Optimal Pricing Research Articles

Loyalty Currency and Mental Accounting: Do Consumers Treat Points Like Money?

Problem definition: Loyalty programs have greatly expanded in scale and scope, and loyalty points issued by firms serve as a new form of currency alongside the traditional currency of money. In this paper, we study how consumers decide to pay with points or money for a purchase and how these decisions are affected by consumers’ points earning characteristics. Methodology/results: We develop a model of consumers’ payment choices and estimate it on proprietary loyalty program data from a major U.S. airline company using a hierarchical Bayesian framework. Our results demonstrate that mental accounting, the subjective perceived value of points, and the reference exchange rate play important roles in consumers’ payment choices. Moreover, the primary points earning source and the total earning level are jointly associated with consumers’ attitudes toward points and money: Consumers who earn many points and mostly with the focal firm tend to value points more than money, while those who earn few points or mostly through a cobranded credit card tend to value money more than points. To better understand heterogeneity in consumers’ attitudes toward points, we propose a probabilistic segmentation of consumers and identify four behavioral segments with distinctive characteristics. Through counterfactual analysis, we demonstrate how a firm can implement money and point pricing policies to optimally target and influence consumers’ payment choices. Managerial implications: It is important for firms to understand how consumers think about points and decide to pay with points or money as it affects firms’ cash flows, profitability, and consumer loyalty and engagement. We demonstrate that firms can design optimal price targeting policies by utilizing the significant level of heterogeneity in consumers’ attitudes toward points. Furthermore, our results indicate that firms that are expanding their partnership networks and offering more points earning sources should consider the impact that this would have on consumers’ mental accounting of money and points, and consequently their payment choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0287 .

Applying K Means Clustering Techniques on Retail Shop

Implement a customer segmentation system using k-mean clustering for a Retail business. Collect Data and pre-processing to clear missing data and inconsistence data and then visualization to analysis(EDA), apply K-Means to divide customer into distinct groups based on purchasing behaviour. Analyse and create customer profiles for each segment to optimize marketing strategies and enhance customer Engagement. Main goal of in this project on price optimization, Increase revenue, brand awareness and increase relationship between customer and enhance marketing Strategies and increasing sales.

A cost-effective model for emergency supplies prepositioning and procurement pricing via option contracts

Sudden-onset disasters frequently trigger an unexpected surge in demand for essential emergency supplies. Although current approaches prioritize cost-effectiveness and on-time delivery, they often neglect the suffering of disaster victims caused by insufficient emergency supplies. Our research aims to bridge this gap by mitigating the suffering of affected individuals while avoiding excessive costs. Initially, we integrate an option contract (OC) into a single-authority and −supplier framework agreement (FA). The cumulative suffering experienced by the affected population during deprivation periods is quantified under the FA. The supplier aims to maximize profit, while the government seeks to maximize social efficiency, defined as the ratio of the averted suffering to logistics costs. Subsequently, an option-based model for prepositioning and pricing emergency supplies is proposed. The results indicate that more suffering is averted by an OC in comparison to a wholesale contract (WC). Moreover, the option-based model is more cost-effective when the price of emergency purchases falls within a Goldilocks range, thus providing an optimal strike price for executing emergency options that maximizes social efficiency. Finally, we extend the model and observe that varying demand distributions indeed influence humanitarian decision-makers’ optimal decision strategies and seemingly have little effect on the feasibility of incorporating OCs into the prepositioning and procurement of emergency supplies, and that the dual mode of physical and production capacity reserves reduces to the single physical reserve mode when the urgent production cost exceeds a certain threshold. Additionally, the duration required deliver emergency supplies to the beneficiaries is prolonged with the increase in urgent conversion time, which leads to a reduction in social efficiency. Our research proposes a people-oriented, option-based mechanism for prepositioning and procuring emergency supplies to improve social efficiency, with a focus on the alleviation of the affected individuals’ suffering. Various measures are presented for emergency practitioners concerning the prepositioning and pricing of emergency supplies, thereby fostering an enduring government-enterprise cooperative relationship.

