Running Head Title: Visibility of DMO Websites in Google

Assessing the Visibility of Destination Marketing Organizations in Google: A Case Study of Convention and Visitors Bureau Websites in the United States Running Head Title: Visibility of DMO Websites in Google Zheng Xiang* School of Merchandising and Hospitality Management University of North Texas Denton, TX 76203-5017, USA Telephone: 1-940-369-7680 Fax: 1-940-565-4348 Email: philxz@unt.edu Bing Pan Department of Hospitality and Tourism Management School of Business and Economics College of Charleston, Charleston, SC 29424-001, USA Telephone: 1-843-953-2025 Fax: 1-843-953-5697 E-mail: bingpan@gmail.com Rob Law School of Hotel and Tourism Management Hong Kong Polytechnic University, Kowloon, Hong Kong Telephone: 852-2766-6349 Fax: 852-2362-9362 Email: hmroblaw@polyu.edu.hk Daniel R. Fesenmaier National Laboratory for Tourism & ecommerce School of Tourism and Hospitality Management Temple University, Philadelphia, PA 19122, USA Fellow, International Academy for the Study of Tourism Visiting Fellow, Inst. for Innovation in Business and Social Research (IIBSoR) University of Wollongong, Australia Telephone: 1-215-204-5612 Fax: 1-215-204-8705 Email: drfez@temple.edu Submitted for consideration for publication in the Journal of Travel and Tourism Marketing *: correspondence author

Assessing the Visibility of Destination Marketing Organizations in Google: A Case Study of Convention and Visitors Bureau Websites in the United States ABSTRACT Search engines are playing an increasingly dominant role in providing access to tourism information on the Internet. As such, it is argued that destination marketing organizations (DMOs) must have a substantial understanding of the visibility in search engines in order to create competitive positions within this important marketplace. The goal of this study was to develop a process to assess the visibility of DMO websites in one of the major search engines (i.e., Google). A set of 18 cities in the United States were selected to be used as case studies of the visibility of their convention and visitors bureaus (CVBs) websites in relation to travel queries identified using Google Adwords Keyword Tool. The results indicate that there are substantial differences in the relative positions of CVB websites on Google. In particular, there seems to be huge gaps among the search domains wherein CVB websites in terms of their visibility to online travelers and volume of search within those domains. This study offers a number of implications for research and practice of search engine marketing for tourism destinations. Keywords: Search engine marketing; destination marketing; competitive analysis, internet. 1

Assessing the Visibility of Destination Marketing Organizations in Google: A Case Study of Convention and Visitors Bureau Websites in the United States INTRODUCTION Search engines have become a dominant tool for accessing travel products on the Internet in that they play a central role in bridging the supply and demand of tourism by enabling travelers to access enormous amount of information online and, as a result, generating upstream traffic and direct bookings for many tourism and hospitality websites (emarketer, 2008; Hopkins, 2008; Prescott, 2006; TIA, 2005, 2008). As such, search engines have become one of the most important strategic tools for destinations and businesses to compete for consumers attention on the Internet and to engage in direct conversations with their potential customers (Google, 2006; Moran & Hunt, 2005; Wang & Fesenmaier, 2006). It is generally understood that search engines like Google and Yahoo! have inherently built-in limitations in representing a large information domain (Henzinger, 2007). Search results are usually represented in the form of rank ordered information snippets on the search engine results pages (SERPs), which provides a powerful structure that determines, to a large degree, what is presented and therefore, what is seen by users. Also, a series of studies within travel and tourism by Wöber (2006), Pan et al. (2007), Xiang, Wöber and Fesenmaier (2008), and Xiang, Gretzel and Fesenmaier (2009) indicate that search engines do not represent the domain of tourism as desired by the suppliers. However, this information can be used by destination marketing organizations (DMOs) to gain a competitive position in search engines. Thus from a marketing viewpoint, it is extremely important to understand the extent to which tourism websites are visible to travelers when they are looking for travel related information. Given the role of search engines in destination marketing, the goal of this study was to to 2

