Pricing Strategies Research Articles

Tesla’s Electric Future: A Comprehensive Marketing Strategy Analysis and Implementation Plan

This paper deeply explores the essence of Tesla’s marketing strategy in the field of electric vehicles, and accurately extracts three core competitiveness: brand influence, technological innovation and vertical integration of industrial chain. At the same time, it also faces the severe challenges faced by Tesla in expanding production capacity and coping with fierce competition in the market. In view of the explosive growth trend of the global electric vehicle market and the huge potential of the international market, this paper carefully planned a series of comprehensive strategic measures aimed at promoting Tesla’s continued growth. These measures not only cover the diversification of product lines, refined pricing strategies, increased investment in marketing resources and global market expansion, but also emphasize the flexible response mechanism for extreme market demand changes and deep cultivation of emerging markets to ensure Tesla’s leading position in the global electric vehicle industry and continue to lead the trend of industry innovation and development. In such a rapidly changing industry, Tesla must continuously enhance its strategic adaptability and innovation capabilities to meet the challenges of the future.

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

A study of fresh produce pricing and replenishment strategy based on FCM cluster analysis and ARIMA-GARCH time series analysis modelling

Aiming at the shortcomings of poor robustness and weak applicability in the formulation of pricing and replenishment strategies of fresh food superstores and the limitations of ARIMA model in pricing strategies, this paper is based on maximizing superstores' revenue, and proposes to establish an FCM cluster analysis model based on the elbow rule to analyse the correlation of different single products, and concludes that the sales volume of edible mushrooms and aquatic roots and tubers have a strong correlation, and spinach and choy sum have a strong correlation. Then the total sales volume of each category was negatively correlated with cost-plus pricing through least squares fitting. Finally, based on the ARIMA-GARCH model, a daily replenishment model was established, and it was predicted that the maximum daily replenishment of foliage and flowers would reach 213.908kg on 2 July, and the minimum daily replenishment of eggplant would reach 8.415kg on 4 July in the coming week.

Investment and Financing Decisions for Fuel Consumption Reduction in a Vehicle Supply Chain

This paper models the financing and fuel reduction investment decisions of a supply chain under chain and chain competition to analyze firms’ pricing, product, and investment and financing strategies. In two competing supply chains, one of them is willing to invest in fuel consumption reduction to meet its carbon social responsibility and pursue a competitive. It can choose between two financing models, manufacturer financing and retailer financing, to address the lack of investment capital. Consumers are inclined to purchase low-fuel consumption vehicles due to the cost of use and carbon concerns. Results show that under the model of manufacturer financing, the utility of both the manufacturer and the retailer improves. However, in this model, the fuel consumption reduction is lower than that under the model of retailer financing. If it takes the model of retailer financing, the manufacturer can achieve higher utility than under the model of manufacturer financing, but the retailer’s profits fall rather than increase. So the retailer would not take it unless the manufacturer and government compensate it.

Vegetable Pricing and Replenishment Strategy Based on Multivariate Polynomial Regression and SARIMA Model

Fresh products are one of the important parts of the profitability of superstores, and it is very important for merchants to make effective replenishment and pricing suggestions based on historical data. In order to give a reasonable vegetable pricing and replenishment plan, this paper processes the data and then uses multivariate polynomial regression to regress the sales volume and cost-plus pricing of the category, to obtain the relationship between the sales volume of the category and the cost-plus pricing; and combines with the SARIMA model to further give the recommended replenishment quantity; considering the impact of vegetable freshness on pricing, a freshness-based pricing model is proposed, and the optimal pricing and optimal restocking quantity formula, and proved its correctness in principle. In this study, the pricing and replenishment strategies of reference vegetables are given, and future research can further explore the market forecast of other agricultural products on this basis to broaden the application scope of this model.

