Optimal Pricing Strategy Research Articles

Determining Optimal Marketing based on Market Attraction and Market Based Strategies by Considering Customer Life Time Network Value

Customers are now constantly linked and communicate extensively with each other utilizing technology-enabled media, such as online social networks. Therefore, when making buying decisions, customers are often subjected to social influence. The strategic orientation to face the crisis due to the pandemic is not enough to rely solely on market capabilities and dynamics driven by incentives from the government. The advantages that are built on values that become market attractiveness need to be explored so that they become the basis for developing concepts that serve as frameworks for solving problems. The purpose of this study is to calculate customer lifetime value (CLV) in a space such as Marketplace (with the presence of buyers and sellers) considering network relations and the communication that takes place between them. These communications are in the form of interactions between buyers and buyers (verbal advertising), buyers and sellers (each number affects the other) and sellers with sellers (verbal advertising). In addition to calculating CLV in this study, optimal marketing strategies (for buyers) and pricing (for suppliers) are determined. In fact, this research primarily aims to determine the optimal marketing and pricing strategies in order to maximize the profit and CLV of the customers over time. For modeling, an optimal control model is suggested and finally a numerical example and sensitive analysis are provided to demonstrate the proposed model. The research objective is to analyze the attractiveness and market-based strategies to increase competitiveness in the aviation industry. Descriptive research verification survey of 32 airlines with the observation unit leaders and employees and customers. Data analysis used variant-based structural equations (Structural Equation Modeling - SEM). The results show that market attractiveness remains a value for customers to be managed by the company. The implementation of company strategy with customer focus, competitor intelligence, cross-functional intelligence, and performance implications encourages the excellence of the company

Pricing of crowdfunding products with strategic consumers and online reviews

Entrepreneurs can adopt a two-stage sale to sell the product once the crowdfunding campaign succeeds. With All-or-Nothing mechanism, we investigate the effect of online reviews on strategic consumer behavior and the choice of pricing strategy by considering two cases—one without and the other with online reviews. Analytical results show that online reviews play a significant role in determining the pricing strategy and maximizing profits. In the absence of online reviews, regardless of the funding goal, the penetration and uniform pricing strategies always benefits entrepreneurs. However, in the presence of online reviews, the funding goal influences the optimal pricing strategy. In particular, when the funding goal is low, the penetration pricing strategy is preferred; while the skimming pricing strategy may be adopted under certain conditions. By analyzing the model extension, we find that the presence of regret cost is beneficial to the entrepreneur’s profit under most circumstances.

Pricing iterative optimization for multi‐agent simulation of setting electric vehicle charging model in public parking lots

AbstractDespite the increasing scale of the electric vehicle market in recent years, in view of the long charging time of EVs (Electric Vehicles), the accessibility of charging facilities is still an obstacle to the rapid development of EVs. Therefore, the Chinese government has promulgated a one‐size‐fits‐all construction strategy which means constructing charging piles in parking lots with a fixed proportion. This paper mainly simulates the actual demand and adjusts the charging price of charging stations to reduce the uneven spatial distribution of charging demand. In particular, this paper constructs a multi‐agent system of the road network, vehicle, and charging station to simulate the charging behaviour in the mixed scenario of EVs and traditional fuel vehicles. The impact of charging pricing adjustment on individuals and charging stations is fed back in real‐time by using the characteristics of agents to respond to dynamic changes and make intelligent decisions. This paper proposes an iterative optimization method to obtain the optimal pricing strategy for charging stations to reduce the imbalance of charging demand. Taking the Wulin Square business district in Hangzhou as an example, the results show that the proposed pricing optimization method can improve the overall utility.

