Room Rate Parity Analysis Across Different Hotel Distribution Channels in the U.S

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1 Journal of Hospitality Marketing & Management ISSN: (Print) (Online) Journal homepage: Room Rate Parity Analysis Across Different Hotel Distribution Channels in the U.S Tevfik Demirciftci, Cihan Cobanoglu, Srikanth Beldona & Pamela R. Cummings To cite this article: Tevfik Demirciftci, Cihan Cobanoglu, Srikanth Beldona & Pamela R. Cummings (2010) Room Rate Parity Analysis Across Different Hotel Distribution Channels in the U.S, Journal of Hospitality Marketing & Management, 19:4, , DOI: / To link to this article: Published online: 15 Apr Submit your article to this journal Article views: 377 View related articles Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at Download by: [University of South Florida] Date: 27 December 2015, At: 21:43

2 Journal of Hospitality Marketing & Management, 19: , 2010 Copyright Taylor & Francis Group, LLC ISSN: print/ online DOI: / Room Rate Parity Analysis Across Different Hotel Distribution Channels in the U.S. TEVFIK DEMIRCIFTCI Information Technology Department, Borgata Hotel, Casino, and Spa, Atlantic City, New Jersey, USA CIHAN COBANOGLU, SRIKANTH BELDONA, and PAMELA R. CUMMINGS Department of Hotel, Restaurant, and Institutional Management, University of Delaware, Newark, Delaware, USA In order to further enhance a relationship of trust between the hotel company and the guest, the guest must not feel that they are being cheated by paying more because they did not know where to get the best rates. At the same time that consumer savvy is growing, the complexity of purchasing choices is growing at an even faster rate. The purpose of this study was to examine actual rate parity of hotels across direct and indirect channels of distribution. The authors hope to help provide insight into the actual rate parity and to ultimately influence hotel guest perception of fairness in hotel room rate value and equity for what they are paying. Findings showed that there were no significant differences between rates from direct or indirect channels. However, significant differences were found in rates within both direct and indirect channels. Notable improvements in hotel rate parity from past studies were identified in this study. However, this study negates the claim of lowest rates guaranteed as propagated by several hotel chains, which they have stated in order to increase direct distribution through their own websites. KEYWORDS Direct distribution channel, indirect distribution channel, rate parity, online distribution Address correspondence to Cihan Cobanoglu, PhD, CHTP, Department of Hotel, Restaurant, and Institutional Management Technology, University of Delaware, 14 W. Main St., Raub Hall, Newark, DE 19716, USA. cihan@udel.edu 295

3 296 T. Demirciftci et al. INTRODUCTION Rate parity is said to exist when the same rate for a hotel exists across all of its distribution channels. The importance of rate parity across channels in a transparent marketplace such as the Internet is well documented (Kimes, 1994, 2002; Varini & Murphy, 2006). Not only does the lack of rate parity impact the customers perceived fairness of a firm s pricing practices, it can also have a detrimental impact on the brand s image (Choi & Kimes, 2005). The Internet has had a significant impact on the ability of hotels to sustain rate parity at large (Nyheim, McFadden, & Connolly, 2004). This variance of room rates is also known as price dispersion, wherein the spread or distribution of prices across sellers of the same item (in this case hotel rooms), is known to exist (Pan, Ratchford, & Shankar, 2003). Because hotel rates have become transparent, where customers can evaluate prices across numerous channels using sophisticated tools, the importance of rate parity has become all the more critical (Choi & Kimes, 2006). Hotels should monitor their pricing practices consistently on the Internet since online purchasers do not want to be offered different prices for the same products, such as the same hotel rooms on various Internet sites (O Connor, 2003). Rate parity should prevail across both direct and indirect channels. A 2005 study found a significant degree of disparity in rates across channels (Thompson, 2005). While the study provided significant insights into price dispersion in the lodging industry, its findings were limited to data collected over only one data point and one reservation rate for only one reservation date, which looked exclusively at direct channels of distribution. When potential guests called the number, they were given different rates for the same dates at the same hotel than if they called the same hotel directly. Since the hotel had directed their calls to the line after a certain time of day, or when they were especially busy, prices varied depending on when the potential guest called. If people from the same company, association or family were calling the same hotel, the rates quoted were different. The potential guests were frustrated and felt cheated by a system they could not control. From this example some, but not enough, information can be gathered. Additional information could come from a study of indirect distribution channels, as well as direct distribution channels. Have hotels achieved rate parity across indirect distribution channels? Are hotel room rates consistent between the direct distribution channels and indirect distribution channels? The purpose of this study is to examine rate parity in four and five diamond hotels in the United States. Specifically, the primary objectives are to evaluate rate parity (a) within direct channels: direct call to the hotel, hotel central reservation system (1-800 phone), and hotel website, (b) within indirect channels: Expedia.com, Orbitz.com and Travelocity.com, Worldres.com, Travelweb.com, and (c) across direct and indirect channels. We also examine rate parity for each property at four time-based fences: 21 days before

