Tactical Pricing Research Articles

Host Scaling and Revenue Management: Is there a link?

This study explores how a revenue management capability can be created by Airbnb hosts and the role played by their degree of professionalization, defined as the number of listings (apartments) the hosts manage. Considering the explorative nature of this research, a qualitative approach based on interviews has been adopted. The sample includes 55 interviews realized in the Milan destination (Italy). A protocol was created using the four theoretical blocks. These latter identify the main processes of revenue management: i) identifying the required information to establish the revenue management strategy, ii) making strategic and then tactical pricing and revenue management decisions, iii) managing communication with the guests, and iv) making available software dedicated to revenue management. Milan was selected for two reasons: first, is the second leading Italian destination after Rome in terms of overnights. Second, Milan attracts different targets, such as business, leisure, and trade fairs. Therefore, there are continuous changes in off- and on-pick revenue constituting an ideal setting for exploring the creation of a revenue management capability.Three groups of hosts were identified: i) single hosts, ii) hosts managing three to five listings, iii) hosts managing more than 10 listings. Results show strong differences between single hosts and those managing 10 or more listings, explaining why professional hosts are able to extract more value from their listings compared to single hosts. Two theoretical contributions are proposed. First, the research adds to the knowledge about how a revenue management capability (RMC) is created in the context of short-term rentals (STR). The crucial role of professionalization is confirmed, and a complex set of relationships emerge between tangible (such as the presence/absence of a RM software and dedicated staff) and cultural (such as the orientation towards price discrimination and to revenue or price maximization, as well as the approach to guest selection) barriers to RMC. Second, findings can explain why quantitative studies have sometimes shown controversial results on factors affecting price and revenue performance. Interviews with multiple hosts highlighted how they often manage third-party listings based on agreements that include a limit price, reducing the rooms for dynamic and discrimination price, and more generally revenue maximization.

Revenue Management Capability and Host Professionalization Degree

This study explores how a revenue management capability can be created by Airbnb hosts and the role played by the professionalization degree, defined as the number of listings managed by a host. The theoretical lenses used by this study is the resource-based. Based on previous studies on revenue management in the field of hospitality, a wide list of routines, skills, know-how and coordination mechanisms are considered to explain how the revenue management capability can be created by a host. In particular, the revenue management process can be segmented into four major components: i) identifying the required information to set the revenue management strategy, ii) taking strategic and then tactical pricing and revenue management decisions, iii) managing the communication and interaction with the guests, and iv) the availability of a software dedicated to revenue management.
 The study is based on qualitative data collected through interviews in the Milan destination. In line with previous studies, three different groups of hosts where identified: i) single hosts (also called group one), ii) hosts managing 3-5 listings (group two), iii) hosts managing more than 10 listings (group 3). The interviews were recorded, transcribed and coded using the software NVIVO. Results reports strong differences between the three groups of hosts and especially between single and big hosts (those managing ten or more listings). The goals itself are different: while single hosts prefer to select guest and maximize price, big hosts, supporting higher fixed costs, are strongly focused on revenue. All the four blocks are therefore managed in a different way. Small hosts are widely involved in the operating processes (check-in, cleaning, guest interaction) and in many cases the short-term rental is only an additional activity (the main work in another). Therefore, the time dedicated to data collection and revenue management strategy is limited. The opposite is for big hosts that are usually organized as a company with employees specialized per function and with a strong focus on revenue maximization. The paper is supportive for the emerging idea of professionalization degree. The study traces theoretical as well as practical conclusions. The main study limitations are listed.

The impact of a manufacturer’s financial liquidity on its market strategies and pricing and promotion decisions in retail grocery markets

We investigate how two primary determinants of financial liquidity—leverage and cash holdings—affect a firm’s strategic orientation between profit and revenue maximizations, and the consequent tactical pricing and promotion decisions. To this end, we develop a structural model of manufacturers’ strategic behavior and marketing mix decisions in retail markets of grocery and consumer packaged goods. Applying the model to comprehensive data on 92 firms’ financial structure, and their sales, prices, and promotions for 9,111 UPCs sold at 100 retail stores in the Chicago metropolitan area over 400 weeks, we find empirical evidence in support of our hypotheses that increased leverage makes firms with low cash holdings more profit-oriented while firms with high cash holdings become more revenue-oriented. The ensuing pricing and promotion decisions to implement the strategic orientation are shown to vary with the sensitivity of demand to the marketing instruments and their costs.