Optimal extended warranty pricing and retailing strategies in a closed-loop supply chain

Extended warranties are widely adopted and accepted in the marketplace by manufacturers and retailers as it helps to enhance the customers' post-sale satisfaction. In closed-loop supply chains, the extended warranty not only generates profit for the manufacturer, but also provides warranty returns of the new products for remanufacturing. In this paper, a two-period model is developed and optimal pricing strategies for the extended warranties are derived. We compare the optimal pricing and retailing strategies of the extended warranties for remanufactured and new products offered by the manufacturer with and without the retailer's own extended warranty while considering the competition between the manufacturer and the retailer for the extended warranty of new products. We find that the introduction of the retailer's extended warranty does not always hurt the manufacturer's profit. Numerical analyses also show that there exists an optimal extended warranty length for the manufacturer that maximises its profit. Moreover, we show that the retailer cannot extract more profit by increasing the length of its own extended warranty.

Bundling Strategies for Ride-Hailing Platforms Based on Price and Service Level

The increasing popularity of ride-hailing applications has given rise to a new channel in which ride-hailing platforms are bundled into aggregation platforms to earn additional orders by charging commissions and slotting fees. Such bundled channels, unlike traditional reseller electronic ones, may flutter prices, service levels, market demands, and then further affect their profits. These divergent attitudes raise an interesting and key question about whether and under what conditions bundled channels should be introduced to ride-hailing platforms. In this paper, we provide an analytical framework for ride-hailing and aggregation platforms in unbundled and bundled scenarios, respectively. We build a Stackelberg game model in which ride-hailing and aggregation platforms as leaders obtain prices by constructing Nash equilibria, while drivers as followers determine service levels given to two platforms. Drivers’ best responses in terms of service levels for two platforms, as well as platforms’ optimal pricing strategies and profits are achieved. To capture access conditions of the ride-hailing platform and the profit contention between two platforms, we further conduct sensitivity analysis on cost coefficients of service levels, price and cross-price substitutions, service level and cross-service level substitutions, revenue-sharing ratio, cost, as well as commission and slotting fee. Based on numerical examples and analysis of the results, some interesting managerial insights about bundling strategies are gained for ride-hailing platforms.

Solving Dual-Channel Supply Chain Pricing Strategy Problem with Multi-Level Programming Based on Improved Simplified Swarm Optimization

With the evolution of the Internet and the introduction of third-party platforms, a diversified supply chain has gradually emerged. In contrast to the traditional single sales channel, companies can also increase their revenue by selling through multiple channels, such as dual-channel sales: adding a sales channel for direct sales through online third-party platforms. However, due to the complexity of the supply chain structure, previous studies have rarely discussed and analyzed the capital-constrained dual-channel supply chain model, which is more relevant to the actual situation. To solve more complex and realistic supply chain decision problems, this paper uses the concept of game theory to describe the pricing negotiation procedures among the capital-constrained manufacturers and other parties in the dual-channel supply chain by applying the Stackelberg game theory to describe the supply chain structure as a hierarchical multi-level mathematical model to solve the optimal pricing strategy for different financing options to achieve the common benefit of the supply chain. In this study, we propose a Multi-level Improved Simplified Swarm Optimization (MLiSSO) method, which uses the improved, simplified swarm optimization (iSSO) for the Multi-level Programming Problem (MLPP). It is applied to this pricing strategy model of the supply chain and experiments with three related MLPPs in the past studies to verify the effectiveness of the method. The results show that the MLiSSO algorithm is effective, qualitative, and stable and can be used to solve the pricing strategy problem for supply chain models; furthermore, the algorithm can also be applied to other MLPPs.

