Optimal Pricing Strategy Research Articles

Technology options and optimal pricing of a two-sided mobile payment platform

This study investigates the pricing strategies of a mobile payment platform connecting merchants and customers who may bypass the platform and trade in cash. Our analytical model incorporates cross-side network externalities at both sides of the platform. We find that the optimal pricing strategy involves cross-subsidization, including subsidizing customers to register for the service while charging the merchants per transaction. Moreover, the technology option adopted by the platform influences this optimal pricing format. In particular, the platform should subsidize customers more if the payment technology imposes larger transaction costs on customers and charge merchants more if banks demand a higher payment processing fee. Our major findings remain qualitatively valid when per-transaction subsidy is available and the marginal benefit of cross-side network externalities diminishes. Finally, the presence of a platform improves social welfare if the per-transaction subsidy sufficiently stimulates consumption on the platform.

Blockchain adoption and strategic contracting in a green supply chain considering market segmentation

PurposeThe aim of this study is to examine the influence of consumer preferences for overseas green products and the implementation of blockchain technology on the performance of a supply chain, which comprises an overseas manufacturer and a domestic e-commerce platform. This research endeavors to identify the optimal pricing decisions and strategies for both the manufacturer and the platform in the context of the expanding e-commerce and globalization of the economy.Design/methodology/approachThe authors propose and analyze four distinct models based on the selection of selling contracts by the manufacturer and the adoption strategy of blockchain by the platform, using game theory to obtain the optimal solutions for these models.FindingsThe authors show that consumer migration promotes the manufacturer's green inputs, while the expansion of green consumer proportion is not conducive to it. They also show that blockchain technology has the potential to effectively limit manufacturer cannibalization. Interestingly, the study reveals a cascading effect of advantage where the manufacturer's profit variation trend changes only with the integration of pricing power advantage and blockchain technology inputs. This effect suggests that the equilibrium strategy is achievable under the agency contract with blockchain adoption, while Pareto improvement can be obtained with blockchain technology under both selling contracts.Research limitations/implicationsThis research could be extended in several possible directions. First, future work could explore outsourcing strategies for overseas manufacturers. Second, more types of consumer heterogeneity and different risk preferences could be considered. Third, this study can be extended by further exploring the design of mechanisms under asymmetric demand information to make the model more realistic.Originality/valueThe authors examine the impact of market segmentation and consumer preferences on green supply chain decisions, and analyze supply chain members' strategic choices for selling contracts and blockchain adoptions. The research also sheds light on the theoretical underpinnings and practical applications of green supply chain development and blockchain applications.

Optimal pricing strategies and decision‐making systems in e‐commerce using integrated fuzzy multi‐criteria method

AbstractE‐commerce online stores are now virtual platforms for connecting with millions of potential clients worldwide in the age of digitization. The marketing teams develop digital marketing tactics to attract traffic to their e‐commerce sites and increase sales volume. With the vast amount of data provided by the cloud, decisions that were previously made with a significant level of intuition based on the knowledge and experience of decision‐makers can now be backed using artificial intelligence algorithms. To identify the variables influencing pricing decisions for products launched on e‐commerce shopping sites and to develop variable pricing techniques for each product found on an e‐commerce site. This paper introduces a novel approach that applies Fuzzy association rule mining (FARM) and Fuzzy TOPSIS MCDM methodology. A B2B e‐commerce marketing store based in Hong Kong has created and implemented Smart‐Quo, a pricing decision support system for B2B e‐commerce retail businesses. After a six‐month trial period, there has been a substantial advance in the effectiveness and efficiency of choosing prices for each product. The case study illustrates the viability and potential advantages of implementing artificial intelligence tools in marketing management in the digital era.

Optimal employment model for an entrant platform in on-demand service market

PurposeFor an entrant platform in the on-demand service market, choosing an appropriate employment model is critical. This study explores how the entrant optimally chooses the employment model to achieve better performance and investigates the optimal pricing strategies and wage schemes for both incumbent and entrant platforms.Design/methodology/approachBased on the Hotelling model, the authors develop a game-theoretic framework to study the incumbent's and entrant's optimal service prices and wage schemes. Moreover, the authors determine the entrant's optimal employment model by comparing the entrant's optimal profits under different market configurations and analytically analyze the impacts of some critical factors on the platforms' decision-making.FindingsThis study reveals that the impacts of the unit misfit cost of suppliers or consumers on the pricing strategies and wage schemes vary with different operational efficiencies of platforms. Only when both the service efficiency of contractors and the basic employee benefits are low, entrants should adopt the employee model. Moreover, a lower unit misfit cost of suppliers or consumers makes entrants more likely to choose the contractor model. However, the service efficiency of contractors has nonmonotonic effects on the entrant's decision.Originality/valueThis study focuses on an entrant's decision on the optimal employment model in an on-demand service market, considering the competition between entrants and incumbents on both the supplier and consumer sides, which has not been investigated in the prior literature.