Predicting Ship Maintenance and Repair Labor with Artificial Neural Networks

Ship maintenance and repair work cost estimation is often regarded as an “Art,” which may contribute to the financial success or distress of a shipyard. Regarded as experts by senior management, estimators are among the most valued resources, and nonetheless, human. Over time, estimators learn from mistakes, and get better with tenure at sharpening assessments. When estimators retire without having groomed an apprentice, shipyards may be at risk of losing a lot of know-how, all at once. These shipyards may well find very costly to experience, for a while, estimating skills stepping back on the learning curve. Yet, even shipyards relying on less advanced information technology may have unwittingly accumulated a lot of valuable data relevant to ship maintenance and repair works. These shipyards may overlook how easily accessible knowledge can be turned into a competitive advantage through predictive analytics. Not only can this data be literally mined, but machine learning algorithms, such as Artificial Neural Networks (ANN), can now process it for a speedy and preliminary estimate through faster and cheaper computing power. To be clear, the purpose is not to replace the human estimator but to help the expert quickly assess, when times are busy, whether to bid or not on a specific project opportunity. In the absence of The Master Estimator, an Apprentice may also look for a quick and cheap sanity check of the prepared estimate before submitting a bid. The study carried out in this article is based on all ship maintenance and repair data recorded at a single North American shipyard over the last 19 years since the current information systems were implemented. This raw data extract with all directly paid hours logged daily by workers on 1277 ship maintenance and repair projects was screened through advanced data cleansing. To enrich the cleansed data tables, additional independent variables were subsequently collected internally and externally to develop a training–testing data set. The final 657 projects represent 136 vessels regrouped in eight types, for which 28 other independent variables were all made available for training up to testing simple ANN models. The scope of this article is limited to the estimation of the direct labor required to complete ship maintenance and repair projects on a specific type of vessels for which workforce planning and tactical pricing was deemed the most relevant to keep the business afloat.

Price determinants and classification of tactical pricing decisions in the cruise industry

ABSTRACT Pricing strategies and tactics and itinerary design are some of the most important decisions faced by cruise companies in order to maximize their profits. This research aims both at analyzing factors that determine the price of a cruise package and at classifying cruise companies according to their pricing tactical decisions. In order to do so, we have gathered more than 84,000 cruise prices corresponding to around 9,200 departures for 2019, which belong to eight of the most important cruise companies. This paper combines two methodologies. On the one hand, hedonic regressions, which are commonly used in the tourism industry in order to identify factors affecting prices. On the other hand, cluster analysis, which allows us to classify cruise companies’ pricing tactical decisions. The results obtained allow us to conclude that a few factors are able to explain more than 76% of the variance of the price: number of nights, type of cabin, month of departure, destination, cruise company and ship rating. Another relevant contribution of this paper is the definition of three clusters when analyzing tactical pricing decisions for cruise companies jointly and eight clusters when the analysis is carried out with ships.

ОСОБЛИВОСТІ ЦІНОУТВОРЕННЯ У ГОТЕЛЬНОМУ БІЗНЕСІ

The stage of investigating the factors that influence price is the most time-consuming. The validity of strategic and tactical pricing decisions depends on how thorough the analysis is conducted. This article deals with the issues of price management in the hospitality industry, including the pricing concepts of small and medium-sized hotels. Effective pricing policy includes not only setting the initial market price, but also methods of payment, types of discounts, price differentiation on various grounds. The cost of a room depends on seasonality, class of the hotel, as well as city and country. Considered characteristic features affecting the process of pricing in today's hospitality industry deserve special attention and are key to the success of the business. The hotel seeks to set a price that would cover the costs of production, distribution, product promotion and an acceptable rate of return. There is great scope for price differentiation in hotel services, both by the type of service provided in terms of volume, standard, time, quantity and by consumer. The constraints of an active pricing policy in the hotel industry are successfully addressed by differentiating prices for services. In the hotel industry, price is an indicator of the quality of service, so the accommodation tariff should reflect the actual supply. If the advertised prices to consumers are high for a hotel in a given class, they will look for another hotel. At the same time, low prices for accommodation services will lead to lower revenues and profits for the hotel establishment. Demand for hotel services is the main regulator of price levels, so incentives and forecasts should be the most important condition when setting the most effective prices. The rule of thumb almost everywhere is that when demand increases, price increases. Pricing policy formation in the hotel is a component of a unified marketing policy and is constantly being shaped by competing companies.

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

ABSTRACTIn today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two‐layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs.

Data Analysis for Dynamic Pricing in Airline

The competitors in the airline industry often rely on pricing strategy to capture more sales as a main mean of competition. Thus, dynamic pricing is often utilized to maximize profit while allowing better pricing against competition at the same time. In order for dynamic pricing to be effective, airline company has to take in consideration both internal and external information. Tactical pricing is an important component for airline, for it provides both short term and long term strategies to ticket pricing. The case is an exemplification of how tactical pricing plays an important role in the decision making process of an airline company.

