Pricing Strategies Research Articles

Enhanced Ridesharing Using Directions API and Geolocation

Abstract: In the ever-evolving realm of urban transportation, the fusion of technological advancements and ubiquitous mobile connectivity has sparked a paradigm shift in how cities are navi- gated. This shift has led to innovative approaches in ridesharing and crowdsourcing, offering promising solutions to tackle traffic congestion, reduce emissions, and prioritize travel needs in urbanareas. Unlike traditional carpooling methods, where arrange-ments are made in advance, ridesharing in smart cities operates on real-time matching of drivers and riders, with priority given to factors such as destination and purpose.This study delves into the intricate dynamics of ridesharing and crowdsourcing for smart city transportation, investigating the complex interplay between technology and social factors.Additionally, it considers aspects of security, policy, and pricing strategies to ensure a seamlessand equitable experience for all stakeholders.Drawing insights from comprehensive case studies and practical implementations, this exploration highlights the advantages of ridesharing and crowdsourcing, including the reduction of carbon dioxide emis- sions and the prioritization of travel needs based on purpose. It offers guidance for stakeholders navigating the complexities and embracing the transformative potential of these innovative solutions in shaping the future of urban mobility.

Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification

In today's hyper-competitive business landscape, leveraging data-driven strategies is paramount for sustainable growth and profitability. This review presents an overview of the imperative role of predictive analytics in facilitating business expansion by enhancing profitability and identifying opportunities. Predictive analytics harnesses historical data and advanced modeling techniques to forecast future trends, enabling businesses to make informed decisions with precision. By understanding predictive analytics, businesses can effectively identify expansion opportunities by analyzing market trends, segmenting customer bases, and uncovering new markets and niches. Moreover, predictive analytics empowers organizations to enhance profitability through optimized pricing strategies, demand forecasting, and personalized marketing initiatives tailored to customer preferences and behaviors. Implementing data-driven strategies for business expansion requires building a robust data infrastructure, selecting appropriate tools and technologies, and fostering a data-driven culture within the organization. Real-world case studies illustrate the transformative impact of predictive analytics on business expansion, offering insights into best practices and lessons learned. Despite the undeniable benefits of predictive analytics, challenges such as data privacy concerns, ensuring data quality, and organizational resistance must be addressed. However, the rewards of embracing data-driven decision-making far outweigh the challenges, positioning businesses for sustainable growth and competitive advantage in the dynamic marketplace. In conclusion, this review emphasizes the critical role of predictive analytics in driving business expansion by enhancing profitability and opportunity identification. By harnessing the power of data, businesses can unlock new avenues for growth, mitigate risks, and stay ahead of the curve in today's data-driven economy.

Equilibrium balking behavior of customers and regulation measures in a multi-server queue with threshold policy

ABSTRACT The equilibrium balking behavior of customers and some effective regulation measures in a multi-server queueing system with a threshold policy are mostly investigated in this paper. We consider four information levels: entirely visible, nearly visible, nearly invisible and entirely invisible. For each case, we compare the equilibrium and socially optimal balking strategies of customers, formulate pricing strategy and analyze the revenue of the system manager. It is found that no matter what information level, there always exist the optimal thresholds that maximize the optimal social welfare and the system manager’s revenue, respectively. Moreover, how much information the customers know has influence on the optimal social welfare. Such findings can provide guidance for the social planner to formulate pricing strategies, make appropriate threshold policies and decide what information should be disclosed to customers to improve the optimal social welfare.

Research on factors affecting second-hand car market prices

This study uses a multiple linear regression model to analyze the key factors affecting second-hand car prices. Using data exploration and regression analysis, 945 samples spanning the years 2019-2024 were analyzed, and four variables were chosen for further examination. In terms of price, these factors include the impact of mileage, the year the vehicle was manufactured, the brand category, and the color. According to the findings, the most important factors that influence price are the number of miles driven and the year the vehicle was manufactured. Additionally, the category of the brand has a significant impact, while the color has a negligible effect on the price. In addition to assisting customers in making more informed choices, the findings of this study can also provide car dealers with valuable insights regarding pricing strategies and inventory management. The findings of this study also revealed that the market for used automobiles is characterized by a significant circulation and a multitude of factors that have an impact. To further refine the existing model, further research could be conducted to take into consideration additional potential influencing factors.