4 Room Rate Parity Analysis in the U.S. 297 stay, 14 days before stay, 7 days prior; and, on the day of stay). In addition, this study uses five indirect and three direct channels to provide more generalizability to the findings. Theoretical and practical implications are discussed. ONLINE DISTRIBUTION OF HOTEL ROOMS Prior to the emergence of the Internet, the primary channels of distribution for hotels were global distribution systems and central reservation systems (Connolly, 1999; Connolly, 2000; O Connor & Frew, 2002). The emergence of the Internet as a viable mode for conducting commercial transactions for hotels was evident in the late 1990s, when nearly 39% of hotel companies reported that they provide real time reservations over the Internet from their websites (Cline & Warner, 1999). This number quickly jumped to 64% in 2001 (Cline & Warner, 2001). Intermediaries such as online travel agencies (Travelocity, Expedia, etc.) had already become established brand identities in the marketplace. The emergence of online travel intermediaries, however, created significant challenges for the control of distribution of hotel rooms (Carroll & Siguaw, 2003; Brewer & Kang, 2004; Dabas & Manaktola, 2007). This was compounded by the depressive market conditions prevailing after the 9/11 attacks, a time when it was increasingly challenging for hotels to fill hotel rooms. Online intermediaries were one way to help sell rooms. Price became the key differentiator of competition between hotels and their online indirect channels (Carroll & Siguaw, 2003). Control over the distribution of inventory had significant implications on the consistency of rates across direct and indirect channels. Online travel intermediaries had already developed significant intermediation capabilities combined with aggressive branding campaigns that skewed the balance of negotiation contracts in their favor. Price Dispersion on the Internet Price dispersion on the Internet has typically been higher when compared to traditional offline channels (Bailey, 1998; Clemons, Hann, & Hitt 1998; Brynjolfsson & Smith, 2000; Koch & Cebula, 2002). Sellers have reported finding it challenging to generate higher profits on the Internet because of the transparency of the medium (Sinha, 2000). However, data mining is a form of information gathering, storage and usage that enables specific customer package and price differentials, which add to price dispersion. Firms benefit from data mining applications and the cost of updating prices is very inexpensive (Koch & Cebula, 2002). For instance, one study indicated that airline ticket prices fluctuated by 18% across online travel agents (Koch & Cebula, 2002).