A flexible combinatorial auction bidding language for supplier selection and order allocation in a supply chain with price sensitive demand

This study addresses the multi-item multi-sourcing supplier selection and order allocation problem in a Combinatorial Auction (CA) framework. First, we propose a new flexible procurement CA bidding language, then prove that it can convey the same information as existing bidding languages while allowing more efficient auction outcomes and reduced computational complexity. Suppliers submit combinatorial bids to a single manufacturer. Each bid carries information about the supplier’s cost structure and potential discounts. The manufacturer purchases the items and assembles one or more product types that experience price-sensitive demand rates by end consumers. We use the realistic logit function to represent the price-sensitive demand rates of finished products. A Mixed Integer Non-Linear Programming (MINLP) model is developed to help the manufacturer make optimal supplier selection, order allocation, and pricing decisions under the proposed bidding language. The MINLP model considers purchasing, transportation, ordering, administrative, holding, and manufacturing costs. These considerations make the results of this study realistic with the potential to be used in practical applications. Also, we derive a set of necessary conditions that must exist in at least one optimal solution to the problem. These conditions can be utilized to design efficient solution mechanisms for this difficult problem in the future. Finally, an extensive numerical analysis is performed to illustrate the bidding language and the key results.

Guaranteed-selling service of online used-car reselling platform: Feasibility and applicability compared with conventional resell and agency resell

Information asymmetry in the used-car market causes inconsistent product quality and price perceptions between buyers and sellers. Third-party reselling platforms with expertise can play a positive role in resolving this asymmetry. An emerging guaranteed selling (GS) service is a reselling service developed on online platforms, where the seller is offered a guaranteed selling duration and purchase price before the transaction begins. During this period, sellers can trade directly with buyers. After this period, the platform purchases the unsold used car at the agreed guaranteed purchase price. Although the GS service can reduce sellers’ trading uncertainty and guarantee revenue, it remains unclear whether this emerging service model is feasible. Hence, we verify the feasibility and optimal pricing policy of the GS model. It generates profits by extending the guaranteed selling duration within a threshold limited by the agency rate. Furthermore, we identify the optimal application scenario for the GS model. It is more profitable than the conventional and agency resell models, considering different consumer preferences. By extending the GS duration and reducing the agency rate within certain limits, the GS model can divert market shares of the conventional resell model and exclude the agency resell model from the platform’s model selection. The interaction between the GS duration and agency rate determines these limits. This study provides valuable insights and managerial guidelines for used-car reselling platforms.

Mobile telecommunication companies’ investment and pricing strategies for content service

Several mobile telecommunication companies (MTCs), such as AT&T, Verizon, and China Mobile, are investing in content services to drive continued growth as the telecom industry has become saturated. In this study, we investigate the competition between two MTCs, one of which (MTC 1) provides both data and content services, while the other (MTC 2) provides only data services. We propose a two-stage game-theoretic model to analyze the two MTCs' optimal pricing strategies for data service and content service as well as the investment strategy for the content service. Our analysis reveals that a higher value of value-added service offered by MTC 1 will incentivize it to raise its subscription fee for the content service and induce both MTCs to reduce the prices of their data services, which results in an increased demand for the content service and a decreased demand for MTC 2's data service. As MTC 1 invests further in the quality of the content service, it would benefit from increasing the subscription fee for the content service, and the two MTCs should increase the subscription fee for the data service. However, compared to the decisions in the scenario where MTC 1 does not provide content service, the provision of this service by MTC 1 leads to a lower subscription fee for the two MTCs' data services, an increase in the demand for MTC 1's data service, and a decrease in the demand for MTC 2's data service. These findings reveal that regularly monitoring market competition, as well as adjusting pricing strategies based on market scenarios and the value of services, can enhance competitiveness and long-term profitability for MTCs.

