Dynamic pricing — A future airline business model

El impacto de la fijación dinámica de precios en las percepciones de los clientes de restaurantes y en el sentimiento hacia los preciosResumenPropósitoEste estudio examina cómo la fijación dinámica de precios impacta en las percepciones de los clientes de los restaurantes y en el sentimiento hacia los precios a través de métricas de reputación en línea. Además, para profundizar en el debate sobre la fijación dinámica de precios, se propone una definición novedosa explorando las formas específicas de discriminación que pueden manifestarse en diferentes industrias.Diseño/metodología/enfoqueUtilizando un conjunto de datos exhaustivo de reseñas de restaurantes obtenidas de TripAdvisor, el estudio se centra en los restaurantes afiliados a uno de los mayores grupos de restaurantes en España. Empleamos un método cuasiexperimental (diferencias en diferencias) para estudiar cómo las estrategias de precios dinámicos influyen en las percepciones de valor de los clientes basándonos en las calificaciones numéricas. Mientras tanto, empleamos un modelo BERT en el componente textual de las reseñas para desentrañar los matices emocionales de la fijación dinámica de precios.HallazgosLos resultados no revelaron un impacto causal de las estrategias de precios dinámicos en las percepciones de los clientes. Además, el análisis de sentimiento no muestra una visión negativa aumentada después de introducir la fijación dinámica de precios en los restaurantes en comparación con el grupo de control. Contrariamente a lo que sugiere la literatura previa, nuestros hallazgos indican que la implementación de precios dinámicos no afecta negativamente las percepciones o los sentimientos de los clientes respecto a los precios en los restaurantes.Limitaciones/implicaciones de la investigaciónLa configuración cuasiexperimental del estudio presenta desafíos inherentes para establecer la causalidad que requieren una investigación más profunda utilizando entornos experimentales controlados. Sin embargo, nuestro estudio revela que los clientes de restaurantes no perciben la fijación de precios dinámica como injusta. Este hallazgo es crítico para los gerentes de restaurantes al considerar la implementación de la fijación de precios dinámica y estrategias de gestión de ingresos. Además, nuestro estudio resalta la importancia de considerar no solo las calificaciones numéricas sino también el análisis del sentimiento del cliente. Este enfoque más holístico para evaluar el impacto de las estrategias de precios puede dar a los gerentes de restaurantes una comprensión más profunda de las reacciones de los clientes. Además, se proporciona una definición de fijación de precios dinámica más rigurosa, aclarando su naturaleza y su distinción en el uso de diferentes discriminaciones de precios.Originalidad/valorEste estudio contribuye a la comprensión en evolución del impacto de las estrategias de fijación de precios dinámicos en las percepciones y sentimientos de los clientes en la industria restaurantera. Su objetivo es llenar el vacío en la comprensión de las reacciones de los clientes a los precios determinados algorítmicamente (a través de sistemas de gestión de ingresos (RMS) como DynamEat) en esta industria. La combinación de inferencia causal y análisis de sentimientos ofrece una perspectiva novedosa, arrojando luz sobre las conexiones matizadas entre la implementación de la fijación de precios dinámicos y las emociones de los clientes.

Peak reduction in decentralised electricity systems : Markets and prices for flexible planning

Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method

The study examined the effect of pricing strategies on the revenue of maize marketers in Benue State, Nigeria. A multistage and purposive sampling technique was used in selecting 400 registered maize Marketers in six Local Government Areas of the State. Primary data were collected using structured questionnaires and analyzed using descriptive and inferential statistics. The findings revealed that majority (91.7%) applied cost plus pricing strategy in marketing of maize; (87.0%) used competitive pricing strategy, while (57.1%) used value based pricing strategy. The results also showed that 56.1% adopted bundle pricing strategy; (49.4%) applied geographical pricing strategy; few marketers used dynamic pricing (16.4%), psychological pricing (6.5%), premium pricing (2.6%), penetration pricing (1.6%), and price skimming (0.5%) respectively. The findings revealed that marketers who used dynamic pricing strategy made the highest sales revenue of ₦122,484.803, marketers that used penetration pricing recorded the least sales revenue of ₦104,333.33. Evidence from the study shows that dynamic pricing strategy enhance sales revenue of maize marketers by ₦15,104.32. It was recommended that government should enhance maize marketer’s access to extension services in the state by conducting training sessions focusing on enlightening maize marketers on more profitable pricing strategies to adopt. The government should also encourage continued education among maize marketers in the state through workshops so as to enable them make informed marketing decisions.