Developing Pricing Strategy in a Closed-Loop Supply Chain for Electric Vehicle Batteries With a Government Reward and Punishment Mechanism

Electric vehicles are expected to become a major means of transportation in the future, but entail the challenge of collecting used electric vehicle batteries. This article examines pricing strategies in a closed-loop dual-channel supply chain for electric vehicle batteries both with and without a governmental reward and punishment mechanism. The discussion considers the optimal pricing strategy considering the competition between dual collecting channels, both with the government's reward and punishment mechanism and without it. The degree of influence of government mechanisms, echelon utilization, and EV battery collection on a) collection price, b) collection capacity, and c) the profit obtained by participants are also examined. The discussion shows that collection capacity, collection price, and recycler profits in the closed-loop EV battery supply chain could be improved with a government reward and punishment mechanism. Finally, the robustness of the proposed model was verified through numerical examples.

Strategic live streaming choices for vertically differentiated products

Live streaming as a new shopping channel can disclose revealing information on quality preference. With this channel, viewers can obtain additional product details along with entertainment utility from streamers' product showcasing. This paper examines live streaming strategies for vertically differentiated firms that sell products of high or low quality. We obtain the optimal pricing strategies for the high-quality, low-quality firms under a particular live streaming strategy (i.e., (N,N); (S,N); (N,S); (S,S)). We identify how the competing firms should set their live streaming strategies. As the entertainment value increases, firms with a competitive edge are more willing to adopt live streaming. If the “quality-adjusted cost” is lower than 12, then the high-quality firm is prone to adopt live streaming; otherwise, the low-quality firm is prone to adopt live streaming. The entertainment value greatly influences the adoption decision. When the streamer is more popular and charges a medium fixed cost, the competitive firm (i.e., small marginal cost, high quality) should adopt live streaming to expand its marketplace. The other firm should refuse live streaming and emphasize improving its product quality and competitiveness. Interestingly, live streaming is not always beneficial to firms when the competitive advantages dominate the impact of live streaming and the informative degree is improved little.

DIGITALISATION OF PRICING METHODS TO DETERMINE THE OPTIMAL PRICE

The article discusses modern approaches to pricing in the context of digital business transformation. The author discusses the impact of artificial intelligence, dynamic pricing and personalisation on the development of optimal pricing strategies. The article reveals how machine learning algorithms allow businesses to effectively analyse large amounts of data and accurately predict market trends. Focusing on dynamic pricing, in the context of the digital environment, it is revealed as a strategy that allows companies to respond quickly to changing market conditions and effectively adapt prices to demand and competition in real time. A specific feature of the article is the analysis of the impact of personalisation on price offers. Taking into account the individual needs and buying habits of customers allows businesses to create unique pricing strategies that increase customer loyalty and satisfaction. Personalisation of price offers and experimentation with different strategies are also identified as key factors in optimising pricing. This article serves as an important reference for those seeking to understand how digital tools are shaping a new approach to effective pricing management in today's business environment. Overall, the digitalisation of marketing pricing methods not only ensures greater accuracy and efficiency in pricing, but also opens up new opportunities for businesses to adapt to changing market conditions and meet individual customer needs. Businesses can use digital platforms to hold auctions or listings for their goods or services. This not only creates competition among buyers, but also allows them to effectively determine the optimal price based on real demand. The digital transformation in marketing pricing opens up new opportunities for businesses to determine the optimal price of goods. The use of analytics, personalisation, dynamic pricing and other digital tools allows businesses to maximise profits by adapting to rapidly changing market conditions. The development of digital pricing strategies is setting a new standard for effective pricing management, allowing companies to remain competitive in today's digital business environment.