Leveraging Machine Learning to Optimize Pricing and Restocking Strategies for Fresh Vegetables

In the competitive and diverse retail market, restocking and pricing perishable vegetables is challenging. Using big data and machine learning to analyze sales data, this research can build predictive models to refine these strategies and predict trends[1][2]. This research analyzed sales data from 2020 to 2023, identifying key trends in sales volumes, seasonality, and consumer preferences. It incorporated these findings into a multi-objective programming model that also considered wholesale and loss rates. This research used statistical tests like the Kruskal-Wallis and Mann-Whitney U to detect significant sales differences across vegetable categories, while the Spearman correlation showed strong seasonal and culinary impacts on consumer buying behaviors. To optimize freshness and maximize sales and profits, this research utilized genetic algorithms and regression analyses, including an effective XGBoost model. This model had a Mean Absolute Percentage Error of 16.97 and an R² of 0.94, aiding in predicting daily sales and improving pricing strategies and restocking schedules. These methods helped enhance decision-making, ensuring vegetable freshness and maximizing profitability, with projected profits reaching ¥41,582.87. This study not only highlights the effectiveness of integrating big data and machine learning in the retail sector but also sets a benchmark for operational efficiency in managing perishable goods.

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction

In the digital era, precise product price prediction becomes crucial for enhancing competitiveness in the online marketplace. This paper presents a hybrid model framework that enhances the accuracy of online product price predictions by integrating several machine learning algorithms, including Linear Regression, Decision Trees, and Gradient Boosting. The objective of this approach is to leverage the distinct advantages of each model to address their individual limitations and create a robust unified predictive model. This integration allows for improved handling of complex data relationships and diverse market dynamics that are typical in online sales environments. The results demonstrate that the hybrid model achieves superior prediction accuracy, as reflected in reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics, and an exceptionally high R ² value compared to single-model approaches. These outcomes underscore the efficacy of combining multiple predictive models to enhance the precision of price forecasts in the highly competitive online marketplace. This model fusion strategy not only provides more accurate pricing predictions but also offers strategic insights into the optimal pricing strategies for businesses looking to enhance their market position.

The Art of Balancing Price and Plug: Developing a Theoretical Model for Dynamic Pricing in the Electric Vehicle Market

This study presents a novel approach to understanding the complex dynamics of the electric vehicle (EV) market through the lens of differential game theory. We developed a comprehensive model that captures the strategic interactions between EV manufacturers and charging network operators, while incorporating the effects of consumer behavior, market uncertainties, and reference price effects. Using differential game theory, we examined the impact of reference price effects and the charging network’s influence on pricing strategies, focusing on three distinct approaches: basic pricing, static pricing considering reference price effects, and dynamic pricing strategies. Our model offers new insights into consumer behavior and price expectations in the rapidly evolving EV market. The key findings reveal that under static or dynamic pricing strategies, the optimal pricing for EV manufacturers is positively correlated with the initial reference price. When the initial reference price is high (low), the optimal pricing strategy resembles skimming pricing (penetration pricing). As the effort level of charging network operators increases and their influence on consumers’ purchase decisions grows stronger, EV manufacturers tend to set higher prices. Notably, while dynamic pricing strategies can optimize EV manufacturers’ profits, the profits of charging network operators may decrease compared with static pricing strategies. This integrated approach significantly contributes to the field by bridging gaps among market dynamics, pricing strategies, and the infrastructure’s development in the context of electric mobility, providing a comprehensive framework for understanding and optimizing the EV ecosystem. Ultimately, this study advances sustainable business models that balance profitability, consumer behavior, and the infrastructure’s growth in the rapidly evolving EV market.

Unlocking Market Potential: Strategic Consumer Segmentation and Dynamic Pricing for Balancing Loyalty and Deal Seeking

Background: This paper examines the economic implications of market segmentation on consumer purchasing behavior with a particular emphasis on intertemporal pricing strategies in dynamic markets. Methods: In order to analyze optimal discount rates and the timing for price reductions for consumer segments, including loyal and deal-prone customers, a detailed mathematical model was developed. The model incorporates theories of consumer behavior and pricing elasticity to simulate market responses to price changes throughout a product’s lifecycle. Results: This research indicates that market segmentation enhances sales by targeting the distinct preferences of loyal consumers, who are less price-sensitive and who stabilize revenue streams, and deal-prone consumers, who respond to price reductions. Customizing pricing strategies for loyal consumers and deal-prone consumers increases sales volumes and optimizes profitability. Conclusions: This research improves our comprehension of market segmentation and dynamic pricing, providing a practical framework for businesses to create effective pricing strategies that can be promptly implemented. It emphasizes the significance of understanding consumer behavior and price sensitivity in the interest of revenue promotion. This study also emphasizes the social implications of equitable pricing practices, promoting the implementation of transparent and value-based strategies to promote market inclusivity and consumer trust.

