考虑顾客策略行为的易逝品定价与再制造柔性补货机制研究

本文将再制造作为一种柔性补货机制,研究了考虑顾客策略行为的易逝品定价和再制造柔性补货问题。研究表明顾客策略行为减少了零售商的期望利润,再制造柔性补货机制可以缓解顾客策略行为的影响,提高零售商的期望利润,最后分析了该机制对消费者剩余和社会福利的影响。     

竞争环境下基于顾客策略行为的易逝品动态定价研究

在两个提供相同易逝品的零售商折线竞争下,从顾客策略行为出发,研究竞争环境下两零售商的动态定价策略。首先建立供大于求的情况下两零售商动态定价模型,给出了均衡价格满足的条件,并探讨了在一些特殊情况下均衡价格的特性。然后将这一模型扩展到供小于求的情形,探讨模型的求解方法。算例分析了在折线竞争模式下零售商在面对顾客策略行为时,如何动态的决定价格。同时发现,在供大于求的情况下,顾客策略行为导致零售商的收益降低;在供小于求的情况下,一定程度的顾客策略行为可以使零售商获得更高的期望收益。     

Dynamic pricing in the presence of consumer inertia

Customer behavior modeling has gained increasing attention in the context of dynamic pricing. As an important behavior phenomenon, consumer inertia refers to consumers' inherent tendency of purchase procrastination and may induce consumers to wait even when immediate purchase is optimal from an objective perspective. This paper studies a dynamic pricing problem for a monopolist firm selling perishable goods to consumers who may be influenced by inertia. We formulate this problem using the finite-horizon dynamic programming approach and derive the optimal dynamic pricing policy. We demonstrate that consumer inertia produces negative effects on firms' expected revenues and optimal prices, which are monotonically decreasing in both inertia depth and breadth. Through numerical illustrations, we further show that the marginal effects of inertia depth on optimal prices and expected revenues are decreasing, whereas the marginal effects of inertia breadth are increasing. Finally we propose some suggestions for firms to influence the level of consumer inertia.     

考虑残值变化和顾客异质性的零售商定价与库存决策

本文研究策略型消费者对零售商的定价和库存决策的影响。与以往的研究假设残值固定不变,且消费者均为策略型消费者不同的是,本文考虑了顾客的异质性以及残值由清仓期库存决定这一特点。本文的研究表明:在消费者均为策略型消费者,且清楚零售商将采取残值定价策略的情况下,零售商可以通过数量保证策略来提高自身的期望利润,降低库存订货量。在考虑顾客异质性的条件下,当零售商采取估值定价策略时,零售商的最大期望利润与短视型消费者在市场上的比例成正相关。并且,存在一个唯一的阈值点,当短视型消费者在市场上的比例高于这一阈值点时,估值定价策略优于保留价格定价策略,反之,则保留价格定价策略更优。     

A Choice‐Based Dynamic Programming Approach for Setting Opaque Prices

Opaque pricing is a form of pricing where certain characteristics of the product or service are hidden from the consumer until after purchase. In essence, opaque selling transforms a differentiated good into a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated product at higher prices to regular brand loyal customers while simultaneously selling to non‐brand loyal customers at discounted prices. We use a nested logit model in combination with logistic regression and dynamic programming to illustrate how a service firm can optimally set prices on an opaque sales channel. The choice model allows the characterization of consumer trade‐offs when purchasing opaque products while the dynamic programming approach allows the characterization of the optimal pricing policy as a function of inventory and time remaining. We compare optimal prices and expected revenues when dynamic pricing is restricted to daily price changes. We provide an illustrative example using data from an opaque selling mechanism ( Hotwire.com ) and a Washington DC‐based hotel.     

