected profits by making an upfront commitment to a price path. We focus on such pricing strategies in this paper, and study the potential benefit of DO compared to DA, and its effectiveness in mitigating the adverse impact of strategic consumer behavior. We find support for our hypothesis that the DO format could potentially create an increased sense of shortage risk, and hence it is better than the DA format. However, although potentially beneficial, a move from DA to DO is typically very far from eliminating the adverse impact of strategic consumer behavior. We observe that, generally, it is not important for a retailer to modify the level of inventory when moving from a DA to a DO format; a change in the display format, along with an appropriate price modification, is typically sufficient. Interestingly, across all scenarios in which a change in inventory is significantly beneficial, we observed that only one of the following two actions takes place: either the premium price is increased along with a reduction in inventory, or inventory is increased along with premium price reduction. We find that the marginal benefit of DO can vary dramatically as a function of the per-unit cost to the retailer. In particular, when the retailer's per-unit cost is relatively high, but not too high to make sales unprofitable or to justify exclusive sales to high-valuation customers only, the benefits of DO appear to be at their highest level, and could reach up to 20% increase in profit. Finally, we demonstrate that by moving from DA to DO, while keeping the price path unchanged, the volatility of the retailer's profit decreases.
178 citations
TL;DR: An economic framework is proposed that can be used to guide i) the dynamic spectrum allocation process and ii) the service pricing mechanisms that the providers can use and it is demonstrated how pricing can be use as an effective tool for providing incentives to the WSPs to upgrade their network resources and offer better services.
Abstract: The concept of dynamic spectrum access will allow the radio spectrum to be traded in a market like scenario allowing wireless service providers (WSPs) to lease chunks of spectrum on a short-term basis. Such market mechanisms will lead to competition among WSPs where they not only compete to acquire spectrum but also attract and retain users. Currently, there is little understanding on how such a dynamic trading system will operate so as to make the system feasible under economic terms. In this paper, we propose an economic framework that can be used to guide i) the dynamic spectrum allocation process and ii) the service pricing mechanisms that the providers can use. We propose a knapsack based auction model that dynamically allocates spectrum to the WSPs such that revenue and spectrum usage are maximized. We borrow techniques from game theory to capture the conflict of interest between WSPs and end users. A dynamic pricing strategy for the providers is also proposed. We show that even in a greedy and non-cooperative behavioral game model, it is in the best interest of the WSPs to adhere to a price and channel threshold which is a direct consequence of price equilibrium. Through simulation results, we show that the proposed auction model entices WSPs to participate in the auction, makes optimal use of the spectrum, and avoids collusion among WSPs. We demonstrate how pricing can be used as an effective tool for providing incentives to the WSPs to upgrade their network resources and offer better services.
164 citations
TL;DR: In this paper, the authors studied oligopoly firms' dynamic pricing strategies in a gasoline market before and after the introduction of a unique law that constrains firms to set price simultaneously and only once per day.
Abstract: This paper studies oligopoly firms’ dynamic pricing strategies in a gasoline market before and after the introduction of a unique law that constrains firms to set price simultaneously and only once per day. The observed gasoline pricing behavior, both before and under the law, is well captured by the Edgeworth price cycle equilibrium in the Maskin and Tirole dynamic oligopoly model. My results highlight the importance of price commitment in tacit collusion. I also find evidence that the price leadership outcome under the law is better predicted by mixed strategies play than by alternative hypotheses.
TL;DR: In this article, the authors analyse the pricing policy adopted by Ryanair, the main low-cost carrier in Europe, and find a positive correlation between the average fare for each route and its length, the frequency of flights operating on that route, and the percentage of fully booked flights.
22 Oct 2009
TL;DR: An overview of pricing models for software is provided, which discusses the six parameters formation of prices, structure of payment flow, assessment base, price discrimination, price bundling, and dynamic pricing strategies.
Abstract: Due to the economic characteristics specific to the software industry, pricing concepts existing in other industries cannot be transferred without adaptation. Therefore, this article provides an overview of pricing models for software. In this context we discuss the six parameters formation of prices, structure of payment flow, assessment base, price discrimination, price bundling, and dynamic pricing strategies. Furthermore, we refer to recent software delivery models, such as Software as a service. The results are based on literature research and empirical studies.