assess the visibility of DMO websites in search engines in order to understand the current competitive positions of these organizations. Specifically, this study employed a method that extracts information describing the visibility of a sample of American convention and visitor bureau (CVB) websites in one of the major search engines, i.e., Google. This paper is organized into five sections. Followed by the Introduction, the Research Background section reviews relevant literature and provides the rationale for the present study. The Research Methods section explains the design of the research process and addresses the validity of the methodology. The Findings section provides the description and summary of study results. Then, the Discussion section summarizes this paper and discusses the implications for both theory development and managerial practices as well as the limitations of the study and directions for future research. SEARCH ENGINES AND WEBSITE VISIBILITY In order to successfully promote their products to potential visitors, tourism destinations must make sure relevant information is made visible and accessible (Buhalis, 2000; Connolly, Olsen, & Moore, 1998; O'Connor, 1999; O'Connor & Frew, 2002; Werthner & Klein, 1999). On the Internet, tourism organizations employ a variety of techniques and tools to communicate and engage online travelers (Buhalis & Law, 2008; Buhalis & Licata, 2002; Wang & Fesenmaier, 2006). As search engines are playing an increasingly important role in bridging the traveler and the tourism domain online, it is imperative that tourism organizations understand the way search engines influence travelers when searching for tourism information in order to develop effective online marketing strategies. This section briefly reviews the literature on the role of search engines in online travel information search as well as on website visibility as a key indicator of website performance in search engines in order to establish the rationale for the present study. Metaphorically, search engines can be thought of as the Hubble Telescope in that they 3

enable travelers to gain access to billions of web pages that comprise the online tourism domain (Xiang, Gretzel et al., 2009). Search engines generally consist of two main components that support this function. First, it has an offline component that collects hyper-textual documents on the Internet and builds an internal representative image (index) of these documents. Second, it has an online component that allows users to search, order, and classify documents in order to select the most relevant search results (Henzinger, 2007; Marchionini, 1997). The offline component usually includes a crawler and an indexer. A crawler is a computer program that follows the links on the web the same way a user clicks from one page to another, but then downloads the web pages to a server. An indexer uses the documents retrieved by the crawler to build searchable indices. The online component is a user interface that: 1) allows users to enter queries; 2) based upon the queries, retrieves relevant documents found in the searchable indexes created by the indexer; 3) generates informational snippets consisting of the web address, a short description, and other metadata; and, 4) displays the snippets in the form of a rank ordered list on the search engine result page (SERP). The main part of a SERP is used to display those results based on the internal ranking algorithms, which is called Organic Listings. In addition, major search engines such as Google and Yahoo! display paid advertisements on the top and right side of major result pages based on businesses willingness to pay; these ads are referred to as Paid Listings. The use of search engines to access a repository of information has been well documented in fields such as information science, information retrieval, computer science, as well as humancomputer interaction. In general, the process of using a search engine can be understood as consisting of three steps (Henzinger, 2007; Kim & Fesenmaier, 2008; Marchionini, 1997). First, the user enters a query into the interface. Research has shown that three factors largely determine 4

query formulation and include the user s understanding of how search engines work, his/her knowledge of the domain, as well as the search task itself. Second, based upon the query, the search engine retrieves and returns a number of search results that match the search query and displays them in a pre-defined format. Lastly, the subsequent interaction with a search engine involves the user s reading and understanding of the search results and then navigating back and forth between the result page and the following websites originated from those results. This implies, then, that the user makes a series of decisions based on the relevance of search results in relation to the information-seeking task at hand. Travel information search plays an important role in a traveler s trip planning and decision making process (Fodness & Murray, 1998; Gursoy & McLeary, 2004; Vogt & Fesenmaier, 1998). Recently, due to the growing amount of information on the Internet, search engines are becoming increasingly important in facilitating travelers access to the tourismrelated information online (Fesenmaier, Xiang, Pan, & Law, 2010; TIA, 2008). Recent research indicates that the order of search results strongly influences the traveler s evaluation and selection of search results (Henzinger, 2007; Moran & Hunt, 2005; Pan et al., 2007; Spink & Jansen, 2004). In particular, these studies indicate that the majority of search engine users do not look beyond the first three pages of search results (Pan et al., 2007) and that the top three search results have the highest impact on users perception of the relevance of search results. As such, the visibility of a website, which is directly related to its ranking on a SERP, can be measured based upon a website s position on a search engine such as Google (Enquiro, 2006). Further, these indicate organic search result listings should be used to measure visibility as search engine users tend to consider organic listings more trustworthy than paid listings (Jansen & Spink, 2003; Zhang & Dimitroff, 2005b). In addition, paid listings are dynamic and are usually generated real 5