考虑战略顾客行为时的供应链性能分析与协调

动态定价策略的广泛应用使得越来越多的顾客具有了战略性,战略顾客会根据产品在销售期内的价格路径确定最优购买时机,零售商则根据顾客的购买行为确定订货数量和销售价格.研究了双方静态博弈时的理性预期均衡解和零售商进行数量承诺时的情形.研究表明:理性预期均衡时的最优销售价格、最优存货数量和最优期望利润分别小于标准报童模型的情形;数量承诺时的最优存货数量小于理性预期均衡时的最优存货数量;最优期望利润则大于理性预期均衡时的最优期望利润,并且在一定条件下可能会大于标准报童模型的最优期望利润,战略顾客行为的存在对零售商可能有利.最后分析了在分散式供应链中如何利用收入分享契约和数量折扣契约实现供应链协调.     

双边市场中用户满意度与平台战略的选择

目前,有关双边市场理论的研究多在单期博弈模型中展开,并未在多期的情况下考虑平台的动态定价问题.显然,这与两边用户总是在平台中展开多次交易,平台总是面临动态定价的客观现实相违背.基于此,本文构建了一个双寡头两期动态博弈模型,通过引入用户满意度,研究了双边平台在不同定价策略(统一定价策略和歧视定价策略)和不同战略(短期战略和长期战略)情形下的最优定价,比较了策略间和战略间的差异性和有效性.研究发现: 第一期获得较大市场份额并不能保证平台在第二期竞争中占据优势地位,也不能保证获得较高重复购买率;歧视定价策略比统一定价策略更有利于平台获得较高利润,而不利于两边用户效用和社会福利水平的提升;(歧视定价策略,歧视定价策略)是帕累托上策均衡解;两期社会福利总水平仅仅与平台的定价策略有关,而与平台的战略无关.     

存在战略购买需求的定价、供给、机制选择和配给研究

本文研究存在战略购买需求的易逝资产销售策略问题。垄断厂商基于利润最大化目标确定易逝资产定价、供给、机制选择和配给策略,战略消费者通过锚定预期价格安排战略购买时机。不同于通常基于效用理论研究定价的思路,本文首先基于锚定效应和跨期价格均衡思想探寻不同战略等待购买规模的市场预期需求曲线和动静态定价区域;其次在众多预期需求曲线中寻找市场有效定价前沿（即有效预期需求曲线）;再次在利润曲面上找出与有效定价前沿对应的容量扩展线（即最大利润曲线）;最后沿容量扩展线和有效定价前沿搜寻最大期望利润及相应策略。研究表明,消费者保留价异质和需求不确定性是动态定价和战略购买存在的根本原因;市场在不同战略等待购买规模状态拥有不同预期需求曲线,最大战略等待购买规模状态预期需求曲线是市场有效定价前沿。动静态定价机制各有其所适用的容量和价格空间,消费者保留价水平和战略消费者规模决定动态定价空间大小,随机需求分布差异只影响动态定价空间形状（即影响需求弹性）。在跨期价格均衡区域内,提价和扩容都会加剧消费者战略购买程度,供给越大定价往往越低。战略购买不仅会降低厂商供给、定价和利润水平,改变不同类型消费者之间高低价购买机会,甚至还可能影响定价机制选择和配给策略。压缩过度供给和虚高价格空间可降低战略购买导致的利润损失。本文研究结果可为考虑消费者行为的需求价格理论研究和运营管理实践提供参考。     

考虑战略顾客行为带预算约束的多产品报童问题

动态定价策略的广泛使用使得越来越多的顾客呈现战略性的特点,即顾客在决定购买时机时不仅会考虑当前的价格是否超过其心理预留价格,同时也会考虑到等到降价时再购买产品的可能性,从而延迟购买决策。研究了一类考虑顾客战略行为且带有预算约束的多产品报童问题,引入理性预期均衡分析,得到了报童和战略顾客双方静态博弈时的理性预计均衡解,并进一步分析了数量承诺对于均衡数量和均衡价格的影响。分析了最优解的性质并给出了求解算法,最后通过数值算例对模型的结论进行了验证。     