5 298 T. Demirciftci et al. Finally, many hotel and airlines utilize the Internet in order to sell their unsold inventory as last minute deals. These packages are preferred by the customer because of their lower prices. This situation helps hoteliers sell their unsold inventory, preventing them from losing potential revenue. Price is also considered important by travelers during economic downturns. However, this situation conditions the expectations of customers, which forces the suppliers to offer lower rates as last minute deals from their inventory (O Connor, 2006). For hotels who utilize last minute pricing, the demand boost will not be large, but the loss of revenue will be painful (Enz, 2003, p. 5). Implications of Price Dispersion The most significant implication that emerges from price dispersion is the perception of price fairness (Choi & Kimes, 2005). Yelkur and DelCosta (2001) found that inconsistent rates among various distribution channels further create consumer perceptions of unfair pricing. Hotels should give more information in order to avoid this situation and consider adding extra services in order to prevent the perception of unfair pricing strategies (Kimes, 2002). Hotels should include among its methods of reserving and selling hotel rooms its own well-developed, attractive, and simple-to-use Internet site. The Internet, as a distribution channel, significantly diminishes search costs because of its rich interactivity, reach, and search efficiency (Jiang, 2002). Searching online is less costly, more convenient and quicker than the traditional buying process (Kung, Monroe, & Cox 2002). Economics of information theory requires that consumers acquire information till the point where the marginal costs of acquiring additional information equals or exceeds the marginal benefit (Biswas, 2004 p.725). A price search consists of two main aspects; the person s ability and his or her underlying motivation (Bettman & Park, 1980). Online consumers may not prefer to spend so much time if they are not saving money (Koch & Cebula, 2002). Eight billion pages were indexed by Google in June 2005 making the Internet search process more difficult (Murphy, Schegg, & Qiu, 2006). This problem can be solved through the use of meta-search engines or shop bots. Shop-bots enable consumers to find price and product information from various competing suppliers (Kung et al., 2002). Shop-bots or metasearch engines decrease buyer search costs at least 30-fold (Brynjolfsson & Smith, 2000). They decrease the asymmetry of information between suppliers and consumers (Kung et al. 2002). For instance, Kayak.com facilitates the search process across a number of sites at the same time, which helps the consumer save time (Murphy et al., 2006). Savings are seen as the most essential motivator for a traveler to buy on line. For instance, a research project carried out by PhoCusWright (2007) determined that competitive pricing was the foremost motivator to attract

6 Room Rate Parity Analysis in the U.S. 299 customers. Additionally, a customer group that did not buy online was asked what would motivate them to buy an online product. Sixty-four percent of this customer group answered that saving money would attract them to buy online (O Connor, 2002). Another implication of price dispersion is the commoditization of the hotel product at large (Carroll & Siguaw, 2003). Commoditization is said to prevail when the product is largely differentiated by price and not by its features and attributes. Put differently, consumers who view hotel rooms as homogeneous within given segments, with rooms of one brand being no different from rooms from another brand, then one may as well purchase the less expensive brand (Carvell & Quan, 2005). One phenomenon that feeds this perception is that hotels of one brand are often acquired and reflagged by another brand and changed very little. A customer may have stayed in a certain room as a Westin one month, and a month later as a Marriott and they remained very similar. Commoditization of a product has significant branding implications for lodging firms. In an industry where franchising is the dominant business model, the need to sustain, if not enhance, brand equity is paramount for firms to expand and succeed. Commoditization impedes that very initiative because a customer s choice of a product that is driven more by price differentials and not by attributes will diminish the impact of branding initiatives. Hypotheses In summary, hotels have traditionally struggled to sustain rate parity on the Internet. Arguably, their control over rates across indirect channels is going to be less than when compared across their direct channels. Following are the hypotheses outlined for the study: H1: The room rates on the direct distribution channels of hotels are not equal to or lower than room rates on indirect distribution channels. H2: The room rates on the direct distribution channels of hotels are different from each other. H3: The room rates on the indirect distribution channel of hotels are different from each other. METHODOLOGY The top 10 cities in the United States were selected based on occupancy rates as indicated by the Smith Travel Research (STR) Trends Report (Smith Travel Research Report, 2006). The STR Standard Trend Report displays yearly performance data including occupancy, average daily rate, revenue

