A Choice-Based Dynamic Programming Approach for Setting Opaque Prices

Opaque pricing is a form of pricing where certain characteristics of the product or service are hidden from the consumer until after purchase. In essence, opaque selling transforms a differentiated good into a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated product at higher prices to regular brand loyal customers while simultaneously selling to non-brand loyal customers at discounted prices. We use a nested logit model in combination with logistic regression and dynamic programming to illustrate how a service firm can optimally set prices on an opaque sales channel. The choice model allows the characterization of consumer trade-offs when purchasing opaque products while the dynamic programming approach allows the characterization of the optimal pricing policy as a function of inventory and time remaining. We compare optimal prices and expected revenues when dynamic pricing is restricted to daily price changes. We provide an illustrative example using data from an opaque selling mechanism (Hotwire.com) and a Washington DC-based hotel

Setting Prices on Priceline

Priceline is best known for its name-your-own-price format, in which consumers bid for services but not for service providers. Because Priceline serves as an opaque selling mechanism, it attracts price-conscious consumers. Sellers also benefit because they can price into multiple market segments without worrying that they are diluting revenue they might receive from customers who are willing to use conventional selling channels and pay more. A firm that releases its inventory to Priceline must manage the trade-off of pricing its inventory too low (and forgoing revenue) versus pricing it too high and forgoing a sale. In this paper, we outline the mechanism that Priceline uses to determine if customer bids are successful and, given this mechanism, establishes optimal prices and inventory allocations for Kimpton Hotels

Dynamic Capacity Allocation for Airlines with Multi-Channel Distribution

In 2017, China's online e-commerce sales has already reached 29.16 trillion Yuan. While gaining huge benefits, it also poses great challenges for each industry. One of the biggest challenge is the change of sales channels. Of course, there are also huge opportunities between them. Among them, it is a huge impact on the perishable products industry such as the airline industry and the hotel industry. Because of the perishable of the product, both hotels and airlines want to be able to sell the product for a limited period of time and gain considerable benefits. Therefore, at the beginning of the industry, airlines and hotels hoped to sell their products through more channels and attracted channels to sell products by paying their commission fee. With the rapid development of e-commerce, more and more online channels are replacing traditional offline channels. The change of channels has brought great challenges to airline management and costs. For example, although online channels absorb more customer demand, the commission costs of airlines have increased significantly. In addition to the cost pressures imposed on airlines, the increase in channels has brought conflicts between channels and between channels and airlines. Some of the channels' behaviour has caused great losses to the airlines. For example: change the condition of retreat fee, increase ticket or room price, maliciously reduce the price to compete with the airline and so on. These behaviours have affected the airline's reputation and have also brought losses to the airlines. In order to deal with the challenges of online agents, the airline has also taken some corresponding measures, such as the opening of online direct marketing websites, direct sales APP and so on. However, the effect has not been very good and it is difficult to compete with online agents who have customer volume. At the same time, we also see that the airline industry and hotels are also facing great competitive pressure. For example, the high-speed rail increases the competitive of civil transport markets. High-speed trains generally have higher on-time rates than aircraft and also high-speed rail stations are generally more convenient for customers in the city. Therefore, for passengers, high-speed rail has advantages in short trips. In addition, the emergence of low-cost airlines has also intensified competition in the civil aviation industry such as China's Spring Airlines, Europe's Easyjet and Ryanair. Therefore, recently reducing channel distribution costs has been concerned for many airlines which are facing fierce competition in airline markets. In a long period since the 1970s, capacity control has always played a pivotal role in defining airlines market strategy. However, when airlines select distribution channels and make capacity allocation decisions, they still separately make different decisions. Hence, when a customer purchases a ticket from a channel with an appropriate fare class, the channel might not be an optimal channel from the airlines' perspective. When the airline sells a ticket in a right channel, the ticket price is probably not a right fare. Therefore, how to establish a better channel and fare class capacity control model has become the key for airlines to increase revenue. This thesis is a study based on the above issues. The main work includes the following aspects: At first, we studied the single-leg capacity allocation problem that considers the channel factor. Although the network revenue management has a lot of academic research and has been applied in international routes, for many domestic routes airlines still basically use single-leg revenue management system. In addition, from the historical development of revenue management, the single-leg revenue management model is the basic model of all revenue management models. Therefore, it is important to first establish a single-leg revenue management model that considers the channel issues. In this study, we will integrate channel distribution into dynamic capacity control model. The model can make channel decisions in conjunction with inventory and this is similar to the procedure shown in pure capacity allocation. The study has proposed an optimal policy basing on bid price that incorporates commission fee, price, and capacity. The numerical experiment results illustrate that introducing the channel distribution into airline revenue system can significantly improve the revenues and efficiently reduce the channel distribution cost for airlines. The numerical experiments demonstrate that airline revenues will increase more than 3% in a simple integrated system with two channels compared to the independent model. This study also analyses the reasons for improvements in different situations (such as multi-channels have better improvements than a single-channel and the model has a better match of channels and fare classes) so that management insights are obtained for airlines. Secondly, we analyse customer demand behaviours and we find that customers will experience demand transfer behaviours when facing channels. In the Internet age, due to more transparent information, the customer's transfer behaviour has been continuously expanded. For customers, the transfer of channels is more likely to occur than the transfer of fare classes because they do not need to pay for it. Therefore, it is necessary to establish a better revenue management model to consider the customer's channel transfer behaviour.In this part, we added customer channel transfer behaviour based on the original single-leg dynamic capacity allocation model that considers channel issues. We also developed the optimal policy for this model and made some numerical experiments. The numerical experiments demonstrate that the customer shift behaviour can influence the results of the model and subsequently the decisions of airlines. In the general numerical result, the new model can increase 1.23% than the above channel model. At the same time, through the analysis of the results, the airlines are provided with corresponding suggestions to face the customer's choice behaviour. For example, the airline needs to increase the customer's transfer rate through some methods, such as joining a price comparison network and increasing policy incentives. Thirdly, on the base of single-leg model, we propose a new network dynamic model to integrate network revenue management and channel distribution. To take a network structure airline, the airlines can make more revenue benefits comparing the single-leg method. Although the network dynamic model can make more improvements, the exact optimization is impossible for practical purposes because of the curse of dimensionality. Therefore, we use determined linear programming method for approximating to dynamic model. The numerical experiments demonstrate that the airline revenues can increase more than 3% in a simple network when the commission rate is 15% compared to the traditional network model. In addition to the above studies, the paper also summarizes the original literature on revenue management and channel issues and proposes future research directions