Pricing heterogeneous products to heterogeneous customers who buy sequentially

This paper considers optimal pricing in a system with limited substitutable resources, such as certain goods or services. Prices for the different resources have to be set and then customers with heterogeneous preferences show up sequentially. Customers, of n types, select an item from the m available resources, depending on their valuations of the resources and the prices. The goal is to analyze this optimization problem, characterize a set of candidates to optimal solutions and provide methods for solving it. We prove that this problem is NP-hard to approximate within a factor O(n1-ε)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^{1-\\varepsilon })$$\\end{document} for any fixed ε>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon >0$$\\end{document}. Another important contribution is to prove that, the space of prices (which in principle is a continuous domain in Rm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {R}}^m$$\\end{document}), can be reduced to a finite set of vectors of cardinality mm-2nm2m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m^{m-2}n^m2^m$$\\end{document}. For a deterministic version of the problem, where the customer types are known to the firm, we provide a mathematical program that chooses the best set of prices. We report extensive computational results showing the usefulness of our exact approach to solve medium size problems with up to 200 customers and different assortments of products and customer types. We then show how to approximate the stochastic model by a small number of solutions of deterministic scenarios solved using a mixed-integer linear program.

Evolution of Tax Exemption Policy and Pricing Strategy Selection in a Competitive Market

The evolution of tax exemption policies and consumer preferences for low-carbon products affect firms’ optimal pricing strategy selection in a competitive duopoly market. In our study, we build a two-period pricing model to examine the pricing strategy choices between low-carbon and traditional firms. Low-carbon firms offer consumers greater value, improving their overall experience and satisfaction. Given the evolution of government policies from tax exemption to taxation for low-carbon products, we divide the changes in carbon tax into two periods. Since each firm can choose either the uniform pricing strategy (setting the same price in both periods) or the tiered pricing strategy (setting different prices for two periods), four scenarios may occur. Conventional wisdom suggests that a firm’s pricing increases should result in a reduction in consumer demand. Interestingly, our results show that as traditional firm raises prices, consumer demand for traditional products could increase simultaneously in the second period. In such a case, the low-carbon firm selects the uniform pricing strategy and the traditional firm chooses the tiered pricing strategy. Moreover, as tax exemption policies evolve in duopoly markets, the cancellation of the tax exemption policy may intensify competition between traditional and low-carbon firms under certain conditions. Furthermore, given one firm’s pricing strategy, our results show that the other firm could adopt either a uniform pricing strategy or a tiered pricing strategy, which depends on the low-carbon advantage and tax rate.

Optimal strategies for supply chain with credit guarantee using CVaR

In this paper, we explore optimal strategies of a supply chain consisting of one manufacturer, one capital-constrained retailer and one bank, where the bank provides loans to the retailer due to credit guarantees. However, there are default risks if the retailer can not repay the loans. Using conditional value-at-risk (CVaR) to describe risk aversion of the retailer, optimal order quantities of the retailer and optimal wholesale prices of the manufacturer are obtained by solving a Stackelberg game model, where the manufacturer is a leader and the retailer is a follower, respectively. Our numerical results show that the default probability of the retailer are proportional to optimal wholesale prices of the manufacturer. It implies that when the default probability of the retailer is high, the manufacturer should reduce the default risk by setting higher wholesale prices to avoid a burden of a substantial guarantee. Thus, our results can serve as insights for decision-makers of supply chain management.

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO-based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO-based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

OPTIMASI BIAYA DISTRIBUSI MATERIAL BATAKO DENGAN METODE NORTH WEST CORNER DAN STEPPING STONE PADA PERUMAHAN DI KOTA AMBON

In construction projects, distribution costs for material procurement support the design process of a projects and are also very influential and determine project planning. It is also necessary to utilize costs as optimally as possible to anticipate losses to the company due to the problem of distributing material from the supply to the projects location. In a project, materials needed in large quantities cannot always be met by just one material source. Because of this, several material sources are needed to meet the needed to meet the needs of a project, for this reason this research was carried out using two transportaastion methods, North West Corner fot initial solution and Stepping Stone for the optimum solution. With the aim of calculating material distribution costs to find optimal prices from galunggung, suli banda, and passo to housing demand locations in Ambon City, namely kusu-kusu, suli and upper halong. From the results of the above topic, it can be concluded that optimizing the distribution costs of brick material to housing in Ambon City using th initial North West Corner method obtained a distribution cost of Rp. 349,200.000 and using and advanced method, namely Stepping Stone which has been optimized, resulting in a cost of Rp. 274,655,000 this results in savings of Rp. 74,545.000.Keywords:Cost Optimasion, Housing, Material Distribution, North Westh Corner, Stepping Stone,