Welfare loss of pricing schemes in commuting train and interregional train operations

As metropolitan areas expand, commuter rail increasingly extends outward, creating unintended competition with interregional rail. Competition between commuter train service and interregional train service engenders an inefficient allocation of rail services, thereby inducing welfare losses. Hence, the imperative arises to stratify train classes clearly via rail fare adjustments. However, the public nature of rail services acts as a price ceiling, making it challenging to implement rail service fare increases, thus necessitating a solution capable of distinguishing between train classes with minimal fare adjustments. This study employs a Stated Preference (SP) survey targeting rail users traversing the Seoul-Suwon and Seoul-Cheonan segments of the Gyeongbu Line in Korea to elucidate remedies for enhancing the inefficiency prevalent in the rail pricing schemes. Distinct preference clusters for each rail service and the value of time and seat reservation service for rail users were preliminarily discerned by leveraging the SP survey. The proposed solution advocates for an increase in interregional rail service fares of $0.52 for the Seoul-Suwon section and $3.14 for the Seoul-Cheonan section, along with a restriction in seat reservation services, to achieve differentiation between train classes. Implementation of this solution is anticipated to recalibrate the mode share within the rail services, fostering efficiency in service allocation and bolstering the profitability of rail operators, thereby mitigating welfare losses. This study contributes to rail pricing and efficiency of railway operations through SP analysis of rail transportation.

Identifying and Nurturing New Sales Opportunities: Study of UltraTech Cement’s Exploration of Potential Counters in Bilha and Masturi Blocks (Bilaspur)

This study looks into the dynamics of finding and developing new sales possibilities in the context of UltraTech Cement's investigation on possible counter locations in Masturi Blocks, Bilaspur, and Bilha. The research explores the complex interactions among variables that shape merchants' choices about cement brand promotion or inventory. Important insights that emphasise the critical roles played by supplier incentives, market demand, pricing strategies, and brand reputation in influencing retailers' decisions are revealed by thorough data interpretation and analysis. Furthermore, opinions about UltraTech Cement's standing in the market and contrasts with other well-known companies offer insightful information about positioning tactics. The report also makes suggestions for improving market penetration and retailer cooperation, such as customised marketing campaigns, ongoing training initiatives, and rewards for top-performing merchants. Using these realisations and putting strategic initiatives into place, UltraTech Cement can increase its market share in the Bilha and Masturi Blocks segments, fostering long-term growth and solidifying its dominance. Keywords: Market Dynamics, Brand Reputation, Pricing Strategies, Retailer Collaboration, Marketing Initiatives, Sustainable Growth.

Analysis of Li Auto's Marketing Strategy Based on Marketing Mix Theory

This paper analyzes Li Auto's marketing strategy through marketing mix theory, including links to its products and publicity concepts, a unique selection of additional electric vehicles, optimization of product quality and product series, an extensive direct sales network and major cities, the establishment of service centers in the region, etc. The paper suggests that Li Auto should strengthen advertising operations on the we-media platform and, at the same time, adhere to the combination of online and offline marketing methods, strengthen scientific and technological investigation and development, and continuously launch safer, more comfortable, and more convenient products according to the demands of users. In addition, the direct sales network should be expanded to improve the quality of after-sales service. The paper also makes an in-depth analysis of the market development trend of Li Auto and provides an important reference case for enterprise strategic choice. However, the paper has insufficient analysis of Li Auto's pricing strategy. In the future, Li Auto's price strategy will be discussed in more depth, including its pricing strategy, price adjustment strategy, and price matching with market demand, to evaluate its competitiveness and market performance more comprehensively.

