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

Performance Monitor

Revenue management has experienced tremendous growth since its inception in the airline industry. The practice of dynamically pricing a perishable product across different market segments continues to be applied across an ever-increasing set of business arenas. The proliferation of revenue management can be attributed to its microeconomic foundations (grounded in supply and demand), as well as the numerous well-cited success stories (American Airlines, National Car Rental) and failures (People’s Express) that follow from its application or lack thereof. The continued success of revenue management hinges upon the ability to link organizational performance to the pricing and capacity decisions of revenue management systems. This link both reinforces the financial gains attributable to revenue management and indicates opportunities for future improvement. This paper outlines Performance Monitor, a phased approach to performance measurement designed and implemented at Dollar Car Rental. We also present and discuss some examples of Phase I, which is currently in use

Wait or Buy? The Strategic Consumer: Pricing and Profit Implications

Using tools from operations research, airlines have, for many years, taken a strategic approach to pricing the seats available on a particular flight based on demand forecasts and information. The result of this approach is that the same seat on the same flight is often offered at different fares at different times. Setting of these prices using yield-management approaches is a major activity for many airlines and is well studied in the literature. However, consumers are becoming increasingly aware of the existence of pricing strategies used by airlines. In addition, the availability of airline travel pricing on the Internet affords consumers the opportunity to behave more strategically when making purchase decisions. The onset of the information age makes it possible for an informed consumer or a third party, such as a travel agent, to obtain demand information similar to that used by the airlines. In particular, it is possible for consumers or travel agents to purchase historical data or to obtain it by monitoring the seats that are available at various prices for a given flight. If a consumer understands the pricing strategy and has access to demand information, he/she may decide to defer purchase of a ticket because they believe that a cheaper seat may yet become available. If consumers were to make use of this information to make such strategic purchasing decisions, what would be the impact on airline revenues? The purpose of this paper is to investigate these impacts. This work indicates that use of standard yield management approaches to pricing by airlines can result in significantly reduced revenues when buyers are using an informed and strategic approach to purchasing. Therefore, when airlines are setting or presenting prices, they should investigate the effect of strategic purchasing on their decisions

Pricing and Market Segmentation Using Opaque Selling Mechanisms

In opaque pricing certain characteristics of the product or service are hidden from the consumer until after purchase, transforming a differentiated good into somewhat of a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated products at higher prices to regular brand loyal customers while simultaneously selling to non loyal customers at discounted prices. We develop a stylized model of consumer model a monopolist selling a product via three selling channels: a regular full information channel, an opaque posted price channel and an opaque bidding channel where consumers specify the price they are willing to pay. We illustrate the segmentation created by opaque pricing as well as compare optimal revenues and prices for sellers using regular full information channels with those using opaque selling mechanisms in conjunction with regular channels. We also study the segmentation and policy changes induced by capacity constraints

Online Low-Price Guarantees – A Real Options Analysis

A common practice among large retailers is the low-price guarantee, rebating consumers if they find an identical product cheaper elsewhere. This provides consumers with some level of comfort in their purchase decision. A similar low-price guarantee is provided by numerous service industries that allow reservation of capacity, yet do not penalize the consumer for failure to keep that reservation-examples include hotels and car rental. Given that a consumer is not required to keep the reservation, they may make another reservation, either at a competing firm or the same firm, if future prices decline. The increasing availability of pricing information on the Internet affords consumers the opportunity to be more strategic in their purchasing behavior. As consumers, we are able to quickly and easily check prices from numerous service or goods providers. The ease of price information potentially makes these guarantees very costly to the service or good provider. We analyze the implied costs associated with these guarantees by making analogies to financial options. Motivation for this research comes from a large car rental firm, Dollar Thrifty Automotive Group Inc., that considered offering a low-price guarantee to all consumers that book a reservation though their website

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

Multi-Click Attribution in Sponsored Search Advertising: An Empirical Study in Hospitality Industry

Sponsored search advertising has become a dominant form of advertising for many firms in the hospitality vertical, with Priceline and Expedia each spending in excess of US$2 billion in online advertising in 2015. Given the competition in online advertising, it has become essential for advertisers to know how effectively to allocate financial resources to keywords. Central to budget allocation for keywords is an attribution of revenue (from converted ads) to the keywords generating consumer interest. Conventional wisdom suggests several ways to attribute revenues in the sponsored search advertising domain (e.g., last-click, first & last-click, or evenly distributed approach). We develop a multi-click attribution methodology using a unique multi-advertiser data set, which includes full advertiser and consumer-level click and purchase information. We add to the literature by developing a two-stage multi-click attribution methodology with a specific focus on sponsored search advertising in the hospitality industry with which we develop a parametric approach to calculate the value function from each stage of the estimation process. Given our multi-advertiser data set, we are able to illustrate the inefficiency of single-click attribution approaches, which undervalue assist clicks while overvaluing converted clicks

All-Pay Auctions with Pre- and Post-Bidding Options

Motivated by the emergence of online penny or pay-to-bid auctions, in this study, we analyze the operational consequences of all-pay auctions competing with fixed list price stores. In all-pay auctions, bidders place bids, and highest bidder wins. Depending on the auction format, the winner pays either the amount of their bid or that of the second-highest bid. All losing bidders forfeit their bids, regardless of the auction format. Bidders may visit the store, both before and after bidding, and buy the item at the fixed list price. In a modified version, we consider a setting where bidders can use their sunk bid as a credit towards buying the item from the auctioneer at a fixed price (different from the list price). We characterize a symmetric equilibrium in the bidding/buying strategy and derive optimal list prices for both the seller and auctioneer to maximize expected revenue. We consider two situations: (1) one firm operating both channels (i.e. fixed list price store and all-pay auction), and (2) two competing firms, each operating one of the two channels

Demand Management: Beyond Revenue Management

[Excerpt] Think of yield or revenue management (RM) as the base of a funnel where the demand for services pours into the top is managed using price, terms and conditions, layovers, and length of stay controls to optimise revenue. That process can be carried out with a sophisticated RM system or in a more limited way with an intelligently developed and applied set of pricing and organisational practices. Demand management can be envisioned as both RM and keeping the funnel continuously and cost-effectively full of potential business. Formally, demand management has been defined by Larry Hall, President and CEO of Hotel Booking Solutions as, ‘the art and science of dynamically managing demand to optimise distribution and maximise customer relationships’. More generally, think of demand management as extending the tactical tools of RM to a more strategic level of not just managing demand but creating and directing demand. Used effectively, demand management has the opportunity to create value and avoid the increasing commoditisation of services