Is it wise to compete or to collaborate? Remanufacturing business models under collective extended producer responsibility legislation

Collective extended producer responsibility (CPR) legislation aims to accomplish manufacturers’ extended producer responsibility (EPR) through a common channel of product recovery activities. This work intends to study the impact of the CPR legislation system on competitive and collaborative business models of original equipment manufacturers (OEMs) and independent remanufacturers (IRs). Therein, we developed a two-period competitive and collaborative model considering an oligopoly market scenario for analysing optimal remanufacturing and pricing strategies of OEMs and IRs. Government subsidies promote the remanufacturing activities, and their effects are examined on optimal remanufacturing and pricing decisions for competitive and collaborative models under CPR legislation. The result highlights that competition with IRs under the CPR legislation system within oligopoly market scenarios is profitable for OEMs. Furthermore, the result indicates that a competitive business model increases consumer surplus, increasing used products collection and remanufacturing. The findings benefit OEMs of identical products from implementing a profitable business model under CPR legislation to restrict the availability of low-quality remanufactured products of IRs, thereby protecting the brand image and controlling the cannibalization effect on new products selling.

Consortium Blockchain-Based Spectrum Trading for Network Slicing in 5G RAN: A Multi-Agent Deep Reinforcement Learning Approach

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.

Research on nash game model for user side shared energy storage pricing

With the continuous promotion of the energy revolution, the market-oriented reform of electricity has become the first priority in the energy field, and small-scale energy storage devices on the user side have received more and more attention. However, the disorderly management mode of user-side energy storage not only causes a waste of resources, but also brings hidden dangers to the safe operation of the power grid, such as stability, scheduling and operation, power quality and other problems. To address this issue, this paper proposes a user-side shared energy storage pricing strategy based on Nash game. Firstly, an optimal operation model is established for each participant of energy storage operators, users and grid. Secondly, a cooperative game model is established based on Nash equilibrium theory for the participants under the constraints. Finally, the optimal pricing scheme is solved by simulation analysis of the model, and the feasibility of the proposed pricing mechanism is verified. The results show that the optimal pricing scheme can achieve the purpose of peak shaving and resource saving. At the same time, it can also realize the win–win situation for all parties.

Forming the system of generation of additional sales on the principles of marketing analytics

The aim of the article is consideration of various aspects and indicators of forming an additional sales generation system based on the principles of marketing analytics in the conditions of an aggressive macro-environment. Analyses results. The formation of the system for generating additional sales based on principles of marketing analytics is a necessary step for the enterprises under effect of complex conditions of an aggressive macro-environment impact. Under the analysis of paying demand, the competitive environment and the application of marketing analytics tools, enterprises could gain significant advantages, increase sales and strengthen their competitive position. The use of marketing analytics allows to identify new opportunities for expanding product or service assortment, attracting new target consumer groups, and developing innovative solutions. The utilization of analytical tools enables more accurate demand forecasts, determination of optimal pricing strategies, and the development of effective marketing communications. However, the successful implementation of the system for generating additional sales based on the principles of marketing analytics requires the availability of appropriate resources, qualified personnel and a technological base. An important stage is the collection and processing of a large volume of data, their analysis and interpretation. To accomplish the sales tasks, specialized software tools are needed to be utilized as well as highly skilled analysts should be involved. The article examines various indicators of a sales generation system based on marketing analytics principles such as Potenicial Sales, Repeat Purchase Rate, Customer Retention Rate, Сhurn and Lost Rate, Purchase Frequency , Net Promoter Score, Net Dollar Retention, average check, Price premium Market Share and Value-to-Volume Ratio. Customer Relationship Management & Enterprise Resourse Planning systems. The metioning of ussin CRM and ERP systems is also important. The analysis of solvent demand, the competitive environment and the use of marketing analytics tools are key elements in developing an effective strategy for increasing sales and company revenues. Examining the various factors that influence the decline in demand and markets allows to identify opportunities of value creation for customers and attract them in order to gain additional purchases. The application of marketing analytics in the formation of an additional sales strategy helps companies understand consumer behaviour, predict changes in the market and develop personalized offers to increase customer spending. Therefore, consideration of different aspects of the formation of the system for generating additional sales based on marketing analytics creates the key importance for increasing competitiveness and achieving success in the market. Conclusions and directions for further research. As a result of the study, it was found that the formation of an effective system for generating additional sales in an aggressive macro environment requires a deep analysis of solvent demand, competitive position and the use of marketing analytics principles. The developed methodical aspects of the generation of the system of additional sales based on the indicators of marketing analytics can serve as a basis for making informed decisions and developing effective sales strategies. The results of the research can be used to improve strategic planning, decision-making on sales promotion and increase the competitiveness of enterprises. The application of the principles of marketing analytics allows collecting and analysing large volumes of data about consumers, their behavioural and psychological characteristics, as well as competitive factors. This makes it possible to understand changes in consumer preferences and needs, predict their reactions to market changes and develop personalized sales strategies, as well as identify new opportunities for expanding the range of products or services, attracting new target groups of consumers and developing innovative solutions. The use of analytical tools makes it possible to more accurately determine the volume of demand, substantiate price strategies taking into account the objectives of increasing profits, and develop and implement modern tools of marketing communications in the sales system

