基于顾客不公平规避的服务定价研究

设计合理的服务机制和定价策略对于企业运营至关重要。由于顾客异质性（等待时间成本不同）企业通常对顾客进行分类服务,然而分类服务会引发顾客的不公平心理,并带来负效用,从而引起顾客流动与转移,进而影响企业收益与社会成本。本文针对垄断型服务系统中,顾客不公平规避心理（用参数α表示）对于企业优化目标的影响进行分析,在此基础之上,研究企业是否对顾客采取分类服务以及如何合理定价。结果表明,当顾客不公平规避偏好心理较弱时,从社会成本最小化和企业收益最大化的角度都应该对顾客进行分类服务并收取优先服务费用。当顾客不公规避心理较强时,从企业收益最大化的角度应仅保留优先权顾客并收取优先服务费用,从社会成本最小化的角度则应取消优先服务费用仅保留普通顾客。最后,通过数值模拟和理论分析对上述结论进行验证。     

基于异质性顾客心理期望等待时间的优先权服务定价研究

本文以非抢占式M/M/1排队系统为背景,以企业收益最大化为目标,基于顾客异质性(单位时间等待成本不同)将顾客分为两类,针对顾客的心理期望等待时间对服务提供商最优定价策略的影响进行研究。首先研究优先权顾客心理期望等待时间对企业收益的影响以及相应的优先权定价,然后研究优先权顾客和普通顾客同时存在心理期望等待时间对企业收益的影响和相应的优先权定价。研究表明:仅考虑优先权顾客的心理期望等待时间,企业应通过提高优先权定价来获得最优收益;当优先权顾客和普通顾客同时存在心理期望等待时间时,企业仍然采取提高优先权定价的策略,若普通顾客的价值大(获取服务的基本费用大),企业应对普通顾客提供一定的折扣来消除其心理期望等待时间增加企业收益;如果普通顾客的价值较小,企业应"有意"流失部分普通顾客,吸引更多顾客到优先权队列获取服务来获得更多收益。本文研究对于服务提供商在考虑顾客心理期望等待时间基础上设置最合理的队列机制有一定的指导意义和实际应用价值。     

基于顾客公平偏好的服务机制与定价研究

由于顾客异质性（单位时间等待成本不同），服务提供商通常对顾客采取分类服务策略，然而分类服务会引起服务系统中不同类型顾客之间等待时间和服务价值的差异性，从而给顾客带来心理上的不公平感，进而引起顾客在服务系统中的流动和转移，进一步影响企业收益和社会福利。本文针对非抢占M/M/1服务系统顾客分类情形为背景，由两种顾客之间期望等待时间的不同和公平偏好参数相结合构建普通顾客的公平心理效用模型，以垄断型服务系统为背景，分别从企业收益、社会福利与顾客效用三个视角进行分析。研究表明，服务提供商应对顾客采取可观测型的分类服务机制来获得最大收益；从社会福利视角，服务提供商应对顾客采取不可观测型的分类服务机制；从顾客效用视角，服务提供商应取消顾客分类服务，仅保留普通顾客。最后同现有结论进行比较分析，并进行拓展研究。本文研究对服务提供商采取合理的服务机制及相应的服务定价具有重要参考价值和指导意义。     

基于顾客心理期望等待时间的服务策略研究

顾客在排队系统获取服务时，会存在心理上的期望等待时间，该期望会影响顾客在排队系统中的行为变化和流动，从而影响企业收益。本文以传统的M/M/1排队系统为背景，基于顾客存在期望等待时间的前提下，以企业收益最大化为优化目标进行研究。首先，对相应基础理论和模型假设进行介绍；其次，对顾客存在心理期望等待时间情形提出三种新的策略：重新定价、通过折扣对顾客期望值进行调整、提高服务率；然后，分别对上述三种策略进行优化分析，并同现有结果进行比较；研究表明：三种策略都比维持原有定价带来更大收益；当折扣力度较小时或顾客对费用感知强于时间感知时，折扣策略优于重新定价策略；当折扣力度较大或顾客对时间感知强于费用感知时，重新定价策略优于折扣策略；最后，通过对最优结果分析提出相应管理启示。本文的研究对于顾客存在心理期望等待时间的服务定价具有重要的指导意义和实际应用价值。     