Strategic Capacity Management When Customers Have Boundedly Rational Expectations

In retailing industries, such as apparel, sporting goods, customer electronics, and appliances, many firms deploy sophisticated modeling and optimization software to conduct dynamic pricing in response to uncertain and fluctuating market conditions. However, the possibility of markdown pricing creates an incentive for customers to strategize over the timing of their purchases. How should a retailing firm optimally account for customer behavior when making its pricing and stocking/capacity decisions? For example, is it optimal for a firm to create rationing risk by deliberately under stocking products? In this study, we develop a stylized modeling framework to answer these questions. In our model, customers strategize over the timing of their purchases. However, customers have boundedly rational expectations in the sense of anecdotal reasoning about the firm's fill rate, i.e., they have to rely on anecdotes, past experiences, or word‐of‐mouth to infer the firm's fill rate. In our modeling framework, we distinguish two settings: (i) capacity commitment, where the firm commits to its capacity level in the long run, or (ii) the firm dynamically changes it in each season. For both settings, within the simplest form of anecdotal reasoning, we prove that strategic capacity rationing is not optimal independent of customer risk preferences. Then, using a general form of anecdotal reasoning, we provide sufficient conditions for capacity rationing to be optimal for both settings, respectively. We show that the result of strategic capacity rationing being suboptimal is fairly robust to different valuation distributions and utility functions, heterogeneous sample size, and price optimization.     

The Promise of Strategic Customer Behavior: On the Value of Click Tracking

Click tracking is gaining in popularity, and the practice of web analytics is growing fast. Whether strategic customers are willing to visit a website when they know their clicks may be tracked is an important yet complex problem, which depends on various factors. Using a newsvendor framework, we examine this problem by focusing on the operational factor: how product availability induces strategic customers to voluntarily provide advance demand information. We find that a strong Nash equilibrium exists where every customer is willing to click, and customer incentives to click are robust to noise. Hence, we demonstrate the promise of strategic customer behavior in the context of click tracking, contrary to the conventional wisdom that it is typically a peril for the firm. Notably, click tracking is typically advantageous to both the firm and its customers, compared with other strategies such as advance selling, quantity commitment, availability guarantees, and quick response. Lastly, we extend to two settings by including marketing decisions, price‐sensitive demand and markdown pricing, and discuss how operations and marketing decisions interact in influencing the value of click tracking.     

Improving Valuation Under Consumer Search: Implications for Pricing and Profits

There is a growing trend in the retail industry to improve customer experience. In this article, we study retailer‐initiated strategies to increase consumer valuation for a product under duopoly. In such a setting, it is possible that a consumer's valuation may be increased by one retailer; however, the consumer may decide to buy the product from the competitor. We consider a two‐stage game where retailers first decide whether to invest in improvements in customer valuation and then engage in price competition. We computationally explore the Nash equilibria in terms of both investment and pricing. We find that in the majority of cases retailers price in a manner to discourage their local customers to buy from the competitor. Next, we focus on the pricing game and theoretically characterize the pricing Nash equilibrium. We find that a retailer could overcome competitive effects by improving consumer valuation beyond a certain threshold. We also find that a retailer who does not invest could benefit from competition in situations where his competitor increases consumer valuation beyond a threshold. Finally, we explore through a computational study the Nash equilibria of the two‐stage game using an alternate model to establish the robustness of our findings.     