Due to its success and acceptance in the airline and hospitality industry and the growing availability of behavioral, engagement, and attitudinal consumer data, dynamic pricing strategies are gaining popularity. The purpose of this systematic literature review is to answer the research question about how do dynamic pricing strategies affect customer perceptions and behaviors to avoid negative consumer reactions. The synthesis of over 50 articles revealed eight different research streams like for example the factors moderating the impact of dynamic pricing on customer behavior, strategic purchasing behavior in response to dynamic pricing, effect of dynamic pricing on customer perception of fairness, personalized dynamic pricing (PDP) and channel differentiated pricing. To advance future research, this systematic literature review identified the six propositions for further research like for example the assessment of the efficacy of different types of communication by firms seeking to mitigate the negative impacts of dynamic pricing and the assessment of the role and relevant importance of consumers’ personal characteristics upon their perceptions of price changes. The findings of this study have a practical impact for managers and scholars. Scholars may use them to update their research agendas and managers to optimize their pricing strategies to increase revenues.

Pricing is one of the main determinants of a successful carsharing business plan. Companies develop different pricing strategies to increase attractiveness, profit, and service usage. Using dynamic pricing strategies can lead to service improvement in terms of profit and better customer satisfaction. This paper presents a novel research contribution to the field of transportation policy by introducing a new framework for designing dynamic pricing strategies in carsharing operations. We develop two hybrid-pricing strategies to increase profit and user utility in car sharing and analyze the service key performance indicators. These two different hybrid-pricing strategies are based upon two different approaches: one relying on demand related information (i.e., fixed price and time-based dynamic price) and one relying on supply related characteristics (i.e., maximum profit price and availability-based dynamic price). By considering both user utility and company indicators, this model features a bi-level structure that allows for rapid implementation. The framework relies on real-world data, typically available to carsharing companies, including membership data, geographic distribution of users, fleet composition, and the location of vehicles and stations. Additionally, we propose a relocation procedure that relocates vehicles on a day-to-day adjustment process. We study the impact of these strategies in an agent-based environment capable to accurately replicate a real carsharing service that operated in the city of Munich, Germany. Once these policies are in place, results show how it is possible to increase profit and customers’ utility. Moreover, we show how an increment in profit corresponds to a reduction of the utility and vice versa. Overall, the effectiveness of the proposed hybrid-pricing strategies in improving key performance indicators such as profit and score in carsharing services is demonstrated through the positive impact of demand-based pricing combined with relocation operations, while supply-based pricing strategies were found to be ineffective in enhancing profit and booking time.

This study investigates the application of machine learning models for real-time dynamic pricing strategies in the retail and e-commerce sectors. We employed three prominent supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting Machines (GBM)—to predict optimal prices using a dataset sourced from Kaggle. The models were trained and evaluated with a 70:30 train-test split, while hyperparameter tuning was performed using grid search and cross-validation. The results indicate that the Gradient Boosting Machines (GBM) model consistently outperformed the other models, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and demonstrating a higher R-squared (R²) value. The comparative analysis highlights GBM's ability to capture complex interactions in dynamic pricing data, making it a robust choice for accurate price forecasting. The Random Forest model also delivered satisfactory results, balancing accuracy and computational efficiency, whereas the Linear Regression model showed higher prediction errors due to its limitations in modeling non-linear relationships. Real-time testing in a simulated environment confirmed the models' adaptability and responsiveness in a dynamic marketplace. These findings provide actionable insights for retail and e-commerce businesses, emphasizing the importance of model selection, hyperparameter optimization, and system integration to implement efficient dynamic pricing strategies. Future work should explore more extensive datasets and real-world applications to address seasonal variations, regional preferences, and consumer behavior, ensuring a more comprehensive and practical deployment of machine learning-driven dynamic pricing models.