time and hard to capture. Study Rationale and Research Questions Search engine marketing (SEM) is a form of Internet marketing that seeks to promote websites by improving their ranking and, consequently, visibility in SERPs (Moran & Hunt, 2005). In fact, search engine marketing encompasses a number of techniques or strategies to improve and enhance a website s visibility in SERPs (Moran & Hunt, 2005; Thurow, 2003; Zhang & Dimitroff, 2005b). First, search engine optimization involves utilizing a number of techniques that improve the ranking of a website when a user types in relevant keywords in a search engine. These techniques include creating an efficient website structure, providing appropriate web content, and managing inbound and outbound links to other sites. Second, paid inclusion involves paying search engine companies for inclusion of the site in their organic listings. Third, search engine advertising, or paid placement, refers to buying display positions at the paid listing area of a search engine or on a third party website which the search engine uses as a partner in advertising. Google AdWords or Yahoo! Precision Match are the two most popular programs whereby paid placement listings are shown as sponsored links. Fourth, directory listing refers to the submission of the website to a directory-based search engine (e.g., Yahoo! Directory) to be shown under its subject category list. DMOs play a central role in providing information to travelers about a destination and thus serve to bridge between the supply and demand of tourism (Buhalis, 2000; Gretzel, Fesenmaier, Formica, & O'Leary, 2006; Kotler, Bowen, & Mackens, 2009; Yuan, Gretzel, & Fesenmaier, 2003). With the growing importance of the Internet as a marketing and advertising channel, DMOs are adopting a variety of online tools including search engine marketing and 6

optimization to reach, engage, and persuade their potential visitors (Wang & Fesenmaier, 2006). As DMOs are searching for new tools to gauge success, it is important to develop useful approaches to measure the effectiveness of their online marketing programs (Buhalis & Law, 2008; Gretzel et al., 2006). Importantly, studies have shown that the visibility of many tourism business websites is diminishing. Recently, for example, Wöber (2006) found that many tourism businesses were ranked very low among the search results for travel related queries. This makes it extremely difficult for users to directly access the individual tourism businesses and properties through search engines. In another study conducted by Xiang et al. (2008) on the online tourism domain, the visibility of tourism businesses reflects the power structure created by search engines in that a handful of big players dominate search results in Google, leading to the diminishing visibility of numerous small and medium-sized tourism enterprises. The competitiveness in search engines representation of tourism has been further escalated by the emergence and exponential growth of the so-called social media or consumer generated content on the Internet. For example, in a recent study conducted by Xiang and Gretzel (2010) many of the social media sites such as tripadvisor.com, virtualtourist.com, and igougo.com were ranked at prominent positions on Google s search results pages. Potentially, travelers could have first visited these websites, which largely reflect online consumers impressions and opinions based upon individual or personal experiences, before they actually visit the DMO s website. This could create challenging problems for DMOs because consumers may already have their predispositions and attitudes toward the DMO website before the actual visit. Given the dynamic nature of the information space on the Internet, it is critical for tourism marketers to constantly monitor these changes in order to develop effective strategies so as to improve their visibility in search engines (Pan, Xiang, Fesenmaier, & Law, in print). 7

While past research, particularly Wöber (2006) and Xiang et al. (2008), indicates that many tourism businesses exhibit low visibility in search engines, there are a number of important limitations in these studies. First, these studies have not considered the visibility of destination marketing organizations (TIA, 2008). Second, there is a lack of in-depth, contextual analysis of the visibility issue related to tourism. For example, studies have not been conducted which assess the visibility of DMOs in relation to travelers information needs. And third, these studies have been based upon search queries that were collected from relatively old search engines or are artificially constructed; thus, these studies may not truly reflect real and current dynamics of search engines. In order to address these limitations, this study focused on the visibility of DMO websites in search engines by utilizing information tracking real travel queries. Specifically, the following research questions were used to guide this study: 1. Overall, to what extent are DMOs visible in Google? 2. How do DMOs compare to each other in terms of search engine visibility? 3. To what extent are DMOs visible in relation to travelers search queries? RESEARCH METHODS The goal of this study was to examine the visibility of DMO websites in search engines within the context of travel planning. The idea was to analyze search results retrieved from a search engine based upon current travel queries to simulate travelers use of search engines for travel planning. A number of considerations were given in order to establish the validity of the method. First, different from previous studies that focused on travelers use of search engines, this study utilized the most up-to-date travel related queries collected from one major search engines. Second, since convention and visitors bureaus (CVBs) play a central role in destination marketing in the United States, websites of CVBs in 18 cities in the U.S. were used as the focal 8