A comparison of fixed and dynamic pricing policies in revenue management

We consider the problem of selling a fixed capacity or inventory of items over a finite selling period. Earlier research has shown that using a properly set fixed price during the selling period is asymptotically optimal as the demand potential and capacity grow large and that dynamic pricing has only a secondary effect on revenues. However, additional revenue improvements through dynamic pricing can be important in practice and need to be further explored. We suggest two simple dynamic heuristics that continuously update prices based on remaining inventory and time in the selling period. The first heuristic is based on approximating the optimal expected revenue function and the second heuristic is based on the solution of the deterministic version of the problem. We show through a numerical study that the revenue impact of using these dynamic pricing heuristics rather than fixed pricing may be substantial. In particular, the first heuristic has a consistent and remarkable performance leading to at most 0.2% gap compared to optimal dynamic pricing. We also show that the benefits of these dynamic pricing heuristics persist under a periodic setting. This is especially true for the first heuristic for which the performance is monotone in the frequency of price changes. We conclude that dynamic pricing should be considered as a more favorable option in practice.     

Service Systems with Experience‐Based Anecdotal Reasoning Customers

The existing queueing literature typically assumes that customers either perfectly know the expected waiting time or are able to form rational expectations about it. In contrast, in this article, we study canonical service models where customers do not have such full information or capability. We assume that customers lack full capability or ample opportunities to perfectly infer the service rate or estimate the expected waiting time, and thus can only rely on past experiences and anecdotal reasoning to make their joining decisions. We fully characterize the steady‐state equilibrium in this service system. Compared with the fully rational benchmark, we find that customers with anecdotal reasoning are less price‐sensitive. Consequently, with a higher market potential (higher arrival rate), a revenue‐maximizing firm may increase the price if the service rate is exogenous, and it may decrease the price if the service rate is at the firm's discretion. Both results go against the commonly accepted pricing recommendations in the fully rational benchmark. We also show that revenue maximization and welfare maximization lead to fundamentally different pricing strategies with anecdotal reasoning, whereas they are equivalent in the fully rational benchmark.     

基于顾客消费信息的网络定价分析与仿真

本文通过仿真的方法研究了两种不同的定价策略对消费者网络消费需求的影响。在网络供应市场上,我们建立一个价格竞争的伯川德双垄断模型:一个网络提供商对其提供的网络服务采用固定定价,另一网络提供商采用两部分定价策略——固定链接费加使用费。基于中国社会调查事务(SSIC)的调查数据,我们采用随机数生成的方法来产生每一位消费者的消费额与消费时间,通过建立消费者效用模型来仿真在两种不同定价策略下消费者的消费选择,比较了不同价格策略对网络提供商收入、消费者行为及社会总福利的影响。     

Pricing cloud services—the impact of broadband quality

In the last few years, adoption of cloud computing has shown a marked increase across the world. Moreover, the smaller markets, viz., Asia-Pacific, Latin America, Middle-East, etc., are expected to grow at more than the average rate for the next few years. While this is good news for cloud service providers, significant obstacles to cloud adoption still remain a major cause of concern, for example, the quality of broadband services. As the quality of broadband services is not uniform across the different geographies, pricing of cloud services must take this non-uniformity into account. This paper provides managerial guidelines for cloud service providers on pricing their offerings. We develop optimal pricing strategies for a typical cloud service provider by modeling the utility of a customer of cloud services as a function of two vectors. The first vector is a set of parameters which contribute positively to the utility of a customer, and the second vector is a set of parameters which have a negative effect on the utility. We explore two pricing plans: usage based and fixed fee plan; determine the conditions under which customers would select one plan over another, and discuss the significance of these conditions for cloud service providers.     

Up Then Down: Bid‐Price Trends in Revenue Management

In the classic revenue management (RM) problem of selling a fixed quantity of perishable inventories to price‐sensitive non‐strategic consumers over a finite horizon, the optimal pricing decision at any time depends on two important factors: consumer valuation and bid price. The former is determined exogenously by the demand side, while the latter is determined jointly by the inventory level on the supply side and the consumer valuations in the time remaining within the selling horizon. Because of the importance of bid prices in theory and practice of RM, this study aims to enhance the understanding of the intertemporal behavior of bid prices in dynamic RM environments. We provide a probabilistic characterization of the optimal policies from the perspective of bid‐price processes. We show that an optimal bid‐price process has an upward trend over time before the inventory level falls to one and then has a downward trend. This intertemporal up‐then‐down pattern of bid‐price processes is related to two fundamental static properties of the optimal bid prices: (i) At any given time, a lower inventory level yields a higher optimal bid price, which is referred to as the resource scarcity effect; (ii) Given any inventory level, the optimal bid price decreases with time; that is referred to as the resource perishability effect. The demonstrated upward trend implies that the optimal bid‐price process is mainly driven by the resource scarcity effect, while the downward trend implies that the bid‐price process is mainly driven by the resource perishability effect. We also demonstrate how optimal bid price and consumer valuation, as two competing forces, interact over time to drive the optimal‐price process. The results are also extended to the network RM problems.     