Pricing strategy and collection rate for a supply chain considering environmental responsibility behaviors and rationality degree

Owing to the coexistence of environmental pollution and green behaviors, optimizing pricing strategy and collection rate is a growing concern. We develop game-theoretical models to investigate these issues in a supply chain considering environmental responsibility behaviors under two scenarios, perfect rationality and bounded rationality. Equal-pricing and differentiated-pricing strategies are given special attention. Under perfect rationality, we find that: (i) for a high unit environmental cost of uncollected used products, to optimize collection rate, the manufacturer makes a trade-off between total environmental cost reduction and the investment in collecting used products, rather than consumers’ demand; (ii) the retailer’s profitability for remanufactured products depends on green consumers partly or fully accepting remanufactured products, especially under equal-pricing; and (iii) when green consumers partly accept remanufactured products, for a medium green consumers’ acceptance degree and a low ordinary consumers’ acceptance degree, differentiated-pricing benefits the manufacturer but hurts the retailer. When there are enough green consumers, the retailer’s interests are aligned with the manufacturer and the supply chain under differentiated-pricing. Under bounded rationality, we find that: (i) compared with perfect rationality, the manufacturer and the retailer decrease the prices for new products and collection rate, and increase the price for remanufactured products, especially under differentiated-pricing; (ii) for a low bounded rationality level, the manufacturer and supply chain are better off by using differentiated-pricing; and (iii) consumers’ multi-dimensional heterogeneities and purchasing uncertainty play important roles in choosing an optimal pricing strategy.

Investment and Production Strategies of Renewable Energy Power under the Quota and Green Power Certificate System

In order to study the impact of a renewable energy quota and green power certificate system on the strategies of energy suppliers, this paper constructs a multi-stage game model of renewable energy power investment and production from the renewable energy interest chain and its stakeholders. Through the calculation and solution of the model, the optimal renewable energy utilization level, pricing and production strategies of renewable energy power of energy suppliers are calculated under the scenarios of direct sale of power and purchase and sale by power grids. The results show that the quota ratio, green certificate price and investment cost are the key influencing factors of energy suppliers’ strategies, and changes in the values of the three factors will completely change the renewable energy investment, pricing and production levels of energy suppliers in equilibrium. In addition, the study found that the impact of the renewable energy quota on renewable energy utilization levels of energy suppliers depends on the relative size of investment cost and green power certificate price. At the same time, it was also found that with a change in investment cost, green power certificate price and user preference, the market share and renewable energy utilization level of traditional energy suppliers and new energy suppliers also change.

Personalized Content Sharing via Mobile Crowdsensing

Personalized content sharing will inevitably become one of the core applications of mobile Internet-of-Things. However, existing strategies for content sharing are far from effective content personalization, since they either fail to protect the diversity of shared content or harm the enthusiasm of users to participate in cooperation. How to optimize the trade-off between content personalization and sharing efficiency thus becomes an extremely challenging problem. To circumvent this dilemma, we propose a social-aware personalized content sharing strategy based on mobile crowdsensing (MCS), which specially introduces positive network externalities derived from MCS and social network. Specifically, we design a two-stage pricing-participation game to model the interactions between mobile users and a profit-making service provider. By solving the subgame-perfect Nash equilibrium (NE) of the proposed game, an efficient participation mechanism and an optimal-pricing strategy are developed. At first, users’ decision selection of whether to join MCS is modeled as a social-aware mobile crowdsensing participation game (SA-MPG), and two algorithms for solving the Pareto-optimal NE of SA-MPG are designed. Subsequently, the pricing issue for network operators is investigated by exploiting the supermodularity of SA-MPG. Stochastic network model and real-world dataset based simulations corroborate the significant gain of our proposed strategy.

Pricing Strategy for Green Products Based on Disparities in Energy Consumption

The energy consumption disparity is an important factor affecting product pricing. The risk and the cost of new energy products are generally higher than those of traditional energy products, which results in conflicting pricing strategies between manufacturers and retailers. Applying the concepts from dynamic cooperative game theory, such as improved Shapley value, in this paper, we have studied the optimal pricing strategies of manufacturers and retailers in a cooperative and a noncooperative game. Based on policies and market conditions in China, we have used a logistic regression model to determine the impact of product sales, devised pricing strategies affected by the relevant government tax, and subsidy policy. The results show that, first, under the basic condition in which the manufacturer obtains profit, the optimal pricing of the retailer in a noncooperative game is more than that in a cooperative game. Second, the differential coefficient of the energy consumption of the two products in the cooperative game is greater than that in the noncooperative game. Third, the cooperative game not only plays an incentivizing role for retailers in setting reasonable prices of new energy products but also encourages manufacturers, producing a greater difference of energy consumption between traditional energy products and new energy products, which further reduces the consumption of new energy products. Finally, the strategies of sharing the green marketing costs by manufacturers and retailers can improve the total profits of the supply chain in addition to their own.