7 300 T. Demirciftci et al. per available room, supply, demand, and revenue. These cities in the 2006 STR Standard Trend Report were Oahu Island; New York; Los Angeles; Miami; San Diego; Orlando; Phoenix; Washington, DC; San Francisco; and Philadelphia. Oahu Island, a city in Hawaii, was removed since the majority of the hotels in Hawaii are resorts and these hotels distribute their rooms as part of packages. Therefore, instead of Oahu Island, Atlanta was added to our study, which was 11th in the list. Ten properties were randomly selected for each city. These properties were selected according to the American Automobile Association (AAA) diamond rating process since AAA s diamond process is considered the most reliable rating process for North American travelers (Wilkening, 2007). Ten hotels were selected for each city based on random numbers from the AAA list of four and five-diamond hotels. Previous literature has shown that many economy and midscale hotels utilize single-based pricing according to season or day of the week (O Connor, 2006). Therefore, our study included four and five-diamond hotels, which offer various pricing options for their guests. These four and five-diamond hotels can be considered as luxury hotels in the U.S. In total, 800 data points were collected by the researchers. Data were collected from both direct and indirect distribution channels. Direct distribution channels included hotel direct call, central reservation systems (CRS), and hotel website. Indirect distribution channels were Wordres. com, Travelweb.com, Orbitz.com, Expedia.com and Travelocity.com. Travelweb was chosen since it utilized the databases and reservation engine of switch companies (O Connor, 2003). Worldres.com was chosen as an indirect distribution alternative as it is considered the largest pure web based channel with inventory and reservations maintained online (O Connor, 2003). Orbitz.com, Expedia.com, and Travelocity.com were selected according to the Hitwise travel category report. These three merchant-based sites were inside the top 20 sites in the travel online industry for the month of October 2006 based on the visits. Furthermore, data was collected during the month of January Data were collected by researchers, and trained assistants. Data were analyzed using published room rates for a double standard room for four different dates. These are also known as rate fences, and are typically cutoff dates, scenarios or conditions based on seasonality and other factors that suppliers of perishable inventory, such as hotel rooms, leverage to control the flow of demand (Kimes & Wirtz, 2003; Beldona & Kwansa, 2008). The reservation date was determined to be January 31, This date was selected because surrounding this date, there were no major events in these 10 cities. The first data collection was made on January 10th, which was 21 days before the check in date (January 31st). The second data collection was made on January 17th, which was 14 days before the check-in date. The third data collection was made on January 24th, which was seven days before the check-in date. The last data collection was made on January 31st,

8 Room Rate Parity Analysis in the U.S. 301 the check-in day. Furthermore, the lowest available rate was recorded by the researchers excluding the discounted rates such as corporate, military and AAA or AARPs memberships. Reservations were never purchased by the researchers. In addition to this, two researchers called both the CRS and hotels directly. RESULTS AND DISCUSSIONS Profile of Properties Table 1 shows the profile of the hotels examined. Each city was represented by 10 hotels in this study. Seventy-three percent of the hotels had four diamonds while 27% of the hotels had five diamonds. Moreover, 50% of the hotels in this study were downtown hotels and 44% were located in suburban areas. The remaining 6% were airport hotels. Ninety-seven percent of the hotels were franchised hotels. Only 3% of these hotels were independent hotels. In Philadelphia, 9 out of 10 hotels were listed as downtown hotels. Only one hotel was indicated as an airport hotel. In Atlanta, 50% of the hotels; in Los Angeles and Miami, 60% of the hotels; in San Diego, 80% of the hotels and in Orlando, 90% of the hotels were located in suburban areas. All of the hotels in Washington DC (100%) were downtown. Hypotheses Testing At the outset, H1 stated that room rates for hotels in direct distribution channels be less than on indirect distribution channels. To test this, a paired t-test was conducted on the rates of both direct and indirect distribution channels across all four fences, namely 21 days, 14 days, 7 days, and the day of stay. As seen in Table 2, there were no significant differences between means of direct and indirect distribution channels across all four fences. An additional paired t-test was conducted between average rates for all days and the result was not significant. Therefore, H1 was not supported. Hotels have maintained rate parity between themselves and indirect channels when viewed in the aggregate. Alternately, one can also say that the lack of any differences indicates that hotels have not provided lower rates at their end to counter third party efforts at increasing control over the distribution of hotel inventory. H2 posited that room rates on the direct distribution channels of hotels will be different from each other. To test this assumption, paired t-tests were conducted to evaluate differences between the three direct channels namely CRS, hotel direct call, and the hotel website across all four fences. Table 3 outlines the mean differences of rates found on hotel websites and the CRSs. With the exception of the day of check-in, there were significant