Optimal pricing strategy in an unreliable M/G/1 retrial queue with Bernoulli preventive maintenance

This paper studies an unreliable M/G/1 retrial queue with Bernoulli preventive maintenance, in which the server may break down while providing service to customers. Before the start of each service, the server is subjected to preventive maintenance with a certain probability, which extends the lifetime of the server. If arriving customers find the server unavailable, they decide to balk or enter the orbit based on the linear reward-cost structure. First, the stationary distribution of system states and some important performance measures are derived. Then, we investigate the equilibrium joining strategies of customers in both cooperative and non-cooperative cases, respectively. Next, the optimal pricing strategy is considered from the perspective of the social planner. Finally, numerical examples are provided to illustrate the influence of system parameters on customer equilibrium joining probabilities and the optimal pricing strategy.

Pricing strategies for new product and remanufactured product considering emission sensitive demand.

With the rapidly increasing concern on environmental pollution and resource shortage, remanufactured products attract many attentions. In order to determine the optimal production and pricing strategy, we construct decision models for both single-product market and mixed-product market. Consumers' different preferences for new products and remanufactured products are considered. First, we construct pricing models for a single-product market, and achieve a judging condition to determine the optimal strategy. Second, we develop a pricing model for a multiple-product market and put forward a suppose to show that the multiple-product strategy is not always optimal. Finally, numerical illustrations are designed to examine the impacts of the two crucial factors and obtain the dominant regions for each strategy. By introducing an emission sensitive demand, we show the superiority of the remanufactured product when the extra demand attracted by the emission saving is large.

Local demand management of charging stations using vehicle-to-vehicle service: A welfare maximization-based soft actor-critic model

Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

Pricing curb parking: Differentiated parking fees or cash rewards?

Parking price adjustment is an effective method for influencing curb parking choices, playing a crucial role in alleviating urban parking shortages and balancing uneven distribution. The government has extensively explored the implementation of differentiated parking fees and cash incentives for curb parking; however, few studies have investigated the effects of these policies on parking balance management. This study utilized a questionnaire survey to construct two multinomial logit models and examine the mechanisms behind drivers' choice behaviors. Taking the curb parking facilities in Yinzhou District, Ningbo as the case study area, the optimal dynamic pricing schemes for the two strategies were established, and their operational effectiveness was evaluated. The results show that parking cost, value of cash rewards, parking occupancy, and surrounding road conditions have a significant impact on curb parking choice behavior. Both strategies can effectively balance the utilization rate of curb parking space-time resources. The cash rewards strategy yields higher earnings compared to the differentiated parking fee strategy, but the latter is more conducive for reducing the occupancy during peak hours. The research findings of this study can provide insights and a theoretical foundation for real-time and proactive management of roadside parking.

Leveraging ride-hailing services for social good: Fleet optimal routing and system optimal pricing

With the penetration of ride-hailing services, the impacts of transportation network companies (TNC) on network performance grow. TNC comes with a ubiquitous sensing and pricing system that may be leveraged by public agencies to improve transportation system performance. This study first formulates and solves a mixed equilibrium (ME) of personal driving vehicles and ride-hailing vehicles, where TNCs centrally assign routes to ride-hailing vehicles to achieve fleet-wide optimum. We propose a novel fleet behavior desired by TNCs, named fleet-optimal behavior with service constraint (FOSC), which provides a good compromise between total fleet cost minimization and fairness among riders. However, we show that the system state of FOSC can be far from the system optimum state. To this end, we propose a novel Optimal Ride-hailing Pricing (ORHP) scheme for public agencies as an efficient manner to intervene ride-hailing online platforms. The essential idea of ORHP is to regulate and subsidize TNCs, in exchange for guaranteed network performance improvement. Under ORHP, public agencies set the value of a subsidy for each link for any TNC rider using this link. The subsidies are provided to TNCs, not directly to riders. TNC receives subsidies, and determines the best way to provide compensations for each rider who deviates from his/her ”shortest” route, determined by FOSC. TNC’s ultimate goal is to reduce their total cost, including total fleet vehicle travel time and total compensations, subtracted by the total subsidy received from public agencies. The ORHP is likely less controversial than other pricing schemes, since it calls for voluntary participation of travelers who are provided with multiple route options. Because it is built into the TNCs’ fare system, it is cost effective to implement and hard to game. This would be a win-win: a win for public agencies to cost-effectively improve system performance leveraging TNC’s platform without building physical infrastructure or services; and a win for TNC to profit from subsidies and improve service quality. ORHP is formulated as a bi-level optimization problem, solved with a sensitivity analysis based algorithm and tested on two networks implying the ORHP scheme can be effective: a small total subsidy provided to TNC can lead to significant improvement in system performance.