考虑消费者预期的电动汽车分时租赁市场价格竞争模型

分时租赁平台作为电动汽车推广的主要方式，其特征与打车软件类似，在风险资本的推动下极易引发价格战。各租车公司的市场需求量不仅与自己的定价有关，也与竞争对手的定价策略相关，目标都是各自利润的最大化。本文在考虑消费者预期的基础上，研究了需求函数为线性情形下的多人博弈，证明了此博弈是超模博弈以及Nash均衡的存在性和唯一性。并进一步揭示了降价幅度、交叉价格和价格敏感消费群体占比对均衡价格、均衡需求量和利润的影响关系，讨论了价格竞争过程中各租车公司的决策过程与利弊关系。结果表明：任意租车公司的降价行为都会引发整个市场的降价反应，且降价幅度随着博弈阶段的推进逐渐减小，最终收敛于均衡价格；动态定价博弈存在最优组合策略，市场竞争时应当把握好降价幅度，避免过度的价格竞争；均衡价格是关于消费者预期的单调减函数，价格敏感的消费群体占比越大，租车公司间价格竞争越激烈，降价幅度会越大，均衡价格越低；对于市场份额大的租车公司利润是关于价格敏感型消费者占比的单调增函数，但对中小租车公司而言则是单调减函数，价格敏感的消费者越多，对大公司有利，对中小公司不利。     

基于策略消费者行为和市场细分的联合定价库存策略

策略消费者的行为越来越受到厂商的重视,本文在假设消费者为风险规避型,对商品评价异质,考虑策略消费者和非策略消费者并存的细分市场上,比较两阶段定价和单一定价的优劣,研究限量策略实施的条件,进一步扩展至厂商能同时决定库存和价格的情况。研究表明消费者为风险规避型时,两阶段定价策略优于单一定价策略;策略消费者所占比例越大,消费者风险规避程度越高,第二阶段价格折扣越大,厂商越青睐限量的两阶段定价策略。此外,制定适度的价格折扣才会使厂商增加利润,过度的折扣竞争对厂商和消费者都没有好处。     

Dynamic Pricing in the Presence of Myopic and Strategic Consumers: Theory and Experiment

We investigate retailers’ dynamic pricing decisions in a stylized two‐period setting with possible supply constraints and demand from both myopic and strategic consumers. We present an analytical model and then test its predictions in a behavioral experiment in which human subjects played the role of pricing managers. We find that the fraction of strategic consumers in the market systematically moderates the optimal pricing structure. When this fraction exceeds a certain threshold, the retailer offers relatively small late season markdowns to discourage strategic consumers from waiting and to incentivize them to buy during the early season; otherwise, the retailer offers relatively large markdowns to divert all strategic consumers to the late season, where the majority of revenue is made. Our model analyses suggest that the latter policy is optimal under fairly broad conditions. Our experiment shows that after some significant learning, aggregate behavior is able to approximate the key qualitative predictions from our model analysis, with one notable deviation: in the presence of a mixture of myopic and strategic consumers, subjects act somewhat myopically – they underprice and oversell in the main selling season, which significantly limits their ability to generate revenue in the markdown season.     

Randomization Approaches for Network Revenue Management with Customer Choice Behavior

In this study, we present new approximation methods for the network revenue management problem with customer choice behavior. Our methods are sampling‐based and so can handle fairly general customer choice models. The starting point for our methods is a dynamic program that allows randomization. An attractive feature of this dynamic program is that the size of its action space is linear in the number of itineraries, as opposed to exponential. It turns out that this dynamic program has a structure that is similar to the dynamic program for the network revenue management problem under the so called independent demand setting. Our approximation methods exploit this similarity and build on ideas developed for the independent demand setting. We present two approximation methods. The first one is based on relaxing the flight leg capacity constraints using Lagrange multipliers, whereas the second method involves solving a perfect hindsight relaxation problem. We show that both methods yield upper bounds on the optimal expected total revenue. Computational experiments demonstrate the tractability of our methods and indicate that they can generate tighter upper bounds and higher expected revenues when compared with the standard deterministic linear program that appears in the literature.     