Performance of Channel Members under Emission-Sensitive Demand for Green Supply Chain Management: A Game Theory Approach

The skyrocketing growth of e-commerce and traditional retailing contributes to a large proportion of carbon emissions in any supply chain. Nevertheless, the literature related to carbon emission has focused on manufacturers and their potential for emission reduction. Therefore, it is imperative to understand the role of the retailing sector in reducing carbon emissions. Therefore, this study considers emission-sensitive demands which are faced by an r-store (brick and mortar retailer) and an e-store (online retailer) under different channel power structures. The competition between the channel members is modeled using game theory for the following channel structures, i.e., (i) r-store and e-store have commensurate channel power, (ii) r-store holds higher channel power, and (iii) e-store holds higher channel power. Equilibrium analysis was carried out to obtain the optimal pricing strategies and the r-store’s optimal profit and e-store. Further, the pricing strategies and resulting sales volumes were compared analytically and followed by a numerical validation. Three subcases were considered under numerical examples based on the parameter values with special reference to the base demand. It was found that competition between the r-store and the e-store having commensurate channel power will make them worse off. Therefore, the channel leadership is neither helping the r-store nor the e-store obtain more profit when the customer demand is emission sensitive.

An Improved Charging Navigation Strategy of Electric Vehicles via Optimal Time-of-Use Pricing

Electric vehicles (EVs) have attracted worldwide attention and have been vigorously promoted by the government. However, users’ mileage anxiety and the intermittent charging load are still great challenges to the popularization of EVs. To tackle these problems, this paper proposes an improved charging navigation model, aiming at maximizing the benefits of multiple parties by setting time-of-use (TOU) price of fast charging stations (FCSs) to attract EVs to charge at off-peak hours and saving the costs of EVs with real-time navigation. It is modeled as a Stackelberg game, in which the FCSs operator and EVs are the leader and followers respectively. First, a rigorous and dedicated traffic simulation model, air conditioner energy consumption model and queuing model of EVs are established. Then, by researching the impact of prices on charging choices, an EV strategy including the selection of charging time, charging energy, charging station and routes is proposed to minimize EV’s cost. Based on EV charging response behavior, the optimal TOU pricing strategy for FCSs is formulated to maximize FCSs’ revenue. The simulation results confirm that the proposed approach is beneficial to EVs and FCSs, and can effectively reduce the peak-to-average ratio and the peak-valley difference of the power system.

Consider strategic consumers and trade-in product dynamic pricing strategy research

This paper analyzes the choice behavior of strategic consumers and the impact of trade- in on product pricing path, compares the equilibrium deviation between different pricing decision- making methods, and discusses the optimal product pricing strategy under different situations. It is found that strategic consumers tend to buy low quality products with lower price if the degree of strategy is higher. The higher the level of quality investment, the stronger the willingness of strategic consumers to buy new products. Compared with centralized decision-making, decentralized decision-making is more conducive to the two enterprises to improve their own profit level. The relevant conclusions of the research can provide forward-looking basis for enterprises in the launch timing of new and old products, pricing decisions and trade-in strategies, which has certain theoretical significance and practical value.