Pricing and Revenue Management

The focus of this chapter is on the strategic role of price in revenue management (RM). In order to successfully use price as a strategic weapon, firms must address two questions: what prices to charge and how’ to determine which customers or market segments should be offered those prices. In addition, companies must study and understand both customer and competitive reaction to their use of RM pricing. In this chapter, I address these questions through a review of the relevant literature and of current practice

Revenue Management: Advanced Strategies and Tools to Enhance Firm Profitability

Much of the past research on revenue management (RM) has focused on forecasting and optimization models and, more recently, on adaptation of RM to the specific needs in various industries, such as restaurants, car rental, transport and even health care services. Surprisingly, although many industries have become increasingly customer-focused, the customer seems to have been relatively forgotten in this stream of research. Our intent in this monograph is to help explore the role of marketing in RM in more depth

Revenue management: progress, challenges, and research prospects

This paper evaluates the main developments of revenue management (RM) over the past decade and discusses RM challenges and research prospects. It examines nine notable emerging themes: total hotel RM, big data analytics, distribution, rate integrity, RM and marketing strategies alignment, social media impacts on RM, RM system, applications of RM in non-traditional service sectors, and RM education and training. We argue that these developments have far-reaching implications for real-world RM practice and anticipate that the topic areas will continue to be popular for hospitality and tourism research in the foreseeable future

Competition in the advanced sale of service capacity

Pre-print of an article accepted for publication in International Journal of Revenue Management; authors' draft dated March 6, 2008; final version available at http://www.inderscience.com

Identifying the Optimal Combination of Hotel Room Distribution Channels: A DEA Analysis with a Balanced Scorecard Approach