Optimal pricing strategy of an on-demand platform with cross-regional passengers

Recent years have witnessed the emergence and prosperity of online ride-hailing, which empowers the traditional taxi industry with information technology. However, the ride-hailing platform has no direct control over drivers, one of the biggest challenges for the platform is how to balance the supply and the demand. Considering the cross-regional demand and the surge demand, this paper focuses on the pricing strategy of the ride-hailing platform, and further introduces strategies such as bonus incentive and information sharing. We find that when facing demand surging and supply shortage in one region, the platform can adopt surge pricing or bonus incentive in the adjacent region before the demand surges to balance the supply and the demand. Specially, the surge pricing strategy benefits the platform when the surge demand is high or the cross-regional demand is low, but the bonus incentive strategy has the opposite effect. The existence of cross-regional demand improves the performance of the bonus incentive strategy but reduces that of the surge pricing strategy. We further show that the platform prefers bonus incentive to surge pricing when only a small number of drivers are required to move. But bonus incentive always improves social welfare better than surge pricing. Extending to information asymmetry, information sharing can also encourage more drivers to move. However, the effect is limited such that the proportion of drivers who move will not exceed a threshold. The platform also has the incentive to share false information, which makes drivers question the credibility of the shared information. In this case, the surge pricing strategy can be used as a costly signal to verify the credibility of the information.

Research on the Trade-in Pricing Strategy of New Energy Vehicle Producers Considering the Consumers’ Heterogeneous Behavior

In practice, trade-ins are offered by new energy vehicle producers and include trade-in-for-new fuel vehicles. And trade-in-for-new energy vehicles. By using the game-analytical method, we mainly analyze the optimal pricing strategy of new energy vehicle producers when they provide both of the above mentioned trade-in services. The results show that automobile producers should consider the production cost of new vehicles and the trade-in rebate when they provide a trade-in strategy. On the one hand, when the production cost of new vehicles is high but the trade-in rebate is very low, automobile producers do not provide the trade-in service. On the other hand, when the production cost of new vehicles is very low but the trade-in rebate is very high, automobile producers should provide trade-in services to heterogeneous consumers. Moreover, when the heterogeneous behavior of consumers is strong and the innovation value of new generation fuel vehicles is low, automobile producers should choose the preannounce pricing strategy. Otherwise, these producers should adopt the dynamic pricing strategy.

Differential pricing in social networks with strategic consumers

This study analyzes a monopolistic seller's optimal differential pricing problem with strategic consumers connected in social networks. The consumers who purchase in the later period can get positive externalities from their friends who purchased in the early period but have to bear a utility discount for the delayed consumption. We first characterize consumers' strategic purchase decisions under general network structures. We then derive the optimal differential pricing strategies and demonstrate that different network structures lead to substantially different strategies. We find that when the intensity of the network externality effect is lower than a threshold and the influence matrix is symmetric, it is always optimal for the seller to conduct an increasing‐pricing strategy. However, when the network externality effect is strong, a decreasing‐pricing strategy may also be optimal. We further examine how the imbalance of influence, degree heterogeneity, and network topology impact the optimal pricing policy and profit. We find that when the intensity of network externality is relatively low, it is more profitable to sell products through many interconnected low‐influencer networks; however, when the network externality intensity is high, it is better to sell through a few high‐influencer networks. Finally, we show that the profit loss caused by uniform pricing strategies or by ignoring consumer network structures can be significant under certain conditions, thereby revealing the substantial value of differential pricing in social networks.

Joint Traffic Offloading and Aging Control in 5G IoT Networks

The widespread adoption of 5G cellular technology will evolve as one of the major drivers for the growth of IoT-based applications. In this paper, we consider a Service Provider (SP) that launches a smart city service based on IoT data readings: in order to serve IoT data collected across different locations, the SP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across sites and IoT data collection patterns. We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of SPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the SP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the SP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.