websites for this study. As can be seen in Table 1, these 18 cities were selected from three tiers of cities in the United States based upon their 2002 U.S. Census populations, with six small cities, six medium-sized cities, and six large cities representing tourist destinations throughout the country; in addition, cities within the three tiers were picked from different census regions including the Northeast, South, Midwest, and West. While this is a relatively small sample of all possible cities in the United States, the rationale for this selection was to have a representation that to a certain degree reflects the geographic and demographic diversity of American cities and allows the researchers to examine commonalities and potential nuances in travel queries. Once these cities were identified, the URLs (Web addresses) of the CVBs in these cities were obtained. Third, following Xiang et al. (2008), Google was chosen as the focal search engine because of its dominance in the American search market. Insert Table 1 about here Similar to Xiang et al. (2008), this study involved using travel-related terms to query Google and then a series of analyses were conducted to describe and compare the visibility of CVB websites based upon the search results retrieved using these queries. Specifically, the research design consisted of three steps: 1) identifying travel related search queries; 2) mining search results retrieved from Google based on these queries; and, 3) describing and comparing the visibility of these CVB websites. In Step 1, the Google AdWords Keyword Tool (https://adwords.google.com) was used as the sampling frame to identify search queries. This tool is provided by Google for marketers to view the volumes and competitiveness of certain queries and thus allows them to select keywords for their search engine marketing campaigns. For each destination the city name (e.g., New York City ) was manually typed into Adwords and all the queries (150 for most cases) suggested by Google, along with their average monthly 9

volumes in the past 12 months were extracted, resulting in 2,678 queries for all 18 destinations. The collection of these query terms were conducted and completed in February 2009. In Step 2, a Web crawler program written in Perl programming language was used to simulate the use of Google by search engine users by applying the queries obtained from Step 1 for each of the 18 destinations. Web addresses (URLs) of organic search results on the first three pages were extracted. Then, a pre-compiled list of the Web addresses of CVBs in these destinations was used to identify the occurrences of these websites displayed as part of Google results, along with the query term, search results page number (1, 2, or 3) and ranking (from 1 to 10) within a specific page. The collection of Google search results was also completed in February 2009. In Step 3, a series of analyses were conducted. First, a content analysis was conducted on queries extracted from Google Adwords Keywords Tool to provide a basic understanding of queries people use to search for information about a specific city. This was accomplished by manually coding each query into two broad categories, i.e., travel related queries and nontravel related queries (including those with high uncertainty). Although these categories may intuitively make sense, coding was not necessarily an easy task. For example, a query such as cheap new york hotels is very likely to be travel related. However, it was difficult to decide if queries such as new york midtown are travel related; queries such as new york law, on the other hand, were determined not to be ravel related. Further, among all travel related or potentially travel related queries, each query was coded into more specific categories such as accommodation, attraction, and travel info. These categories were intended to serve as the basis for comparing destinations. For example, a query for New York City on the Statue of Liberty and another one for Chicago on the Navy Pier were both considered queries about 10

tourist attractions and thus would be coded under the attraction category. Two human coders were recruited to conduct the coding task; however, the researchers finally reviewed all the codes to make certain they were consistent; otherwise, a decision was made by the authors to select the result that was deemed a better fit. An analysis was conducted which compared the visibility of CVB sites among destinations. Specifically, this analysis examined the occurrences of CVB websites among all search results between these destinations; in addition, an analysis was conducted with the focus on website visibility in relation to the volume of search queries in Google. A weighted score was calculated for each CVB website by summing all occurrences within each category of search queries identified in Step 1 multiplied by the search volume for that specific category. In addition, the aggregated occurrences of CVBs websites in relation to certain search categories were plotted to identify potential gaps existing between the CVBs the consumers. FINDINGS The results of the study are presented in two sections. First, the results based upon an analysis of query terms extracted from Google Adwords Keywords Tool are presented to provide a basic understanding of the most up-to-date queries about cities in the United States. Second, the results based upon analyses of the visibility of CVB websites of the 18 American destinations are presented, including the occurrences in Google SERPs, total impressions generated, and their visibility in relation to user queries. Queries on U.S. Cities in Google Table 2 lists the 18 cities with their monthly search volumes and results from the content analysis of these queries rank ordered by the monthly search volume. As can be seen, volumes of 11