The hotel industry has experienced changes brought on by growth, customer expectations and the proliferation in the use of e-commerce and online distribution channels. Future hotel success depends on how effectively hotel revenue managers are able to manage all of the different booking channels to maximize hotel revenue. This study represents a new approach for hotels, the use of a Data Envelopment Analysis-Balanced Scorecard (DEA-BSC) model to measure efficiency of distribution channel mix as measured by balanced scorecard results. DEA-BSC was chosen for this study because while traditional business models typically focus on one performance measure like profit, DEA-BSC considers multiple metrics simultaneously (Zhu, 2014a). Inputs for this study included the percentage of rooms sold revenue of five distribution channels including C-Res/Voice, GDS, brand.com, OTAs, and property/relationship sales. Output was consolidated BSC average. Hotels (DMUs) for the study included fifty-three select service hotels managed by a hotel management company with hotels located throughout the United States. Findings indicated that the DEA-BSC model were able to use channel mix as inputs and consolidated BSC average as output to identify efficient (benchmark) hotels and inefficient hotels. Findings also provided measurement and direction regarding the gap between the hotels that were efficient vs. those that were not. The model could not provide information on whether one output was more effective than another in contributing to the success of a hotel (DMU), but findings generated by the DEA-BSC model provided each inefficient hotel (DMU) with benchmark comparison information to assist the inefficient hotel (DMU) to become efficient

Discounting An Empirical Justification For Its Value In The Lodging Industry

The central focus of this study is to provide an empirical explanation regarding the efficacy of the managerial expectation formation process as it contributes to the understanding of discounting room rates as a rational strategic phenomenon in the lodging industry. The study assesses the nature of the relationship between discounting hotel room rates and hotel financial performance when considering the non-stationary conditions of a time series data set. The study was rooted in an operational based perspective with regard to the challenges presented by the perishable nature of room night sales - the loss of which may impact a manager’s fundamental responsibility: to generate maximum revenue from the existing hotel room capacity. Of critical importance to this study is whether the incremental use of discounting room rates could work to correct for temporal periods of decreased demand and thus increase shortterm hotel financial performance. There is limited research regarding the empirical relationship between discounting room rates and hotel financial performance, as well as the internal process that a hotel manager uses to determine an accurate room rate that corresponds to seasonal lodging market demand conditions. An empirical foundation for this practice is lacking in the extant hospitality literature. Literature reveals that, although the lodging industry commonly incorporates discounting as a pricing strategy, recent research implies that high occupancy levels at discounted room rates do not necessarily lead to an increase in hotel financial performance. The contrast then between what is practiced and the recommendations from pricing strategy studies has led to lack of consistent agreement in current lodging literature regarding how discounting of hotel room rates relates to hotel financial performance. This study is at the forefront in its use of the methodological procedures that support a theoretical framework iv capable of providing explanations regarding managers’ internal process of discounting as an effective pricing strategy that could compensate for times of decreased room demand. An econometric case study research design was used in conjunction with a cointegration analysis and an error correction model (none of which are otherwise appropriated as assessment tools in the lodging industry). These applications provide a means to understand the expectation formation process of managers’ room price setting strategies. They also assess the empirical nature of the relationship between the variables by accounting for the erratic variations of room demand over time as induced by random error fluctuations. A non-deterministic system was assumed and supported through the analysis of the stationarity conditions of the time series data set under investigation. The distinguishing characteristics of a dynamic system that are recognized as traits of the lodging industry are further supported by the theoretical framework of the rational expectations theory and the cobweb model. The results of the study are based on secondary financial data sets that were provided by a midscale independently owned leisure hotel in the Orlando, FL market and that is located on Walt Disney World property. The results of this study delineate from the current normative economic recommendation based on descriptive research that claims discounting hotel room rates does not increase hotel financial performance. The current study does not draw an association between the variables from the presupposition of a deterministic marketplace, nor does it recommend to managers to hold a constant average daily rate over time. Based on the findings of the statistical procedures performed and the theoretical framework, the study contends that previous research may have incorrectly modeled room price expectations; elected to use inappropriate statistical tests; and, therefore, may have entertained misleading conclusions regarding the relationship between discounting of hotel room rates and hotel financial performance. v Through use of an error correction model, the major findings of this study imply several concepts: that residuals may be treated as a variable within the study’s model in order to better understand the short run dynamics that may lead to equilibrium correcting room price positions over the long run of time; that discounting room rates works in the short run; and, that managers use a rational price setting strategy to set future room rates. All of the aforementioned concepts fall within accordance of the rational expectations theory. The study concludes that while the constant room rate adjustments observed in the lodging industry may display what appears to be a random structure that deviates from the expected systematic, or stable, financial performance of a hotel over time, the deviations in performance are actually a rhythmic synthesized process of market information from past and current times. Hence, hotel managers appear to be using a backward looking model to forwardly project optimal room rates to match uncertain consumer demand. The empirical assessment employed in this study supports this determination