9 TABLE 1 Breakdown of Hotel Properties Diamond Location Type 5 4 Downtown Suburban Airport Franchise Independent Cities F % F % F % F % F % F % F % Atlanta Los Angeles Miami New York Orlando Philadelphia Phoenix San Diego San Francisco Washington, DC Total Note. F = frequency. 302

10 Room Rate Parity Analysis in the U.S. 303 TABLE 2 Paired t-test for Direct Channel and Indirect Channel Direct channel Indirect channel Difference M SD M SD M SD t df Sig. (2-tailed) 21-day advance day advance day advance Check-in All dates (average) Note. = Significant at α =.05 level. TABLE 3 Paired t-test for Hotel Direct Website and CRS Hotel direct website ($) CRS ($) Difference ($) M SD M SD M SD t df Sig. (2-tailed) 21-day advance day advance day advance Check-in day Note. = Significant at α =.05 level. differences between rates on the hotel website and CRS across the remaining three fences. CRS rates were significantly higher at $ as opposed to $ on the hotel direct websites. Table 4 highlights mean differences in rates between hotel direct website and hotel direct call. Here, too, with the exception of the day of check-in, the rates on the hotel website were significantly lower than hotel direct call across the 21, 14, and 7-day advance fences. Lastly, mean differences were analyzed for the last combination of direct channels: hotel direct call versus CRS. In this case, with the exception TABLE 4 Paired t-test for Hotel Direct Website and Hotel Direct Call Hotel website Hotel direct call Difference M SD M SD M SD t df Sig. (2-tailed) 21-day advance day advance day advance Check in day Note. = Significant at α=.05 level.

11 304 T. Demirciftci et al. TABLE 5 Paired t-test for Hotel Direct Call and CRS Hotel direct call CRS Difference M SD M SD M SD t df Sig. (2 tailed) 21-day advance day advance day advance Check in day Note. = Significant at α =.05 level. of the 14-day advance fence, there were no significant differences across the remaining three fences. In this case, the mean rate on the CRS for the 14-day advance was significantly higher when compared with hotel direct call channel. H3 proposed that room rates across indirect distribution channels will not be consistent. Table 6 highlights mean differences between indirect channels using paired t-tests. Given space constraints, only those comparisons that had significant differences are reported. Note that there were six distribution channels and 15 two-way comparisons across four fences making up for 60 comparisons overall. Across these 60 comparisons of differences across indirect channels, only 7 comparisons reported statistically significant differences. This provides marginal support for H3. Most notable was that Orbitz was present in 5 of these 7 significant differences. Here, Orbitz reported significantly higher rates compared to Worldres in the 21, 14, and 7-day advance fences and over Travelocity in the 21 and 14 day fences. The additional 2 differences were between Worldres on one hand TABLE 6 Paired t-test for Indirect Distribution Channels Showing Significance No. Pairs M ($) SD ($) t df Sig. (2 tailed) 1 Orbitz (21-day advance) Travelocity (21-day advance) Orbitz (21-day advance) Worldres (21-day advance) Orbitz (14-day advance) Travelocity (14-day advance) Orbitz (14-day advance) Wordres (14-day advance) Expedia (7-day advance) Wordres (7-day advance) Travelocity (7-day advance) Wordres (7-day advance) Orbitz (7-day advance) Wordres (7-day advance) Note. = Significant at α =.05 level.