考虑策略型消费者和退货的零售商定价和订货决策研究

考虑策略型消费者对产品估值的异质性和无缺陷退货行为，应用理性预期（RE）均衡构建了两期决策模型，引入产品的耐用性参数，得到了允许（不允许）无缺陷退货两种模式下零售商的定价区间和订货策略。研究发现，产品的耐用性越弱，零售商可制定的价格越高；高类型的消费者越多，零售商定价越高；产品折扣率越低，第二阶段购买的消费者越多；零售商允许消费者无缺陷退货时，消费者退货成本越低，零售商定价越高。在两种模式下，产品定价与其耐用性程度呈负相关，当产品具备高度潮流性特征时，不允许退货更占优势；随商品耐用性程度的提升，允许退货带来的优势逐渐明显，而且退货成本越低，允许退货优势越强；随产品耐用性参数增加，允许退货缓解策略型消费者消极影响的作用愈加明显，退货成本越高，缓解越有效。本文合理刻画了策略型消费者的行为，考虑了消费者跨期决策中产品耐用性的影响，同时考虑了现实中广泛存在的消费者无缺陷退货行为与零售商的退货补贴政策，从而为零售商的定价和订货退货决策提供了理论参考。     

顾客策略行为与风险偏好下供应链利润分享

考虑由1个生产商、1个销售商和具有策略行为与风险偏好的顾客群体所组成的供应链.存在顾客策略行为与风险偏好的情况下,利用理性预期均衡的相关知识,分析销售商定价与库存决策,研究供应链利润分享契约问题.研究结果表明:销售商的利润随着顾客风险偏好程度的增加而减少;为了缓解利润削减,销售商需要降低销售价格和订货量,而不是提高销售...     

Committed Versus Contingent Pricing Under Competition

Should capacitated firms set prices responsively to uncertain market conditions in a competitive environment? We study a duopoly selling differentiated substitutable products with fixed capacities under demand uncertainty, where firms can either commit to a fixed price ex ante, or elect to price contingently ex post, e.g., to charge high prices in booming markets, and low prices in slack markets. Interestingly, we analytically show that even for completely symmetric model primitives, asymmetric equilibria of strategic pricing decisions may arise, in which one firm commits statically and the other firm prices contingently; in this case, there also exists a unique mixed strategy equilibrium. Such equilibrium behavior tends to emerge, when capacity is ampler, and products are less differentiated or demand uncertainty is lower. With asymmetric fixed capacities, if demand uncertainty is low, a unique asymmetric equilibrium emerges, in which the firm with more capacity chooses committed pricing and the firm with less capacity chooses contingent pricing. We identify two countervailing profit effects of contingent pricing under competition: gains from responsively charging high price under high demand, and losses from intensified price competition under low demand. It is the latter detrimental effect that may prevent both firms from choosing a contingent pricing strategy in equilibrium. We show that the insights remain valid when capacity decisions are endogenized. We caution that responsive price changes under aggressive competition of less differentiated products can result in profit‐killing discounting.     

The Impact of Revenue‐Maximizing Priority Pricing on Customer Delay Costs

Speed is an increasingly important determinant of which suppliers will be given customers' business and is defined as the time between when an order is placed by the customer and when the product is delivered, or as the amount of time customers must wait before they receive their desired service. In either case, the speed a customer experiences can be enhanced by giving priority to that particular customer. Such a prioritization scheme will necessarily reduce the speed experienced by lower‐priority customers, but this can lead to a better outcome when different customers place different values on speed. We model a single resource (e.g., a manufacturer) that processes jobs from customers who have heterogeneous waiting costs. We analyze the price that maximizes priority revenue for the resource owner (i.e., supplier, manufacturer) under different assumptions regarding customer behavior. We discover that a revenue‐maximizing supplier facing self‐interested customers (i.e., those that independently minimize their own expected costs) charges a price that also minimizes the expected total delay costs across all customers and that this outcome does not result when customers coordinate to submit priority orders at a level that seeks to minimize their aggregate costs of priority fees and delays. Thus, the customers are better off collectively (as is the supplier) when the supplier and customers act independently in their own best interests. Finally, as the number of priority classes increases, both the priority revenues and the overall customer delay costs improve, but at a decreasing rate.