Marketing Strategy of the Coffee Industry under High Saturation of Globalized Brands-Take Luckin Coffee as an Example

With the arrival of the "Internet +" era, the marketing mode of coffee products is also undergoing constant changes, and the rise of the "new retail" sales model has brought great impact and challenges to the traditional marketing model. This paper will take the typical representative of this business model, Luckin Coffee, as an example, using qualitative research, STP theory, marketing theory, and the case study method to analyze. The article mainly discusses how to utilize and innovate the existing competitive advantages and business model of Luckin coffee, by analyzing and evaluating the strengths, weaknesses, threats, and challenges of Luckin coffee's pricing strategy, product strategy, and promotional strategy to realize precision marketing, introduce the relevant information and some marketing strategies of Starbucks, and borrow from Starbucks' strengths in product marketing, to propose solutions to the the current marketing state of Luckin Coffee: Customer-oriented, while focusing on product differentiation, and through effective "online + offline" publicity to achieve precision marketing and help the coffee industry diversify and develop sustainably under the high saturation of global coffee brands.

Household water price and income elasticities under increasing-block pricing policy in China: An estimation using nationwide large-scale survey data

Abstract Figuring out household water demand response is of importance to sustainable water pricing policy making and optimalization. The paper estimates price and income elasticities of residential water demand in China by using the unique dataset from Chinese Household Water Use Behavior Survey 2019 in 50 cities. Two instrumental variables of marginal water price and average water price are used to address the endogeneity in the context of increasing-block water pricing policy. Results show that price elasticity ranges from -0.170 to -0.543, meaning that the demand for water is inelastic. Income elasticity ranges from 0.062 to 0.133, indicating that water is a necessary commodity. It unveils that water scarcity cities have more sensitive price response. It verifies the effectiveness of the differential increasing-block water pricing schemes tailor-made to local water resources endowments. Besides, it shows that high water-consuming households have more sensitive price response. It suggests that implementing more stringent IBWP scheme for those households constitutes a promising policy improvement option in the future.

Vehicle pricing considering EVs promotion and public transportation investment under governmental policies on sustainable transportation development: The case of Norway

The present research investigates the advancement of sustainable transportation by promoting electric vehicles, expanding public transit, and implementing tax increases on fossil fuels and automobiles, via crucial strategies. The present study has examined a market with a fossil fuel vehicle (FFV) manufacturer, an electric vehicle manufacturer, and the government. The manufacturers' pricing strategies are geared towards optimizing profits, whereas the government's objectives for transportation are centered on attaining sustainability across economic, environmental, and social dimensions. The government has considered two strategies, one taxation-based and the other non-taxation-based, to achieve its objectives. In the taxation-based strategy, fixed taxes on fossil-fuel vehicles and fossil fuels are considered, while in the non-taxation-based strategy, subsidies for purchasing electric vehicles and the number of buses purchased for public transportation are predefined. Based on this, eight scenarios with different values for the predefined factors of each strategy have been defined to attain their respective objectives. Game theory has been utilized to determine an optimal solution based on Norwegian data, owing to the interconnected decision-making among stakeholders. By analyzing the findings, managers can enhance their decision-making capabilities. The results suggest that appropriately adjusting subsidies, taxes, and bus numbers within specific thresholds is crucial for mitigating air pollution and achieving other objectives. The assertion that electric cars are a definitive solution may not be accurate, as it depends on the source of the generated electricity. While excessive subsidies in non-renewable energy countries can lead to increased pollution, even in Norway, where most electricity is renewable, rising demand for electric vehicles contributes to air pollution due to increased production. In countries where electricity generation is not primarily from renewable resources, the growth of electric vehicles further exacerbates pollution.Simultaneous increases in fuel taxes and tariffs on fossil fuel vehicles negatively impact the demand for fossil fuel vehicles and government revenues. Therefore, it is recommended that an increase in fuel taxes be implemented to optimize both government revenues and social welfare. This approach safeguards public revenues, promotes societal well-being, and mitigates air pollution.

Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness

Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses retail price optimization as a regression problem, utilizing machine learning models to predict optimal price points for products. Leveraging factors such as product attributes, competitor pricing dynamics, and customer behaviors, regression analysis provides a structured approach to understanding the intricate relationships between variables. Among various regression techniques, the Random Forest Regressor emerges as a potent strategy, offering robustness and versatility in tackling complex pricing tasks. Results indicate that Random Forest outperforms Decision Tree and Logistic Regression models regarding accuracy, precision, recall, and overall predictive performance. With Random Forest achieving an accuracy of 94%, it demonstrates superior capability in capturing complex data patterns and making accurate predictions of retail prices. By leveraging advanced analytics and machine learning techniques, retailers can optimize pricing strategies, maximize profits, and maintain competitiveness in the market. This study underscores the importance of continuously analyzing and refining pricing strategies to gain a competitive edge in the retail landscape.

Mapping Payment and Pricing Schemes for Health Innovation: Protocol of a Scoping Literature Review.

Innovative pricing and payment/reimbursement schemes have been proposed as one part of the solution to the problem of patient access to new health technologies or to the uncertainty about their long-term effectiveness. As part of a Horizon Europe research project on health innovation next generation pricing and payment models (HI-PRIX), this protocol illustrates the conceptual and methodological steps related to a scoping review aiming at investigating nature and scope of pricing and payment/reimbursement schemes applied to, or proposed for, existing or new health technologies. A scoping review of literature will be performed according to the PRISMA guidelines for scoping reviews (PRISMA-ScR) guidelines. The search will be conducted in three scientific databases (i.e., PubMed, Web of Science, and Scopus), over a 2010-2023 timeframe. The search strategy is structured around two blocks of keywords, namely "pricing and payment/reimbursement schemes," and "innovativeness" (of the scheme type or scheme use). A simplified search will be replicated in the gray literature. Studies illustrating pricing and payment/reimbursement schemes with a sufficient level of details to explain their characteristics and functioning will be deemed eligible to be considered for data synthesis. Pricing and payment/reimbursement schemes will be classified according to several criteria, such as their purpose, nature, governance, data collection needs, and foreseen distribution of risk. The results will populate a publicly available online tool, the Pay-for-Innovation Observatory. The findings of this review have the potential to offer a comprehensive toolkit with a variety of pricing and payment schemes to policymakers and manufacturers facing reimbursement and access decisions.

Limited‐time pricing strategy considering consumer regret under channel competition

AbstractThis study constructs a pricing strategy model that considers consumer busyness and low price duration to examine the effectiveness of limited‐time pricing in influencing consumer behavior. The results show that local store can develop a limited‐time promotion strategy to improve profit, and there is an optimal low price duration. As consumer busyness increases, online retailer's profit may decrease, as local store is better able to leverage consumer busyness to enhance competitiveness. Interestingly, when local store lower price, online retailer may actually need to increase price to obtain greater profit from consumers who missed out on store discounts.

The Effects of Brand-self Connection on the Willingness to Pay a Price Premium: A New Look at the Consumerbrand Relationship in the Chinese Luxury Market

Exploring brand-self connection as a tool for driving profitability is gaining traction among scholars and industry professionals. This study introduces and validates a conceptual model aimed at discerning the influence of brand-self connection on luxury consumers willingness to pay (WTP) a price premium. Based on survey data from 270 Chinese luxury consumers and analysis via SPSS 27.0, the results reveal that brand-self connection not only directly augments consumers WTP a price premium but also exerts an indirect influence through the mediating roles of brand experience and brand loyalty. Interestingly, though brand-related social media engagement emerges as a moderating factor in this relationship, its impact remains marginal within the luxury market context. Together, these findings illuminate the complex nuances of luxury consumer behavior, enriching brand management, consumer-brand relationships, and pricing strategy. Practitioners can harness these insights for strategic decision-making in the luxury market arena.