search queries for these 18 cities extracted from Google Adwords Keyword Tool were huge. Tourist destinations (e.g., Las Vegas and Orlando) and large cities (Chicago and Dallas) lead this list in terms of total number of queries generated over a period of one month. The monthly average total of queries for all 18 cities was approximately three hundred and forty million queries (N=342,661,918). On average, each destination generated 19,036,773 queries per month, ranging from 218,750 (Americus, GA) to 72,599,890 (Las Vegas, NV). This indicates the huge differences in their status as a domain of interest on the Internet among these cities. The average monthly volume of the least frequently used query for all 18 cities were in the hundreds (N=410), indicating the list of the top 150 queries related to the city names is quite likely a comprehensive representation of all possible queries about a specific city and, thus, provides a good basis for understanding the search domain. Insert Table 2 about here On average, about 20 percent of all queries were identified as related to travel in terms of search volume. This indicates that travel is one of the major categories of search on the Internet. Among the 18 destinations, the cities of Las Vegas, Orlando, New York City, and Myrtle Beach have higher percentages of travel related queries, indicating these cities are more touristic than others. This is consistent with an earlier study conducted by Xiang and Pan (2009) which was based upon search queries from a number of general purpose search engines (e.g., AltaVista, AllTheWeb, and Excite). However, the cities of Chicago and Dallas seem to have a very low ratio of travel related queries (6.2% and 9.9%, respectively), which could be caused by seasonality. Table 3 lists the top 20 categories of search queries based upon the content analysis. It 12

shows that among all queries, two queries, i.e., city name (67.6%) and city name with state name (15.5%) constituted a substantial majority of the search domain (in terms of search volume). This indicates many search engine users may have undecided information needs by starting their search with a very general term. This is consistent with a number of recent studies on travel queries which showed that a majority of travelers start their searching from something very general like a place name to things that are more specific (hotels, map, transportation, etc) (Hwang, Xiang, Gretzel, & Fesenmaier, 2009). Also consistently with Xiang and Pan s (2009) finding, the category of search queries related to accommodation had the highest percentage (9.1%) among all travel related queries, followed by attraction (2.4%), deal (1.2%), transportation (1.0%), and restaurant (0.6%). It is interesting to observe that the top 10 search categories constituted more than 98 percent of all queries, indicating search engine users information needs about specific cities are extremely limited. Insert Table 3 about here Assessing the Visibility of CVB Websites in Google Mining the visibility of CVB websites in Google showed that, in total, these CVB websites occurred 702 times on the first three pages of search results. Considering it was generated by 150 queries for 18 cities each, this was just a small fraction (less than 1%) of all search results occurring on the first three SERPs, suggesting the competition space for CVB websites is huge. Among these 702 instances, 422 (about 60%) were displayed on the first page of search results and 244 (approximately 35%) were among the top three search results on the first page, which may suggest that overall CVBs were not at a very competitive position. 13

Figure 1 shows the visibility (measured by number of occurrences) of the websites of the 18 cities on the first three SERPs in Google in response to all queries about these cities. In terms of the total number of occurrences, Fort Worth (N=91), Chattanooga (N=90), and Myrtle Beach (N=86) were the top three, followed by New York City (N=58), San Jose (N=49), Memphis (N=48), Las Vegas (N=46), San Francisco (N=44), Baltimore (N=42), Orlando (N=37), and Chicago (N=33). This seems to suggest that from the supply side these top three medium-sized cities have less competition in the information space and thus their CVB sites could achieve higher ranks in Google. Alternately, the findings could be attributed to more effective online marketing efforts by these CVBs. In terms of number of occurrences on the first SERP, Fort Worth (N=66), New York City (N=43), and Chattanooga (N=42) were the top three, followed by Myrtle Beach (N=36), Memphis (N=34), San Jose (N=33), San Francisco (N=32), and Las Vegas (N=31). Fort Worth (N=47) leads the group in terms of number of occurrences among top three search results on the first SERP, followed by San Francisco (N=26), and New York City (N=25). It is interesting to note that for touristic cities such as Orlando and Las Vegas, their CVB websites were not necessarily ranked in a high position by Google. And for some metropolitan areas such as Dallas, the visibility of their CVB websites seems extremely low (with 9, 9, and 1 number of occurrences on the first three SERPs, the first SERP, and among the top three search results on the first SERP, respectively). Insert Figure 1 about here A further examination of these occurrences weighted by the search volume for each city showed that Las Vegas, Chicago, and Orlando were the top three, indicating that these websites potentially generate the largest numbers of impressions through Google (since the numbers of 14