Quality and Pricing Decisions in a Dual-Channel Closed-Loop Supply Chain Considering Imperfect Product Recycling

Level of quality not only affects demand but also affects the proportion of imperfect products and then affects profit. This paper takes the quality level as a decision variable, considers both the consumer demand and the recovery of imperfect products in a dual-channel closed-loop supply chain (CLSC), and studies the significance of a revenue-sharing contract for coordination. The results show that the relationship between the optimal price and the quality level is affected by the repair cost of imperfect products and that the consumer’s attention to price and quality affects the trend of the overall profit with the quality level. In some cases, simply improving the quality level cannot expand the demand and is not even conducive to the improvement of green supply chain profits. It is also found that in both centralized and decentralized scenarios, coordination contracts can effectively motivate retailers to collect waste and help improve the economic efficiency of green supply chains.

Enhancing affordability and profit in a non-cooperative, coordinated, hypothetical pediatric vaccine market via sequential optimization.

This study considers a hypothetical global pediatric vaccine market where multiple coordinating entities make optimal procurement decisions on behalf of countries with different purchasing power. Each entity aims to improve affordability for its countries while maintaining a profitable market for vaccine producers. This study analyzes the effect of several factors on affordability and profitability, including the number of non-cooperative coordinating entities making procuring decisions, the number of market segments in which countries are grouped for tiered pricing purposes, how producers recover fixed production costs, and the procuring order of the coordinating entities. The study relies on a framework where entities negotiate sequentially with vaccine producers using a three-stage optimization process that solves a MIP and two LP problems to determine the optimal procurement plans and prices per dose that maximize savings for the entities' countries and profit for the vaccine producers. The study's results challenge current vaccine market dynamics and contribute novel alternative strategies to orchestrate the interaction of buyers, producers, and coordinating entities for enhancing affordability in a non-cooperative market. Key results show that the order in which the coordinating entities negotiate with vaccine producers and how the latter recuperate their fixed cost investments can significantly affect profitability and affordability. Furthermore, low-income countries can meet their demands more affordably by procuring vaccines through tiered pricing via entities coordinating many market segments. In contrast, upper-middle and high-income countries increase their affordability by procuring through entities with fewer and more extensive market segments. A procurement order that prioritizes entities based on the descending income level of their countries offers higher opportunities to increase affordability and profit when producers offer volume discounts.

Dynamic efficiency of tradable license system with time-flexible quantities

Tradable license system (TLS) is a fundamental policy instrument for environmental management or resource development. We construct a muti-periods dynamic model with respect to a general TLS with three time-flexible quantity mechanisms: fixed quantity, solely banking, and banking and borrowing, in which the firm maximizes its discounted net benefits over the horizon by selecting an optimal license usage by license trading across agents or transferring across periods. The dynamic efficiency performance and price dynamics in TLS are respectively examined. The decentralized equilibrium in TLS with fixed quantity cannot achieve benefit-maximum unless initial license allocation is efficient. The decentralized behaviors in TLS with solely banking lead to benefit-maximum and price dynamics follows the Hotelling rule, if and only if the cumulative initial license allocation in each period is not less than the optimum, while TLS with banking and borrowing can achieve the optimal outcome and price dynamics follows Hotelling rule regardless of the initial allocation. The findings highlight the synergistic effects between the initial allocation of licenses and time-flexible quantity mechanisms in TLS design.

Transient Analysis for a Queuing System with Impatient Customers and Its Applications to the Pricing Strategy of a Video Website

In this paper, we consider a queuing system with impatient customers, which includes infinite servers and two types of customers. During the service process, Type-1 customers may leave the system or upgrade to be Type-2 customers due to their impatience. By solving the partial differential equations, we obtain the generating functions of the transient distribution of the queue length, and many stationary performance measures are further derived. Then, as an application, we formulate an expected profit function for a video website, and maximize it by determining the optimal pricing strategy. Finally, numerical examples are provided to demonstrate the impacts of parameters on the optimal website profit.