Pharmaceutical Marketing Management in India

Background: In order to identify key tactics and their gaps, the researcher self- designed questionnaire responses from 80 respondents (who either work at managing level in a pharmaceutical company or own their own firm) on Google through convenience sampling across several areas in India. Additionally, the researcher solicited consumer input on marketing assistance, strategies, and tools. We are going to present research on how pharmaceutical firms' marketing tactics change over time as they transition from one to other. It will additionally shine light on the many marketing techniques and tools that pharmaceutical businesses use. Results: A comprehensive cohort comprising 160 patients satisfying the criteria was included in the study. The duration of the study was six months, and the data were collected from the Department of Nephrology of a Government Medical College. Businesses must use digital marketing, or digital devices for marketing (TAB), to improve efficient advertising after studying pharmaceutical marketing strategies and proposing various strategies for conversation. Digital platforms also play a significant role in facilitating effective feedback and surveys related to marketing strategies. For the healthcare sector to effectively communicate and implement strategies, medical representatives must rotate on a regular basis. The supply chain pricing strategy must also adapt to local requirements in order to enhance sales development. The pricing plan for the supply chain must be adjusted to the demands of the region in order to boost sales. In the pharmaceutical sector, developing new medicines and improving existing ones are necessary to boost sales and generate income. This study includes many details and outcomes that need to be analysed further to improve the current pharmaceutical scenario. Work must be done on quality, the use of advanced techniques, and the implementation of new schemes that benefit the MR team

Marketing Strategies and Performance of Small and Medium Scale Enterprises (SMEs) in Ikare Akoko Ondo State

Marketing strategies has been a functional desire and a method for attaining all-inclusive business success. This paper examined effect of marketing strategies on the performance of Small and Medium Enterprises in Ikare Akoko. A descriptive research design was employed. Sample size of 350 was drawn from a population of 2825 which comprised selected registered SMEs owners in Ikare Akoko. Multiple Regression Technique was used to test research hypothesis. The findings obtained revealed that marketing strategy (promotion marketing strategy, price marketing strategy, place marketing strategy, and product marketing strategy) significantly influence performance of SMEs in Ikare Akoko. The research concluded that marketing strategy (product strategies, promotion strategies, price strategies and place strategies) have a significant and positive impact on performance of SMEs in Ikare Akoko. Hence, the paper recommended that SMEs in Nigeria should establish varieties of quality products with new features and design. This will increase many customers to patronize their products. They should also charge equitable price, position suitably to the final consumers. More so, Management of the SMEs in Ikare Akoko should ensure that they increase effort in product advertisement in order to enhance promotional strategy in the organisation. Finally, SMEs should continually ensure goods and services produced reach final consumers or users by locating convenient place for their varieties products. Keywords: Marketing Strategies, Performance, Price Strategies, Promotion Strategies, SMEs,

GST Impact: Perspectives from Registered Traders’ in District Kangra, Himachal Pradesh

This research paper evaluates traders' perceptions of the Goods and Services Tax (GST) and its impact on business operations in District Kangra, Himachal Pradesh. A structured questionnaire was utilized to conduct a comprehensive survey involving four hundred respondents. The focus was on key aspects such as tax simplification, interstate and intrastate trade, pricing strategies, profit margins, transparency in tax collection, and accounting processes. The findings reveal a generally positive perception of GST, particularly in terms of its role in simplifying taxation and enhancing trade efficiency. However, challenges remain, especially concerning compliance issues and fluctuations in profit margins. To improve trader experiences, the study recommends targeted training programs, streamlined compliance processes, and the establishment of effective feedback mechanisms. These insights contribute to a deeper understanding of the real-world impact of GST on business operations and highlight essential areas for future improvement, underscoring the need for continuous support in adapting to the evolving tax landscape.