TL;DR: In this article, the authors investigate whether dynamic targeted pricing based on consumer purchase history could benefit a practicing firm even when consumers are "strategic" in that they actively seek to avail themselves of a low price in the future.
Patent•
17 Aug 2009
TL;DR: In this paper, a dynamic pricing system for complex energy securities, comprising a communications interface executing on a network-connected server and adapted to receive information from a plurality of iNodes, an event database coupled to the communications interface, and a pricing server coupled with the event database and the pricing server, is described.
Abstract: A dynamic pricing system for complex energy securities, comprising a communications interface executing on a network-connected server and adapted to receive information from a plurality of iNodes, an event database coupled to the communications interface and adapted to receive events from a plurality of iNodes via the communications interface, a pricing server coupled to the communications interface, and a statistics server coupled to the event database and the pricing server, is disclosed. According to the invention, the pricing server, on receiving a request to establish a price for an energy security, requests at least one statistical indicia of risk from the statistics server, the statistical indicia of risk being computed by the statistics server based on a plurality of historical data obtained from the event database, and the pricing server computes a price for the security based at least in part on the statistical indicia of risk.
TL;DR: In this paper, the authors used data from a generic California utility to develop dynamic pricing rates for all customer classes and showed that these rates have the potential to reduce system peak demands from 1 to 9 percent.
TL;DR: In this paper, a two-period pricing and ordering model for a dominant retailer with demand uncertainty in a declining price environment is presented, and it is shown that the maximum expected profit function is continuous concave.
Abstract: Retailing channels are increasingly being dominated by ‘power’ retailers who are in a position to dictate prices and ordering schedules to manufacturers and suppliers. A dominant retailer, such as Wal-Mart, has the ‘power’ to decide retail prices of products because there are so many manufacturers who are keen to sell their products through or to such a large and powerful retailer. Several products, such as electronic products, can be sold in the market for some periods during their lifecycles before they retreat, except when they are not popular with consumers after been introduced. Therefore, in case of such products, the retailer should not just consider a single-period pricing and ordering policy. It should make dynamic pricing and ordering decisions based on market demand forecast, in order to obtain maximum cumulative profit from the product during its lifecycle. In this study, we consider this scenario and construct a two-period model to discuss pricing and ordering problems for a dominant retailer with demand uncertainty in a declining price environment. We show that the maximum expected profit function is continuous concave, so the optimal solution to pricing and ordering policy exists and it is the one and only. We also analyze sensitivity of retailer's expected profit to the effects of parameters of price-discount sharing scheme and market demand.
TL;DR: A two-stage pricing scheme that sets in a first-stage a time-of-use tariff that is corrected later by a dynamic component once the real-time demand has been observed.
Abstract: This paper aims at defining a dynamic and flexible tariff structure for a distribution company that protects the retail consumers against the excessive fluctuations of the wholesales market prices. We propose a two-stage pricing scheme that sets in a first-stage a time-of-use tariff that is corrected later by a dynamic component once the real-time demand has been observed. A personalized tariff scheme may be offered by a distribution company to each dynamic customer by allowing him to choose the appropriate robustness level expressed in terms of variability between the first and the second-stage decisions. The arising limited recourse model has been tested on realistic test problems, by using a slight modification of a recently proposed interior point solution framework.
TL;DR: Extensive numerical experiments show the value- and policy-approximation approaches to work well across a wide range of problem parameters, and to outperform the pooling-based heuristics in most cases.
TL;DR: A game-theoretic model is developed to describe real-time dynamic price competition between firms that sell substitutable products and shows the existence of Nash equilibrium.
TL;DR: This paper studies a monopolist firm selling a fixed capacity and finds that by facilitating resale, the firm can mimic dynamic pricing outcomes and enjoy the associated benefits while charging a fixed price.
Abstract: This paper studies a monopolist firm selling a fixed capacity. The firm sets a price before demand uncertainty is resolved. Speculators may enter the market purely with the intention of resale, which can be profitable if demand turns out to be high. Consumers may strategically choose when to purchase, and they may also choose to purchase from the firm or from the speculators. We characterize equilibrium prices and profits and analyze the long run capacity decisions of the firm. There are three major findings. First, the presence of speculators increases the firm's expected profits even though the resale market competes with the firm. Second, by facilitating resale, the firm can mimic dynamic pricing outcomes and enjoy the associated benefits while charging a fixed price. Third, speculative behavior may generate incentives for the seller to artificially restrict supply and thus may lead to lower capacity investments. We also explore several model extensions that highlight the robustness of our results.