Managing the Revenue of Sales Platforms: Insights into Service Fees

Online sales platforms have grown substantially in recent years. These platforms assist sellers to conduct sales, and in return, collect service fees from sellers. We study the fee policies by considering a fee‐setting platform, on which a seller may conduct a sale with a reserve price to a group of potential buyers: the seller retains the object for sale if the final trading price is below the reserve price. The platform may charge two types of fees as in current practice: a reserve fee as a function of the seller's reserve price and a final value fee as a function of the sale's final trading price. We derive the optimality condition for fee policies, and show that the platform can use either just a final value fee or just a reserve fee to achieve optimality. In the former case, the optimal final value fee charged by the platform is independent of the number of buyers. In the latter case, the optimal reserve fee is often a decreasing, instead of increasing, function of the seller's reserve price. An increasing reserve fee may make the seller reluctant to use a positive reserve price and hurt the platform's revenue. In general, the optimal fees are nonlinear functions, but in reality, linear fees are commonly used because of their simplicity for implementation. We show that a linear fee policy is indeed optimal in the case that the seller's valuation follows a power distribution. In other cases, our numerical analysis suggests close‐to‐optimal performance of the linear policy.     

Quality‐Speed Competition in Customer‐Intensive Services with Boundedly Rational Customers

We consider a system in which two competing servers provide customer‐intensive services and the service reward is affected by the length of service time. The customers are boundedly rational and choose their service providers according to a logit model. We demonstrate that the service provider revenue function is unimodal in the service rate, its decision variable, and show that the service rate competition has a unique and stable equilibrium. We then study the price decision under three scenarios with the price determined by a revenue‐maximizing firm, a welfare‐maximizing social planner, or two servers in competition. We find that the socially optimal price, subject to the requirement that the customer actual utility must be non‐negative, is always lower than the competition equilibrium price which, in turn, is lower than the revenue‐maximizing monopoly price. However, if the customer actual utility is allowed to be negative in social optimization, the socially optimal price can be higher than the other two prices in a large market.     

Pricing,Allocation, and Overbooking in Dynamic Service Network Competition When Demand Is Uncertain

We study the problem of combined pricing, resource allocation, and overbooking by service providers involved in dynamic noncooperative oligopolistic competition on a network that represents the relationships of the providers to one another and to their customers when service demand is uncertain. We propose, analyze, and compute solutions for a model that is more general than other models reported in the revenue management literature to date. In particular, previous models typically consider only three or four of five key revenue management features that we have purposely built into our model: (1) pricing, (2) resource allocation, (3) dynamic competition, (4) an explicit network, and (5) uncertain demand. Illustrative realizations of the abstract problem we study are those of airline revenue management and service provision by companies facing resource constraints. Under fairly general regularity conditions, we prove existence and uniqueness of a pure strategy Nash equilibrium for dynamic oligopolistic service network competition described by our model. We also show, for an appropriate notion of regularity, that competition leads to the underpricing of network services, a finding numerically illustrated by an example of intermediate size. Our proposed algorithm can be implemented using well‐known off‐the‐shelf commercial software.     

Optimal Uniform Pricing Strategy of a Service Firm When Facing Two Classes of Customers

When facing heterogeneous customers, how should a service firm make its pricing decision to maximize revenue? If discrimination is allowed, then priority schemes and differentiated pricing are often used to achieve that. In many applications, however, the firm cannot or is not allowed to set discriminatory prices, for example, list price in retail stores, online shopping, and gas stations; thus a uniform price must be applied to all customers. This study addresses the optimal uniform pricing problem of a service firm using a queueing system with two classes of customers. Our result shows that the potential pool of customers plays a central role in the firm's optimal decision. Depending on the range of system parameters, which are determined explicitly by the primitive data, the firm's optimal strategy may choose to serve only one class of customers, a subset of a class of customers, or a combination of different classes of customers. In addition, the optimal price is in general not monotonic with respect to the potential market sizes because their changes may lead to a major shift in the firm's decision on which customer class to serve. However, unless such a shift occurs, the optimal price is weakly decreasing in the potential market sizes.     