Pricing policies for printed and electronic books in a triple-channel supply chain

Purpose This study aims to investigate the pricing dynamics within a triple-channel supply chain. The publisher can sell printed books (p-books) through bookstores or online direct sales, and electronic books (e-books) are sold directly through the internet. The primary objectives include determining optimal wholesale and final prices for p-books, assessing the profitability of introducing e-books, comparing profits across channels and supply chain modes and identifying optimal demand volumes. Design/methodology/approach The research uses first-order derivatives and the Stackelberg game to analyze the pricing strategies. Two supply chain modes, centralized and decentralized, are considered, and various parameters are examined to understand their impact on prices, demand volumes and final sales profit. Findings The results indicate that the e-book is either not published or is introduced simultaneously with the printed version in both modes. In the decentralized mode, the wholesale price of a p-book is equivalent to the final price in the bookstore channel in the centralized mode. One channel among the three selling channels is used to maximize the total profit in the centralized supply chain, whereas all demand should be fulfilled through either online direct sales or e-book channels in the decentralized mode. Originality/value This paper introduces a comprehensive triple-channel book supply chain model, considering cross-price sensitivities and lag times for e-books. The study provides insights into the dynamics of the book industry and compares them with existing literature, contributing to a broader understanding of the pricing strategies in a triple-channel context.

Financial Analysis of Apple's Product Pricing Strategy

Abstract: Due to the rise of other domestic mobile phone brands, iPhone updates have slowed down and become less attractive to consumers, resulting in excess iPhone inventory. In response to this situation, in early 2024, Apple began to implement a price reduction strategy, and the price of Apple's iPhone dropped significantly. Given this phenomenon, this paper studies the financial status of Apple in the past three years, including profitability, operating ability and debt-paying ability, and puts forward the problems of Apple's declining cash flow ability in recent years, weakening short-term debt-paying ability, and unsalable inventory products. However, gross profit margin increased slightly and net profit margin on sales was also little changed. Finally, the paper analyzes the reasons for the sharp price reduction of the iPhone and puts forward some suggestions for Apple's future development, such as strengthening product innovation, promoting old products, and reducing the backlog of old products.

Consumer-Oriented Marketing Strategy of Chinese Domestic Otome Games: A Case Study of Love and Deepspace

Abstract: Otome games, story-based romance video games targeting women, originated in Japan in the late 1990s and have since gained popularity all around the world. This paper investigates the rise of Chinese domestic otome games, focusing on Love and Deepspace, a burgeoning and competitive title. The study analyzes its marketing strategy using the 4P model and examines the game's reflection on consumer behavior. The findings show that Love and Deepspace has innovated marketing approaches within the otome game industry, leveraging 3D modeling as the core of its product and price strategies and employing consumer-centric promotion strategies globally. As a conclusion, the paper states that understanding target consumers preferences is vital for future market planning. The research uses Love and Deepspaces success as an example to highlight the significant business value of domestic otome games and provide insights for companies to refine their strategies in this growing market.

Channel structures of third-party platforms

The growing prominence of third-party (3P) platforms in the online retail sector has made the selection of an appropriate channel structure strategy a critical concern for manufacturers, 3P platforms, and retailers operating within this environment. We construct a Stackelberg game model with the manufacturer as the leader, evaluating four channel structures: (A) reselling and retailer agency selling, (B) agency selling and retailer agency selling, (C) reselling and agency selling, and (D) reselling, agency selling, and retailer agency selling. The supply chain can adopt two pricing strategies: uniform pricing (UP) and differential pricing (DP). Research indicates that expanding the number of channels may not always enhance profitability for suppliers and supply chain participants. Under strategy UP, the 3P platform opts for structure B, whereas the retailer favors structure C. Incorporating agency selling is advantageous for the manufacturer; however, structure A represents the least beneficial option for the supply chain. Under strategy DP, a substantial portion of the pareto optimal region is present. Intense competition and reduced agency fees lead both the supply chain as a whole and individual member to prefer structure A. The addition of a reselling or retailer agency channel boosts the manufacturer's profits, and benefits the 3P platform, while structure D emerges as the least favorable option for the retailer. Furthermore, under strategy DP, structure C is rendered obsolete, signifying the retailer's essential role in the supply chain to the advantage of all involved parties. Our study advances the scholarly understanding of sales models and platform economies by offering valuable insights into the decision-making processes of manufacturers, third-party (3P) platforms, and retailers regarding channel structure choices in a 3P platform environment.

