Journal of Revenue and Pricing Management最新文献

The implementation of promotional pricing strategies constitutes a key component within the realm of retail revenue management. Nonetheless, the accurate prediction of sales in the presence of price discounts proves challenging due to the influence of various factors that contribute to demand uncertainty and high fluctuations. This study aims to find the most suitable prediction models for retail product unit sales while comprehensively accounting for the complex impacts of contributing factors. The dataset, sourced from a case study of a retail company, spans the temporal interval from January 2020 to December 2022. The predictive models, encompassing linear regression, random forest, XGBoost, artificial neural networks, and hybrid machine-learning models, are systematically developed. Then, the identification of the most suitable model is facilitated through the computation and comparative analysis of the Mean Absolute Percentage Error, with due consideration given to the weighting by the respective product’s revenue, thereby offering a comprehensive assessment of overall performance. Additionally, different types of feature selection are experimented. Factors used in machine learning models are either using all the independent variables or using significant factors from the stepwise method, and either considering or not considering exogenous factors of other products in the same cluster grouped by category, subcategory, or K-means method. The result shows that the series hybrid model of random forest and XGBoost outperformed others. Considering factors affecting sales, it is found that the promotion period factor was the most important, followed by discount percentage and price factors. This research provides analytics framework for sales prediction for retails using promotional pricing as a key demand driver.

Once the coronavirus pandemic was declared by government authorities in March 2020 and several measures were adopted around the world to limit the effects of COVID-19, the limit agroeconomic processing affected important operations such as not being able to prepare the olive trees for the next harvest. This lack of processes has caused the consumer to perceive an increase in prices due to the shortage of product and the growing demand for olive oil around the world. This research paper, through the use of advanced statistical and econometric techniques, attempts to perform a specific analysis and understand the persistence of the data and the trend of global olive oil prices. Artificial intelligence techniques such as neural network models are also used to predict long-term price behavior. Using ARFIMA (p, d, q) model, the results suggest a non-mean reversion behavior, suggesting that the shock is expected to be permanent, causing a change in trend. This result is in line with that obtained using machine learning techniques, where the forecast suggests an increase of the prices around + 11.36% in the next 12 months.

Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of (pm 20%) can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by (-10%). Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory.

This study examines consumers’ perceived fairness of hotel dynamic pricing, particularly in the evolving contexts of inflation and the post-pandemic phase. Instead of focusing solely on individual price points or price increases, this study develops a fairness model of dynamic pricing at the strategy level. It incorporates both social and physiological cues and broader contextual factors, given the inherent uncertainty surrounding the equality of outcomes. A sample of 579 U.S. consumers was recruited using Qualtrics consumer panel services. The study employs an orthogonalizing approach to eliminate the collinearity introduced by creating interaction terms. Rather than relying on internal price comparison, this study finds that consumers rationalize the pricing strategy based on two key cues: negative emotions and corporate social responsibility (CSR). Moreover, the study reveals an asymmetric effect of inflation attribution in moderating the cue-fairness linkage. Attributing dynamic pricing to inflation buffers the adverse effect of negative emotions while not enhancing the positive effect of CSR. Lastly, the study indicates that consumers’ perceived fairness of dynamic pricing increases consumer loyalty while decreasing revenge.

Demand estimation is a fundamental component of revenue management systems. The demand for a product can be ascertained from the customers who purchase it. Identifying customer types in this context is a challenging endeavor, recently resolved using meta-heuristic and mathematical techniques. Meta-heuristics leverage the scarcity of data in the search space, commencing with random samples and employing the fitness function as a guide during operations. Our proposed approach generates the search space by incorporating supplementary data to identify valuable customer types. We employ a new period table with additional data to achieve this objective. Subsequently, we reduce the search space through data mining's clustering method and ultimately employ a greedy algorithm and fitness function to identify valuable customer types and construct our solution. To validate our approach, we compare our solution and the most recent research in this field, including genetic, memetic, and mathematical approaches. Compared to memetic methods, our results indicate that our solution has a smaller length, with a maximum reduction of 34%, and exhibits improvement in log value, with a maximum of 7%.

Booking control focuses on the problem of deciding whether to accept or reject bookings to maximize revenue while considering limited capacity. For freight applications, computing the cost of fulfilling requests requires solving an operational decision-making problem which often corresponds to a mixed-integer linear program. We propose a two-phase learning-based approach that first learns to predict the objective of the operational problem, then leverages the prediction within reinforcement learning algorithms to compute the policies. The method is general and applies to different problems faced in practice. We show strong performance on two booking control problems in the literature: distributional logistics and airline cargo management.

Single and small batch production is characterized by complex value-added processes and products. The transparent calculation of new offers and change requests is therefore a particular challenge. At the same time, the rising spread of product service systems (PSS) increases the complexity of costing, as additional intangible services have to be calculated precisely. In addition to the challenges posed by such precise calculation of intangible services, companies have to master another complexity driver in the form of PSS. Innovative information and communication technologies (ICT) offer new potential for effective and efficient design of the costing process for the entire life cycle. The rising availability of data along the entire product life cycle significantly increases transparency and, thanks to intelligent analysis algorithms, allows the identification of clear cause-and-effect relationships and forecasting options. The aim of the presented paper is thus to develop a model for calculation of PPS in single and small batch production that exploits the new potential of ICT.

In the context of volatilities in the international markets in recent times, studies regarding the complexities of oil price fluctuations have focussed on analysing the special fluctuation characteristics of oil prices in different historical perspectives. This study examines the extent to which petroleum price fluctuations under the petroleum price deregulation regime impact on business cycles in Ghana. The study uses the autoregressive distributed lag (ARDL) model with a quarterly data spanning from the first quarter of 2005 to the fourth quarter of 2022. Our empirical results show that price stability impacts positively on economic growth, both in the short and the long run, while foreign direct investment also has a positive effect on economic growth in the short run. Again, we observe that increases in inflation rate and government petroleum revenue negatively affect economic growth both in the short and the long run. To the best of the authors’ belief and knowledge, the observations and recommendations made are consistent with theory and empirical studies and contribute immensely to the discussions about price asymmetry and business cycles. It also offers a nuanced perspective on how policy makers can enact policies that ensure efficient and effective deregulation and price stability.

Abstract

Currently, the hospitality industry is experiencing an increase in the adoption of subscription-based business models among restaurants. Pricing is a critical factor to consider when deploying the subscription models. However, only a few studies in the literature talk of pricing the new subscriptions and even in these studies no algorithm is given for setting the prices. Consequently, this study aims to derive an optimal pricing strategy for subscription services in the restaurant industry through a two-step implementable framework. In the first step, we try to understand the preferences of the consumers and accordingly curate different subscription packages for them. In the second step, we propose a linear programming-based optimization model to price these packages in an optimal manner. The linear programming model is solved by CPLEX 12.7 solver software. Finally, the authors discuss the theoretical and managerial implications of their findings.