Revenue Management for a Multiclass Single-Server Queue via a Fluid Model Analysis

Motivated by the recent adoption of tactical pricing strategies in manufacturing settings, this paper studies a problem of dynamic pricing for a multiproduct make-to-order system. Specifically, for a multiclass Mn/M/1 queue with controllable arrival rates, general demand curves, and linear holding costs, we study the problem of maximizing the expected revenues minus holding costs by selecting a pair of dynamic pricing and sequencing policies. Using a deterministic and continuous (fluid model) relaxation of this problem, which can be justified asymptotically as the capacity and the potential demand grow large, we show the following: (i) greedy sequencing (i.e., the cμ-rule) is optimal, (ii) the optimal pricing and sequencing decisions decouple in finite time, after which (iii) the system evolution and thus the optimal prices depend only on the total workload. Building on (i)–(iii), we propose a one-dimensional workload relaxation to the fluid pricing problem that is simpler to analyze, and leads to intuitive and implementable pricing heuristics. Numerical results illustrate the near-optimal performance of the fluid heuristics and the benefits from dynamic pricing.

Teaching Pricing and Revenue Optimization

Pricing and revenue optimization, defined as the formulation and solution of tactical pricing decisions using constrained optimization, is becoming an increasingly popular subject to be taught at the MBA level. Perhaps the best-known example of pricing and revenue optimization is revenue management whereby airlines, hotels, and other companies seek to maximize operating contribution by opening and closing fare classes. However, a number of other important business problems including markdown management, dynamic pricing for e-commerce, and customized pricing are also parts of pricing and revenue optimization. This article gives an overview of the field for those who are considering teaching the topic to MBA students. I outline the scope of the area, describe specific topics that can be included, present a sample syllabus and provide some guidance based on my own experience and links to additional resources.

The numbers game: the role of yield management in the tour operations industry

Courtline/Clarksons Holidays‘ collapse in 1974 was directly due to overoptimistic volume targets, which failed to take account of capacity or demand. Despite the availability of techniques such as yield management, tour operators continue to rely too heavily upon tactical pricing in their marketing. This paper outlines the critical relationship between yield management, the management of capacity, impact of price, and stability of tour operators. © 1998 John Wiley & Sons, Ltd.

The numbers game: the role of yield management in the tour operations industry

Courtline/Clarksons Holidays‘ collapse in 1974 was directly due to overoptimistic volume targets, which failed to take account of capacity or demand. Despite the availability of techniques such as yield management, tour operators continue to rely too heavily upon tactical pricing in their marketing. This paper outlines the critical relationship between yield management, the management of capacity, impact of price, and stability of tour operators. © 1998 John Wiley & Sons, Ltd.

The valuation of brands for licensing

This paper considers the character of brands as capital assets and provides an introduction to the application of discounted cashflow analysis and the capital asset pricing model to the valuation of brands for licensing purposes, as seen from the differing points of view of licensor and licensee. It indicates how the differing levels of risk associated with different brand licensing projects can be quantified for the purposes of strategic decision-making, and provides a model to support tactical pricing decisions in brand licence negotiations.

Pricing Strategies in Highly Competitive Markets

In most highly competitive markets, the use of certain strategic and tactical pricing practices is a key factor in determining profitability. Discusses the extent to which these practices can benefit a firm. Evidence from research conducted in the markets of two groups of industrial distributors (paper merchants and engineers′ distributors) suggests that price wars are inevitable under certain market conditions, and that a strong and defensible position in the market is a necessary prerequisite for control of price levels. The purchase of market share by investment, rather than competition, emerges as the preferred strategy for firms wishing to make a long‐term commitment to remaining in a highly competitive market.

Identifying Negative Products: Do Customers Like to Purchase Your Products

Describes how negative products/services are perceived by consumers as an unpleasant necessity to avoid or reduce a disutility, the negative concept being based on the behavioural modification phenomena of negative reinforcement. Examines empirical data on 14 product categories associated with negative perception. Suggests a pragmatic method for marketers to distinguish between negativeand positive products in order to aid the making of effective strategic and tactical decisions for promotion, distribution, sales training and pricing.

Knowledge support systems for 'smarter' pricing and resource allocation

The role of knowledge support in decision-making is examined. An architecture for knowledge support for product pricing and resource allocation at the tactical or management control decision-making level in companies is presented. It is a hybrid of a decision support system and an expert system. Two concrete systems devised to implement this approach are described. One is Price-Strat, system for tactical pricing. The other is TAPS (targeted allocation of promotional spending), which makes managers to look at the total budget allocation process. It is emphasized that Price-Strat and TAPS are first attempts at providing such knowledge support and leave much room for improvement through further research. >

Use Tactical Pricing to Uncover Hidden Profits

Astute competitors are turning to the flip side of cost—price—for the next wave of performance enhancement.