Microsoft's Strategic Advantage: An In-depth Analysis Based on Porter's Five Forces Model

Abstract: This article uses Michael Porter's five forces model to analyze Microsoft's competitive situation in the global technology industry. It emphasizes that Microsoft has maintained its industry leadership by continuously innovating its technology and being keenly aware of market changes. The analysis covers Microsoft's strategies in facing market competition, the threat of new entrants, the challenge of substitute products, the bargaining power of suppliers and buyers, and its impact on the company's performance. The study pointed out that Microsoft has established high barriers to entry in its main business areas, thereby effectively preventing the entry of new competitors. The article also analyzes how Microsoft maintains its competitive advantage and market leadership through product differentiation, innovation, flexible pricing strategies, and the ability to adapt to market structure. Although Microsoft faces many challenges, it has successfully maintained its competitiveness in the global market through solid R&D capabilities, an extensive product portfolio, deep market penetration, and an active response to changes globally.

Machine Learning Applications in Salesforce CPQ Transforming Sales

This paper aims to understand the innovative disposals of machine learning applications on Salesforce Configure, Price, Quote (CPQ) applications more precisely how these innovations are revolutionizing software development. Salesforce CPQ is an ideal solution for organizations that want a modern tool to help them with their sales processes, and adding machine learning elements significantly improves the system. For example, the study analyses different cases where and how machine learning algorithm is utilised in the application of the following areas: pricing strategy, product configuration and sales forecasting. Upon collection of historical data, machine learning models are able to make computations of patterns that enables businesses to make quick analysis of the best strategies to use in an offer of various prices to the customers in an effort to enhance customer satisfaction. Furthermore, the application of I/A in the context of CPQ decreases the time engaged in manual configurations, liberating software development teams to work on other priorities essentially. These difficulties and strategies are enumerated in the paper: data quality issues and the impossibility of the Machine learning successful implementation without cooperation with other departments, specifically with Salesforce CPQ teams. In addition, it shares its vision on the tendencies in application of machine learning in CPQ systems with reference to software development paradigms. Finally, the scope of this research is as follows: The study will examine how the utilization of machine learning applications in Salesforce CPQ has changed traditional software development practices in today’s dynamic market environment by increasing efficiency and accuracy effectively, improving competitiveness within the market.

A Study on Work Environment and its Impact on Employees Performance at Boston Business Solutions

This research presents a thorough examination of the supermarket's sales data, focusing on customer behaviour, regional performance, and payment preferences. The supermarket has successfully cultivated a diverse customer base, with a nearly equal split between normal customers and members. This balanced customer distribution suggests a stable foundation for growth, particularly through strategic initiatives aimed at increasing membership and enhancing customer loyalty. The supermarket's regional presence is well-balanced across major cities, including Mandalay, Naypyitaw, and Yangon. Sales are dispersed equally among these regions, indicating that the supermarket has established a strong foothold in each market. This balanced distribution provides an opportunity to tailor strategies to the specific characteristics and preferences of each city, potentially driving increased market share and customer satisfaction through localized approaches. Analysing supermarket sales data is necessary for selecting wisely in business situations and ensuring long-term success. Sales data can also uncover issues in the supply chain, helping supermarkets run more smoothly and efficiently. By examining sales in different regions, supermarkets can adjust their products and promotions to better fit local preferences, improving sales in specific areas. Financially, analysing sales data helps with accurate budgeting and pricing strategies, leading to higher profits. Additionally, it allows supermarkets to evaluate their performance in relation to rivals and quickly adapt to changes in the market, helping them stay ahead. Analysing sales data helps supermarkets understand their customers, operate more efficiently, tailor strategies to different regions, and make better financial decisions, all of which contribute to their growth and success