In current competitive environment, retailers are facing a fierce competition and are aiming to manipulate customer purchasing attitudes. Dynamic pricing strategy is a major determinant of retailer’s profitability when considering perishable food. Furthermore, increasing pressure from society and international organizations calls for food security, safety and decreased food waste and losses. This paper investigates dynamic pricing strategy with the objective of maximizing revenue and minimizing food waste to ensure sustainability. A simulation model with stochastic demand based on product price and age is developed using ExtendSimTM Suite. The effect of inventory replenishment quantity on the performance measures is analyzed. Results reveal the superiority of dynamic pricing over fixed pricing strategy in terms of retailer profit and food waste.

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

Dynamic pricing is widely adopted in inventory management for perishable items, and the corresponding price adjustment cost should be taken into account. This work assumes that the price adjustment cost comprises of a fixed component and a variable one, and attempts to search for the optimal dynamic pricing strategy to maximize the firm’s profit. However, considering the fixed price adjustment cost turns this dynamic pricing problem to a non-smooth optimal control problem which cannot be solved directly by Pontryagin’s maximum principle. Hence, we first degenerate the original problem into a standard optimal control problem and calculate the corresponding solution. On the basis of this solution, we further propose a suboptimal pricing strategy which simultaneously combines static pricing and dynamic pricing strategies. The upper bound of profit gap between the suboptimal solution and the optimal one is obtained. Numerical simulation indicates that the suboptimal pricing strategy enjoys an efficient performance.

This study investigates the impact of dynamic pricing strategy on sales performance in private villas listed on the Airbnb platform, focusing on the Canggu area of Bali. Market conditions were examined both as antecedent and moderating variables. Data were drawn from a population of 1,681 private villas in Canggu, with a sample of 323 villas selected using Slovin’s formula (e = 5%). Secondary data were obtained from Airbtics and AirDNA, leading platforms that provide analytics for Airbnb listings. The research applied Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate relationships among variables. Key performance indicators included Average Daily Rate (ADR), Revenue per Available Room (RevPAR), occupancy rate, and total revenue. The findings reveal that dynamic pricing strategy significantly improves sales performance. Market conditions significantly affect sales performance and strengthen the effect of dynamic pricing as a moderator. However, market conditions do not have a significant direct effect on the adoption of dynamic pricing. These results highlight the adaptive nature of pricing strategies to market dynamics and suggest that villa managers should design responsive and context-based pricing policies to maximize revenue.

Reasonable price strategy is of great significance to the safe and economic operation of a microgrid. To reduce the power exchange fluctuation between the main grid and a microgrid and to enhance the profit of a microgrid, a dynamic price strategy based on modified particle swarm optimization (PSO) is proposed. Then, a multi-objective optimization model considering the income of a microgrid and load fluctuation of distribution network is presented. To validate the proposed price strategy and the optimization model, a series of numerical simulations are conducted to demonstrate their effect on the power exchange fluctuation and the profit of the microgrid. The result shows that the proposed dynamic price strategy markedly increases the profit of the microgrid and strengthen the effect on power peak shaving and valley filling compared with time-of-use(TOU) price strategy and the fixed electricity price strategy.

Dynamic pricing strategies: Uniform or discrimination for multi-channel retailing under platform-based supply chain competition

A data-driven methodology for dynamic pricing and demand response in electric power networks