基于收益分成的多受让方两部技术转移定价策略研究

首先,从一般需求函数出发,得出两部技术转移定价法的一般解析表达式。然后,以线性需求函数为例,通过数值计算,对比分析了收益分成比例、最终产品价格、研发企业利润和受让方企业利润随着转让方和受让方股权比例的不同而变化的趋势。最后,分析得出,通过采用前期固定费用加后期收益分成的两部转移定价方法,不仅可以使研发企业的利润最优,同时还可以缓解技术受让方在单一定价方法下的一次性支出所带来的巨大资金压力和较高风险水平,并且将研发企业的利润与受让方的未来收益紧密联系,实现了双方的收益共享和风险共担。     

Optimal Pricing Strategies for Capacity Leasing Based on Time and Volume Usage in Telecommunication Networks

In this study, we examined optimal pricing strategies for “pay‐per‐time,” “pay‐per‐volume,” and “pay‐per‐both‐time‐and‐volume” based leasing of data networks in a monopoly environment. Conventionally, network capacity distribution includes short‐/long‐term bandwidth and/or usage time leasing. When customers choose connection‐time–based pricing, their rational behavior is to fully utilize the bandwidth capacity within a fixed time period, which may cause the network to burst (or overload). Conversely, when customers choose volume‐based strategies their rational behavior is to send only the minimum bytes necessary (even for time‐fixed tasks for real time applications), causing the quality of the task to decrease, which in turn creates an opportunity cost for the provider. Choosing a pay‐per time and volume hybridized pricing scheme allows customers to take advantage of both pricing strategies while lessening the disadvantages of each, because consumers generally have both time‐ and size‐fixed tasks such as batch data transactions. One of the key contributions of this study is to show that pay‐per both time and volume pricing is a viable and often preferable alternative to the offerings based on only time or volume, and that judicious use of such a pricing policy is profitable to the network provider.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

Static and Dynamic Pricing of Excess Capacity in a Make‐to‐Order Environment

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.     

Revenue Management of Reusable Resources with Advanced Reservations

We consider a revenue management problem wherein the seller is endowed with a single type resource with a finite capacity and the resource can be repeatedly used to serve customers. There are multiple classes of customers arriving according to a multi‐class Poisson process. Each customer, upon arrival, submits a service request that specifies his service start time and end time. Our model allows customer advanced reservation times and services times in each class to be arbitrarily distributed and correlated. Upon arrival of each customer, the seller must instantaneously decide whether to accept this customer's service request. A customer whose request is denied leaves the system. A customer whose request is accepted is allocated with a specific item of the resource at his service start time. The resource unit occupied by a customer becomes available to other customers after serving this customer. The seller aims to design an admission control policy that maximizes her expected long‐run average revenue. We propose a policy called the ε‐perturbation class selection policy (ε‐CSP), based on the optimal solution in the fluid setting wherein customers are infinitesimal and customer arrival processes are deterministic, under the restriction that the seller can utilize at most (1 − ε) of her capacity for any ε ∈ (0, 1). We prove that the ε‐CSP is near‐optimal. More precisely, we develop an upper bound of the performance loss of the ε‐CSP relative to the seller's optimal revenue, and show that it converges to zero with a square‐root convergence rate in the asymptotic regime wherein the arrival rates and the capacity grow up proportionally and the capacity buffer level ε decays to zero.     

Strategic Management of Distressed Inventory

It is well known that maximizing revenue from a fixed stock of perishable goods may require discounting prices rather than allowing unsold inventory to perish. This behavior is seen in industries ranging from fashion retail to tour packages and baked goods. A number of authors have addressed the markdown management problem in which a seller seeks to determine the optimal sequence of discounts to maximize the revenue from a fixed stock of perishable goods. However, merchants who consistently use markdown policies risk training customers to “wait for the sale.” We investigate models in which the decision to sell inventory at a discount will change the future expectations of customers and hence their buying behavior. We show that, in equilibrium, a single‐price policy is optimal if all consumers are strategic and demand is known to the seller. Relaxing any of these conditions can lead to a situation in which a two‐price markdown policy is optimal. We show using numerical simulation that if customers update their expectations of availability over time, then optimal sales limit policies can evolve in a complex fashion.