TL;DR: In this article, the problem of dynamic pricing of web content on a site where revenue is generated from subscription fee as well as advertisements was studied, and the optimal control theory was used to obtain the subscription fee and the advertisement level over time.
TL;DR: It is shown that promotions are useful when frequent shoppers are willing to pay more for the product than are occasional shoppers and several model extensions to study the impact of consumer stockpiling on the seller's inventory, production, and rationing strategies are developed.
Abstract: We study a dynamic pricing problem for a class of products with stable consumption patterns (e.g., household items, staple foods). Consumers may stock up the product at current prices for future consumption, but they incur inventory holding costs. We model this situation as a dynamic game over an infinite time horizon: in each period, the seller sets a price, and each consumer chooses how many units to buy. We develop a solution methodology based on rational expectations. By endowing each player with beliefs, we decouple the dynamic game into individual dynamic programs for each player. We solve for the rational expectations equilibrium, where all players make optimal dynamic decisions given correct beliefs about others' behavior. In equilibrium, the seller may either charge a constant fixed price or offer periodic price promotions at predictable time intervals. We show that promotions are useful when frequent shoppers are willing to pay more than occasional shoppers for the product. We also develop several model extensions to study the impact of consumer stockpiling on the seller's inventory, production, and rationing strategies.
TL;DR: Numerical experiments demonstrate that the learning procedure is robust to deviations of the actual market from the model of the market used in learning, which is based on a game-theoretic consumer choice model.
Abstract: We study the problem faced by a monopolistic company that is dynamically pricing a perishable product or service and simultaneously learning the demand characteristics of its customers. In the learning procedure, the company observes the sales history over consecutive learning stages and predicts consumer demand by applying an aggregating algorithm (AA) to a pool of online stochastic predictors. Numerical implementation uses finite-sample distribution approximations that are periodically updated using the most recent sales data. These are subsequently altered with a random step characterizing the stochastic predictors. The company's pricing policy is optimized with a simulation-based procedure integrated with AA. The methodology of the paper is general and independent of specific distributional assumptions. We illustrate this procedure on a demand model for a market in which customers are aware that pricing is dynamic, may time their purchases strategically, and compete for a limited product supply. We derive the form of this demand model using a game-theoretic consumer choice model and study its structural properties. Numerical experiments demonstrate that the learning procedure is robust to deviations of the actual market from the model of the market used in learning.
TL;DR: In this paper, an integrated real options (IRO) approach with analytic hierarchy process (AHP) for the auction RM problem under competitive/dynamic pricing and revenue uncertainty in Internet retailing is presented.
Abstract: Competition and demand volatility often cause modern enterprises to be confronted by uncertain environments. When a firm manages revenue in such competitive and risky environments, the optimization of pricing and capacity allocation, subject to a fixed time and capacity, becomes a complicated problem. Many previous papers concerning revenue management (RM) and pricing require that the firm possesses the ability to know the demand curve (or demand distribution) and set prices on it to maximize profits. However, this assumption may not be the case in some industries. Therefore, this paper focuses on the dynamic lead indicators rather than assumptive lag indicators to establish a concise and flexible decision model for practical use. This paper provides an integrated real options (IRO) approach with analytic hierarchy process (AHP) for the auction RM problem under competitive/dynamic pricing and revenue uncertainty in Internet retailing. A numerical example is also presented to illustrate that the IRO approach can generate better decisions than the nai¨ve (or risk unawareness) approach in revenue quality of safety and profitability. The new perspective and approach proposed by this paper can be extended to other RM fields whenever both profitability and risk are critical to decision making.
TL;DR: A Markov decision process-based pricing model is developed that recognizes the need to balance utilization of delivery capacity by the grocer and theneed to have the goods delivered at the most convenient time for the customer.