The effect of Green Marketing, Brand Image, Advertising and Price on Purchasing Decisions

This research aims to determine whether or not green marketing, brand image, advertising, and price influence purchasing decisions for AQUALife products. The population of this research is consumers of AQUALife products in the Central Jakarta area. The sample for this research method used a questionnaire via Gform for a month with a total of 120 respondents. The sampling technique uses a non-probability method, with a purposive sampling technique through distributing questionnaires. The data analysis methods used are multiple regression analysis, t-test (partial), and f-test (simultaneous). The research results show that: (1) Green marketing has no significant effect on purchasing decisions. (2) Brand image has a positive and significant effect on purchasing decisions. (3) Advertising has a positive and significant effect on purchasing decisions. (4) Price has a positive and significant effect on purchasing decisions. Managerial implications: Managers need to ensure products and production processes comply with environmentally friendly principles, managers need to ensure consistent brand image across all marketing channels, managers need to develop competitive pricing strategies, managers need to understand competitors' prices and their price position in the market to maintain competitiveness.

Bridging operations research and machine learning for service cost prediction in logistics and service industries

AbstractOptimizing shared resources across multiple clients is a complex challenge in the production, logistics, and service sectors. This study addresses the underexplored area of forecasting service costs for non-cooperative clients, which is essential for sustainable business management. We propose a framework that merges Operations Research (OR) and Machine Learning (ML) to fill this gap. It begins by applying the OR model to historical instances, optimizing resource allocation, and determining equitable service cost allocations for each client. These allocations serve as training targets for ML models, which are trained using a combination of original and augmented client data, aiming to reliably project service costs and support competitive, sustainable pricing strategies. The framework’s efficacy is demonstrated in a reverse logistics case study, benchmarked against two traditional cost estimation methods for new clients. Comparative analysis shows that our framework outperforms these methods in terms of predictive accuracy, highlighting its superior effectiveness. The integration of OR and ML offers a significant decision-support mechanism, improving sustainable business strategies across sectors. Our framework provides a scalable solution for cost forecasting and resource optimization, marking progress toward a circular, sustainable economy by accurately estimating costs and promoting efficient operations.

Dynamic demand response to electricity prices: Evidence from the Spanish retail market

The energy transition is driving a significant transformation in the electricity sector, with a focus on demand-side engagement. Using the VAR-VEC model methodology, we explore hourly consumer responsiveness to electricity prices for two distinct consumer groups differentiated by their pricing schemes within the Spanish retail market. The research reveals that consumers under real-time pricing adapt their consumption patterns in response to expected retail price fluctuations, particularly during peak hours. These findings offer valuable insights for policymakers evaluating time-differentiated pricing policies. Additionally, retailers can benefit by incorporating these results into wholesale market bidding strategies and retail market planning.

MEDICAL INSURANCE PREMIUM PREDICTION WITH MACHINE LEARNING

A machine learning method for predicting health insurance rates is presented in this article. With healthcare expenditures becoming more complex, it is critical for insurance companies and policyholders to accurately estimate insurance prices. Utilizing a dataset that included medical history, demographic data, and other pertinent variables, a variety of machine learning techniques, such as ensemble methods and regression, were used to create prediction models. R-Squared and mean absolute error were two measures used to assess these models' performance. According to the developed models' results, insurance premiums can be predicted with accuracy, offering useful information for insurance counteragents. This approach has the potential to optimize pricing strategies, enhance risk assessment, and improve decision-making in the healthcare insurance sector. Machine Learning-Based Prediction of Medical Insurance Premiums Make predictions about health insurance companies based on personal traits. A dataset of policyholder attributes (such as age, gender, BMI, number of children, smoking behaviors, and geography) was gathered and preprocessed .Divide the data into sets for testing and training. Create and train a model for an artificial neural network with TensorFlow and Karas. R-squared metrics and mean R-squared error were used to assess the performance of the model. created a high R-Squared predictive model that was accurate. determined the main determinants of insurance rates. Machine learning has shown promise in estimating healthcare costs. This experiment demonstrates how well machine learning predicts medical insurance rates. Insurance companies may offer more individualized insurance plans, expedite the underwriting process, and help customers make well-informed decisions about their healthcare coverage by creating these predictive models. The created model can help policyholders make educated judgments and insurance companies establish proper prices. In the long run, our research helps the insurance industry enhance data-driven techniques, which benefits insurers as well as insured individuals in general.