12 Room Rate Parity Analysis in the U.S. 305 and Travelocity and Expedia on the other hand across the 7-day fence. This was another highlight of this combination of comparisons, wherein Worldres reported significantly lower rates when compared with the three major OTAs namely, Travelocity, Expedia, and Orbitz. CONCLUSION AND IMPLICATIONS In summary, the findings indicate that rate parity exists when viewed across direct and indirect channels at an aggregate level. However, this parity is significantly absent when viewed within individual channels in direct channels and to a marginal extent in indirect contexts. The lack of parity across indirect channels can to some extent be attributed to the lack of control over external environmental variables such as competition and mark-ups. Arguably, this variability is to some extent a reality of the marketplace. However, the lack of significant differences between direct channels at the aggregate level indicates that hotels are not leveraging the power of direct distribution through relatively lower rates at direct points of contact with the customer. Put differently, this negates the larger philosophy of lowest rates guaranteed as claimed by several hotel chains to increase direct distribution through the website. In addition to the immediate reasons above are inconsistencies in rates between direct channels. In this situation, however, hotels have lower rates on the website compared to other points of direct contact. This is possibly directly linked to costs since the direct web has the lowest costs of distribution compared to other direct channels. The following recommendations stem as a result of the findings of the study. First, if a hotel claims to offer the lowest rate guaranteed, then it should control the distribution channels so that this is achieved or lower their rates, their true control area. However, the authors believe that setting prices consistently on distribution channels will decrease the search motivation of online consumers, thereby increasing the trust towards brands and their pricing strategy. Anecdotally, consumers are often looking for a backdoor to hotel rates. To prevent this from happening, hotels should invest in managing their distribution channels more effectively so that consumers would be confident that the rate that they find at hotel s direct distribution channel is the lowest one. Secondly, hoteliers should invest in single image inventory. Single image inventory enables all distribution channels to get the rates from a central location, the hotel s property reservation system, which gives the hotel management full control of the inventory, rates, and yield management tools. Though easy to talk about, single image inventory is not easy to implement. For this to happen, hotel systems should interface with central and global

13 306 T. Demirciftci et al. reservation systems. This could be impossible in some cases, where the interface to a central reservation system is currently not possible due to coding of the software, or not practical, as it is too expensive to interface. In addition to proprietary interfaces, switch companies may be used where all of the interfaces to online travel agencies are handled by the switch company such as Pegasus Solutions. Yet, single image inventory is the most effective way of controlling inventory, implementing yield management tools (i.e. increase or decrease rates based on demand, close/open distribution channels as the booking date gets closer), and owning the complete guest data. Finally, the website of a hotel should be designed to be attractive, userfriendly, and search engine-friendly. Starkov and Price (2006) stated that hotels should apply destination-focused search engine strategies in order to motivate consumers to buy from the hotel website directly. Hoteliers should also utilize pay-per-click advertising, direct listings, and pop-up advertising and affiliate programs. These strategies will enable hotels to generate higher revenues. In summary, this article addresses several issues using a methodology that addresses all the key channels of distribution. A limitation of this study was the potential for Type I error. Type I error is typical wherein a multitude of tests of differences are done individually in a study rather than modeling them together. In this context, the number of t-tests conducted raises the potential for Type I error in the results. REFERENCES Bailey, J. P. (1998). Intermediation and electronic markets: Aggregation & pricing in Internet commerce (Unpublished doctoral dissertation). MIT Sloan School of Management, Cambridge, MA. Beldona, S., & Kwansa, F. (1998). The impact of cultural orientation on perceived fairness of demand based pricing. International Journal of Hospitality Management, 27(4), Bettman, J. R., & Park, C. W. (1980). Effects of prior knowledge and experience and phase of the choice process on consumer decision processes: A protocol analysis. The Journal of Consumer Research, 7, Biswas, D. (2004). Economics of information in the web economy towards a new theory? Journal of Business Research, 57, Brewer, K. P., & Kang, B. (2004). Managing electronic distribution channels in hotel booking: Issues in a changing environment (Working Paper No ). Cornell University Working Paper Series. Ithaca, NY: Cornell University. Brynjolfsson, E., & Smith, M. D. (2000). Frictionless commerce? A comparison on Internet and conventional retailers. Management Science, 46, Carroll, B., & Siguaw, J. (2003). The evolution of electronic distribution: Effects on hotels and intermediaries. Cornell Hotel and Restaurant Administration Quarterly, 44(4),

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