Pricing strategy selection for the sharing platform with heterogeneous laborers

In recent years, labor-sharing platforms have attracted widespread attention, and platforms such as Eatwith and ZBJ (ZBJ.com) have developed rapidly. The platforms connect labor providers and buyers by charging a fee. Although most labor-sharing platforms are structured in the same way, they have no clear, dominant pricing strategy. Some platforms adopt a labor-pricing strategy (LP strategy), while others adopt a bargaining-pricing strategy (BP strategy). Buyers can decide whether to purchase services on the platform based on their valuations. Laborers can offer services and decide whether to sell on the platform. In this research, we consider a platform that contemplates labor-sharing and designs its optimal pricing strategy by accounting for the two-sided trade-off between time savings and clarity in service quality. We also consider whether laborers or buyers should pay to connect to the platform under the BP strategy. First, our research shows that the impact of the platform’s commission rate on the price under the BP strategy is nonmonotonic. In addition, we find that the market composition of labor and service quality can affect the labor-sharing platform’s pricing strategy. Specifically, the LP strategy does not always perform well, explaining why some labor-sharing platforms adopt the BP strategy in practice. Finally, we demonstrate that the labor-sharing platform should choose different profit methods to maximize profits under different circumstances when adopting the BP strategy in the extension model.

Research on Dual Channel Supply Chain Decision Making of New Retailing Enterprises Considering Service Behavior in the Era of Big Data

Drawing from extant retailing and supply chain research, this paper studies the dual channel supply chain decision-making of member channel, and obtains the optimal price strategy, maximum demand and maximum total revenue of the supply chain of network channel and retailing channel under the centralized decision-making and decentralized decision-making respectively. The contributions of this study identify that investing in big data within a certain threshold can improve the channel service level, reduce the channel price and improve the income of the supply chain. Supply chain members improve the channel service level and increase the corresponding channel price. The supply chain can get the most advantages when manufacturers and retailers make centralized decisions. This paper provides a starting point for new retailing academic and practical research in a domain that is deficient in empirical research, provides the theoretical framework to new retailing enterprises and decision-making model for their sustainable competitive advantage.

Multi-Agent Deep Reinforcement Learning Method for EV Charging Station Game

The ongoing quest for transportation electrification with the massive proliferation of EV charging stations (EVCSs) will deepen the interaction and require the further coordination of coupled power and transportation networks (PTN). The individually-owned EVCSs located in an urban transportation network (UTN) will compete using price signals to maximize their respective payoffs. In this paper, a multi-agent deep reinforcement learning (MA-DRL) method is proposed to model the pricing game in UTN and determine the optimal charging prices for a single EVCS. The EVCS charging demand is first analyzed using a modified user equilibrium traffic assignment problem (UE-TAP) with elastic traveling demands and different charging prices. The price competition problem is then formulated as a game with incomplete information in which the market environment is complex due to nonlinear traffic assignments. Thus, the MA-DRL approach is proposed to learn the charging pricing strategies of multiple EVCSs and approximate the Nash Equilibrium (NE) of the pricing game using the incomplete information. The proposed solution will determine the optimal pricing strategies for an EVCS in UTN. The case studies on a 24-node Sioux-Falls network, and the real-world Xi'an and Hangzhou cities are conducted to verify the effectiveness and scalability of the proposed approach.

The Emergence of a Sharing Market: Pricing, Supply, and Consumption

ABSTRACT The emerging sharing economy increases the utilization of existing products/services, reduces the consumption of new resources, and gets more popular in recent years. We consider a market where consumers are differentiated by their preference for the product and develop an analytical model to study how a person-to-person (P2P) sharing market (ideal or imperfect) affects the manufacturer’s pricing strategies, consumers’ consumption time, consumer surplus, and social welfare. Surprisingly, we find that the existence of the sharing market may drive up the selling price of the product if the sharing platform can endogenously determine the commission fee of sharing transactions. Accordingly, depending on the transaction costs and the proportion of high-type consumers, we find that consumers’ total consumption time, consumer surplus, manufacturer’s profit, and social welfare may not improve in the presence of a sharing market. This study also offers guidelines for the sharing platform and optimal pricing strategies for manufacturers.