Machine Learning Insights: Exploring Key Factors Influencing Sale-to-List Ratio—Insights from SVM Classification and Recursive Feature Selection in the US Real Estate Market

The US real estate market is a complex ecosystem influenced by multiple factors, making it critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) data from January 2018 to October 2023 across 100 major regions gathered through Metropolitan Statistical Area (MSA) and advanced machine learning techniques, including radial kernel Support Vector Machines (SVMs), used to predict the sale-to-list ratio, a key metric that indicates the market health and competitiveness of the US real estate. Recursive Feature Elimination (RFE) is used to identify influential variables that provide insight into market dynamics. Results show that SVM achieves approximately 85% accuracy, with temporal indicators such as Days to Pending and Days to Close, pricing dynamics such as Listing Price Cut and Share of Listings with Price Cut, and rental market conditions captured by the Zillow Observed Rent Index (ZORI) emerging as critical factors influencing the sale-to-list ratio. The comparison between SVM alphas and RFE highlights the importance of time, price, and rental market indicators in understanding market trends. This study underscores the interplay between these variables and provides actionable insights for stakeholders. By contextualizing the findings within the existing literature, this study emphasizes the importance of considering multiple factors in housing market analysis. Recommendations include using pricing dynamics and rental market conditions to inform pricing strategies and negotiation tactics. This study adds to the body of knowledge in real estate research and provides a foundation for informed decision-making in the ever-evolving real estate landscape.

On some confirmations in genomic entrepreneurship

Entrepreneurship is an intricate phenotype. Are entrepreneurs born or made or developed or adopted? What makes an entrepreneur think outside the box and pursuing avant-garde paths to attain entrepreneurial goals? Alvin Toffler mentioned of various ‘Waves’ viz. Agrarian, Industrial and so on. Economics and Business Management, today, is experiencing inestimable waves viz. Heterodox Wave, Genetic Wave, Molecular Wave, Cellular Wave, Hereditary Wave, [‘Infoplosion’ or ‘Info-Tectonics’ Wave, Capability Approach Wave, Institutional Wave, Behavioral / Experimental Wave [Cognitive and Emotional dimensions / Develops and uses experiments typically with human subjects], Evolutionary Wave, Cognitive Wave, Information Wave , Artificial [Synthetic and / or Fabricated] Intelligence Wave and Neuro Wave, to list a few, from a rational perspective. Of central interest in this paper is Managerial Business - Economics Wave. It deals with, with complex, deep-rooted problems, gears and technique to dissect market demand, assess expenses, establish pricing strategies, assess risks, and appreciate competitive dynamics. Is entrepreneurship a genetic trait? Of recent origin is emergence of incorporating genetic and biological markers into entrepreneurship. Issue that confronts is; is there a need to sync molecular genetics and entrepreneurial business data to genomic entrepreneurship; under umbrella of ‘Genoeconomics’? Does this mean that entrepreneurship is in genes? Do genes determine entrepreneurial success? Can genetics or molecular genetics and entrepreneurial data predict response to multifarious behavioral intercession? Can genetics calculate retort to intricate behavioral interference? Are genetic markers of interest for entrepreneurship research? What challenges occur when analyzing genetically informative facts? How, if at all, should entrepreneurs use and combine molecular genetics and business data? What challenges arise when analyzing genetically informative data? The moot issue is; can entrepreneurship be genetic? Can an entrepreneur be ‘genetically-inclined’? One debatable question is; why do entrepreneurial parents have entrepreneurial children? What kinds of opportunities will genetic mapping of entrepreneur offer business entities? Do the big Five Personality Traits [Openness, Conscientiousness, Extraversion, Agree-Ableness and Neuroticism] play a role? Do genetic factors influence tendency to acquire skills and develop attributes relevant to entrepreneurship? Is it crucial to emphasize that a genetic predisposition to entrepreneurship should definitely, unequivocally, not be confounded with genetic determinism? This paper aims to seek a conceptualistic conformation to the above issues