Research on the Optimal Pricing Strategy of Supermarket Vegetables Based on Elastic Network Regression Model

Research on the Optimal Pricing Strategy of Supermarket Vegetables Based on Elastic Network Regression Model

Electric bike sharing: Price sensitivity and pricing preferences

In order to attract the highest possible number of users, municipal authorities and operators of bike sharing systems (BSS) often set very low prices for their services. This frequently results in non-profitability or dependency on public funding. Furthermore, low prices may discourage customers who are concerned regarding the quality of services. In our study, we investigate the impact of socio-demographic characteristics and travel habits on price sensitivity for an e-bike sharing system. We use Van Westendorp's price sensitivity meter (PSM) and the general linear model (GLM) to determine differences in optimal prices for various users and types of BSS. We find that optimal prices may be higher than the ones fixed by authorities and that discounts for female and younger users may result in higher demand for BSS. Moreover, car users are willing to pay less for a BSS than people who never use a car.

Token sales design under network effect

AbstractThis paper explores the use of initial coin offerings (ICOs) as a means of fundraising for companies through the issuance of blockchain‐based tokens. Specifically, we investigate how a company can implement ICOs and issue tokens supported by its products while taking into account the presence of network effects. We examine several aspects of ICOs, including the impacts of network effect on the company's optimal token sales design, such as the optimal token issuance price, the ICO cap (which refers to the number of tokens to be issued), and the amount of funds raised. We also consider the cases of deterministic and uncertain network effects, the impacts of limited speculators purchasing tokens during the ICO period, the comparison between ICOs and traditional bank financing, and the impact of ICO cost. The research unfolds in five key parts. Firstly, leveraging a baseline model, we derive equilibrium outcomes and the optimal token sales design, discerning the impact of deterministic and uncertain network effects. Notably, network effect uncertainty consistently benefits companies, ensuring profit resilience despite potential challenges, although in the face of such uncertainty, the ICO company may decrease the raised fund. Secondly, we investigate the consequences of a limited number of speculators in the ICO landscape, finding that adjustments on the token sales design are not required under low expected network effect. Thirdly, a comparative study between ICOs and bank financing reveals distinctive advantages of ICOs. ICOs prove capable of decreasing profit volatility while maintaining an equivalent profit compared to traditional bank financing. This insight offers valuable guidance for companies seeking optimal financing methods aligned with their risk preferences. Lastly, the impact of ICO cost on outcomes and token sales design is scrutinized. Contrary to expectations, ICO cost does not uniformly lead to an increase in the ICO cap, underscoring the nuanced relationship between costs and fundraising strategies.

Managing consumer bracketing purchases: Optimal return policy analysis

This paper investigates the optimal pricing and return policies of an online retailer selling a product with multiple versions to consumers facing valuation uncertainty. Consumers resolve their uncertainty through bracketing, a practice wherein they purchase multiple versions with the intention of keeping the best fit and returning the rest. By establishing a Stackelberg game between the retailer and consumers, we investigate two return policies: one offering full refunds with purchase limits, and the other offering partial refunds (Q or R) under exogenous and endogenous pricing (N or P), yielding four scenarios: NQ, PQ, NR, PR. Our study provides key insights into optimal return policies for retailers, demonstrating that stringent return policies may not necessarily be more profitable compared to lenient ones when dealing with consumer bracketing. In the case of exogenous pricing, retailers providing convenient returns are better off charging return fees, while those facing cumbersome returns should offer full refunds under certain conditions. When jointly leveraging pricing, retailers have the flexibility to choose between the full-refund and partial-refund policies. We obtain several interesting findings. First, as the mismatch probability increases, retailers tend to charge higher return fees in Scenario PR, while adjusting optimal decisions (i.e., refund under the partial-refund policy and purchase limit under the full-refund policy) non-monotonically in other scenarios. Second, consumers receive nonnegative expected utility in the exogenous pricing scenario, whereas zero in the endogenous pricing scenario. Third, when returns become sufficiently convenient, retailers adopting the partial-refund policy under endogenous pricing shift toward offering full refunds. In addition, when considering heterogeneous consumers, we discover that in some cases, retailers can benefit from consumer bracketing.