Dynamic analysis of a dual-channel closed-loop supply chain with fairness concerns under carbon tax regulation.

In this work, we study a dual-channel closed-loop supply chain (CLSC) where the manufacturer sells the new products via one fair caring retailer in the traditional channel and distributes the remanufactured products through her own direct channel in the presence of the carbon tax regulation. After solving the single-period Stackelberg game model by backward induction and analyzing the impacts of key parameters on the optimal pricing strategies and the performance of channel members, a multi-period dynamic Stackelberg game model with heterogeneous players is further established. The local stability of the Nash equilibrium point and complexity properties of the model are investigated by numerical simulation. The results reveal that (1) the retailer's fairness concern degree is negatively related to the optimal wholesale price as well as positively related to the optimal retail price of the new product. A high level of consumer discount perception for the remanufactured product is conducive to the manufacturer obtaining more profits while it is detrimental to the retailer. (2) The excessive value of the price adjustment speed, carbon tax rate or retailer's fairness concern degree has a strong destabilization effect on the system's stability. (3) The manufacturer suffers profit loss while the retailer's utility levels are elevated when the system falls into periodic cycles and chaotic motions. (4) The delay feedback control method can eliminate the chaos effectively in the dual-channel CLSC system.

DYNAMIC PRICING STRATEGIES IN SALES MANAGEMENT OF DISCRETE PRODUCTS WITH A LIMITED LIFETIME

The paper is devoted to the development of a dynamic pricing strategy for the sale of goods with a limited shelf life. The case of sales of discrete goods to heterogeneous consumers who have at most a unit demand for sold goods and independent identically distributed estimates of its consumer value is considered. Such a structure of demand makes it possible to describe the optimal pricing strategy using a simple system of recursive equations, for which, in some cases, an analytical solution can be found. For the general case, a numerical algorithm has been developed for finding the optimal solution in the form of a feedback loop with time and the remaining stocks of unsold products as state variables. It is shown that optimal prices are non-increasing functions of both of these factors. These two properties, combined with the random nature of product sales, determine the rather complex dynamics of observed prices, examples of which are given in the paper. In particular, it is shown that although, on average, prices can be expected to decrease towards the end of the sales period, in some cases prices can increase and generally change quite chaotically. The proposed strategy is compared with the policy of fixed prices, the optimization of which under the conditions of the model is also a non-trivial task. The results of the comparison indicate the high economic efficiency of the dynamic price adjustment strategy, especially in cases when the product sold nears the end of its lifetime. It is shown that the general process of product sales can be represented as a controlled Markov process. This makes it possible to calculate any numerical characteristics of seller’s expected financial results depending on the model parameters. Based on the analysis of the results of numerical simulation, simple heuristics for sales management under conditions of incomplete information are also proposed.

Managing retrial queueing systems with boundedly rational customers

Bounded rationality includes potential cognitive deficits that limit human rational behaviour. In this paper, we allow for estimation errors of their utility and consider bounded rationality in queueing models with retrials. Upon arrival, customers decide whether to join the system based on their perceived utility, and their choice behaviour is characterised by a logit choice model. For a revenue-seeking server and a social planner, we investigate corresponding optimal pricing strategies. We find that the revenue-optimal price is no longer socially efficient in general but depends on the retrial rate. There exists a threshold such that the socially optimal price is greater than the revenue-optimal one when the retrial rate is below this threshold; otherwise, the revenue-optimal price is greater. A larger bounded rationality widens the gap between social welfare under the revenue- and socially optimal prices. Furthermore, the server’s revenue may increase or first decrease and then increase with respect to customers’ rationality levels, and the server may find it beneficial to reveal waiting time information when customers’ rationality is high. Finally, failing to account for customers’ bounded rationality can lead a significant revenue loss for the server and the proportion of such a revenue loss is close to 1 as customers become fully boundedly rational.