TL;DR: In this paper, the effect of free software offer on the diffusion of new software has been formally analyzed, and the authors show that even if other benefits do not exist, a software firm can still benefit from giving away fully functioning software, due to the accelerated diffusion process and subsequently the increased net present value of future sales.
Abstract: Many software products are available free of charge. While the benefits resulting from network externality have been examined in the related literature, the effect of free offer on the diffusion of new software has not been formally analyzed. We show in this study that even if other benefits do not exist, a software firm can still benefit from giving away fully functioning software. This is due to the accelerated diffusion process and subsequently the increased net present value of future sales. By adapting the Bass diffusion model to capture the impact of free software offer, we provide a methodology to determine the optimal number of free adopters. We show that the optimal free offer solution depends on the discount rate, the length of the demand window, and the ratio of low-valuation to high-valuation free adopters. Our methodology is shown to be applicable for both fixed and dynamic pricing strategies.
TL;DR: In this paper, the authors introduce a decision model of consumer inertia and find that consumer inertia has both positive and negative effects on profits: it decreases demand (in period one) but intensifies competition among consumers for the product(in period two), which is consistent with well-established behavioral regularities, such as loss aversion and probability weighting in the sense of prospect theory, and hyperbolic time preferences.
Abstract: This paper introduces a decision model of consumer inertia. Consumers exhibit inertia when they have an inherent bias to delay purchases. Inertia may induce consumers to wait even when it is optimal to buy immediately. We embed our decision model within a dynamic pricing context. There is a firm that sells a fixed capacity over two time periods to an uncertain number of both rational and inertial consumers. We find that consumer inertia has both positive and negative effects on profits: it decreases demand (in period one) but intensifies competition among consumers for the product (in period two). We show that our model of inertia is consistent with well-established behavioral regularities, such as loss aversion and probability weighting in the sense of prospect theory, and hyperbolic time preferences. We offer practical recommendations for firms to influence the level of consumer inertia. These include offering returns policies (to mitigate potential consumer losses), providing decision aids (to avoid perception errors), and offering flexible payment options (to lower transaction costs).
TL;DR: In this paper, the authors investigate the effect of price and demand on the profit of a capacity constrained profit function and derive pricing formulas under the assumption that demand for products follows a multinomial logit model, and develop an algorithm for finding a global optimal solution to the capacity-constrained profit function.
Abstract: Capacity providers often experience a mismatch between supply and demand that can be partially alleviated while improving revenues by allowing for product upgrades. When prices are fixed and demands are independent, the problem is to decide which customer demands to upgrade to which products and when. We show that a fairness constraint can be imposed without loss of optimality under mild conditions. We also investigate a model that limits upgrades to the next higher quality product, and we provide necessary and sufficient conditions for its revenues to be as high as that of any less restricted upgrade model. Resellers of capacity also have an incentive to use upgrades as a mechanism to entice customers to higher quality products with higher commission margins. We show that this practice can be very profitable and that the profits can be much larger than direct commissions from sales would indicate. We then investigate the case where sellers have pricing flexibility and customer demand is driven by a choice model. We derive pricing formulas under the assumption that demand for products follows a multinomial logit model, and we develop an algorithm for finding a global optimal solution to the capacity constrained profit function. For this model we show that neither upgrades nor upsells improve profits when margins are homogenous and there is complete freedom in selecting prices. However, upgrades can improve revenues significantly when sensible business constraints on prices are imposed and when margins are heterogenous.
01 Jan 2009
TL;DR: In this paper, the authors present and discuss some relevant theoretical contributions in the management science literature that help us understand the potential value of the above mitigating strategies, such as rationing capacity, making price and capacity commitments, using internal price-matching policies, and limiting inventory information.
Abstract: Dynamic pricing and revenue management practices are gaining increasing popularity in the retail industry, and have engendered a large body of academic research in recent decades. When applying dynamic pricing systems, retailers must account for the fact that, often, strategic customers may time their purchases in anticipation of future discounts. Such strategic consumer behavior might lead to severe consequences on the retailers’ revenues and profitability. Researchers have explored several approaches for mitigating the adverse impact of this phenomenon, such as rationing capacity, making price and capacity commitments, using internal price-matching policies, and limiting inventory information. In this chapter, we present and discuss some relevant theoretical contributions in the management science literature that help us understand the potential value of the above mitigating strategies.