Journal of Revenue and Pricing Management最新文献

This paper studies the opaque selling strategy for a parallel-flight airline based on a newsvendor model with stochastic demand. The optimal pricing decision and capacity allocation policy are obtained and analyzed subject to necessary assumptions. There is a relationship between the optimal allocated capacities of these flights, and the airline can adjust its capacity allocation decisions according to this relationship. In addition, the airline is suggested to allocate more capacity for opaque seats if the demand variance is high or the difference in consumer’s preferences between flights is small; meanwhile, a lower price for opaque seats will be provided when the variance is high or the difference is large. Numerical experiments are presented to show the effectiveness of the opaque selling strategy, and the results indicate that this strategy brings a 1.09% revenue increment on average compared to the conventional strategy.

In today’s hotel industry, increasingly more hotels offer additional options on their booking websites, such as “book now, pay later.” Despite the prevalence of this phenomenon, methods to present this option more effectively have received limited attention. Using a 2 × 2 × 2 experimental design, this research examines how appeal type (attribute versus benefit appeals) and temporal distance (i.e., time of booking) jointly influence evaluations of the “pay later” option; it also investigates whether the joint effect has a boundary condition. The results demonstrated that leisure travelers planning a trip in the near future had more positive attitudes and greater purchase intentions when the “pay later” option was presented via an attribute appeal rather than a benefit appeal. However, leisure travelers planning a trip in the distant future did not exhibit such differences in attitudes and purchase intentions. Furthermore, this research revealed that the joint effect of appeal type and temporal distance was evident only for leisure travelers but not for business travelers.

Strategic levers play a fundamental role in revenue management (RM). Earlier research has established price, time, and space as the three levers businesses can wield to optimize performance, but a synthesis of all three is missing. This article presents a three-dimensional model of revenue management levers that categorizes industries in eight octants, visualized as the cube of RM levers. The cube sharpens RM theory and helps companies to identify new opportunities for revenue optimization through comparison with other businesses within their octant. Similarly, it facilitates evaluating possibilities of moving to another octant. Finally, the cube can also assist businesses new to RM to apply strategic levers, address RM collaboration challenges in tourism destinations, and contribute to education in the field.

We investigate the influence of market competition heterogeneity across package sizes on a firm’s pricing strategy. We focus on the transition from quantity discounts (common practice) to quantity surcharges (charging more for larger packages) and hypothesise that firms adopt surcharges when competition is significantly higher in the smaller-package market. Using a survey of 38 grocery stores, we find that the adoption of surcharges rises alongside substantial disparities in competition between sizes, as measured by brand availability. We posit that varying demand elasticities between pack sizes, driven by heterogeneous consumer preferences, may underpin this competition divergence and subsequent pricing strategy shifts. Our findings contribute to the understanding of pricing dynamics under asymmetric competition and offer insights for firms navigating competitive landscapes across product formats.

Recently, price leadership in supermarkets has become a subject of extensive research. In our study, we utilize the Generalized Seaton–Waterson (GSW) method, but with a unique approach based on Granger causality networks. As it naturally captures statistically significant price variation sequences, the multiple interactions observed by a network present dimensions hardly observed when studying pairwise relations. Our investigation centers on retail tea product data from three stores in Argentina. The results highlight numerous significant leader–follower relationships, primarily associated with Black tea options and brand interactions. We distinguish two main brands in the market as segment leaders. This insight sheds light on the dynamics of price leadership within the retail tea market and provides valuable information for market participants and researchers alike.

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%.