impressions are extremely large, the discussion of the results only provides a qualitative comparison instead of a quantitative one using exact numbers). As can be seen in Figure 2, this, of course, may reflect the level of interest in searching for information related to these cities on the Internet. However, in terms of potential impressions generated by being among the top three search results on the first SERP in Google, Las Vegas, San Francisco, and Chicago were the top three in that order. This suggests that there might be some variation in the quality of the potential impressions generated for these city CVBs. Once again, it was interesting to see CVBs from large metropolitan areas such as Dallas and Indianapolis had very few numbers of impressions as compared to others. Generally speaking, this suggest that the compounding effect of the search volume for these cities and the potential effectiveness of their CVB websites. Insert Figure 2 about here Figure 3 shows the CVB visibility among Google search results in relation to the search domains aggregated on all 18 cities. This graph was generated by plotting the percentages of CVB occurrences in the top 10 search categories, i.e., attraction (23.8%), travel info (14.9%), city + state name (8.4%), activity (8.1%), accommodation (6.7%), dining (6.4%), city name (6.0%), events (4.3%), map (3.7%), shopping (3.1%), and convention (2.8%), against the percentages of search volumes for these categories. When comparing supply and demand, it is interesting to observe that there seems to be huge discrepancies between percentages of website occurrences and those of search volumes for the same query categories. For example, the category of CVB websites most frequently occurred is attraction, which, however, represents only 2.4% of the total search volume of travel-related queries. The largest discrepancy occurred in the category of city name where the CVB website was presented approximately 6% of all occurrences, while the search volume for this specific category was 15

nearly 68%. The explanation for this may be city name is a more generic query and its competitive space is substantially larger than the domains defined by other travel specific queries (e.g., attraction ). Overall, there are substantial discrepancies for most query categories, which suggest CVBs may not be responding effectively to what online consumers were actively searching for. Insert Figure 3 about here DISCUSSION With the tremendous amount of information available on the Internet and the growing important role that search engines play in providing access to tourism information, destination marketing organizations must have a substantial understanding of this new technological environment as well as their market positions in order to formulate effective strategies (Buhalis, 2000; Buhalis & Law, 2008; O'Connor & Murphy, 2004; Werthner & Klein, 1999; Yuan & Fesenmaier, 2000). Search engines are important as they play a critical role in bridging the supply and demand of tourism. As such, their visibility in major search engines such as Google has profound implications for the success of any destination marketing effort. This study the visibility of 18 American CVB websites based upon search results retrieved from Google using real, current queries for these cities collected from Google Adwords Keyword Tool. The results show that the search domain for information related to a tourist destination is huge, which reflects the current status of Google as the number one search engine on the Internet. Potentially travel-related queries constitute only a small fraction of all queries. The analysis of DMO website visibility also showed that some (a limited number of) CVB website occurrences on SERPs provide by Google were located on the first SERP, which may indicate that relatively few 16

DMOs developed effective search engine marketing practices. Finally, there appear to be huge gaps among the focus of the CVB search engine marketing programs and the topical areas that consumers search. While exploratory in nature, this study offers a number of important implications for understanding the structure and competitiveness of online tourism as well as for search engine marketing for destinations. First, the examination of queries about cities extracted from Google Adwords Keyword Tool further confirms the structure of demand side of the online tourism domain. While to a certain extent the results are consistent with previous findings (Wöber, 2006; Xiang, Gretzel et al., 2009; Xiang & Pan, 2009), this study reveals that online consumers information needs are focused primarily on a handful of activities related to tourism; these include accommodations, attractions, activities, and dining. Also, these queries are constructed predominantly for a utilitarian purpose (i.e., not related to emotions or feelings). In contrast with previous research, although there might be a long tail of queries with low frequencies, this long tail is likely to be very thin (i.e., members of the long tail will have extremely low frequencies) (Anderson, 2006). In addition, a large portion of queries about cities is directly constructed in the form of either the city name or the city name plus the state name. While it is impossible to confirm whether these queries are directly travel related, it seems very likely that many travelers actually start with these general terms and then move to more specific aspects during the search process. Second, this study contributes to the literature of online tourism marketing by devising a process to assess the visibility of DMOs on the Internet. Tourism marketing is becoming increasingly dependent upon new technologies that support and enable DMOs to connect with visitors (Buhalis & Law, 2008). While organizations are constantly adopting and implementing 17

new applications and techniques (Wang & Fesenmaier, 2006), tourism research is, perhaps, behind the curve of the practice of technology use in terms of the capability to provide directions and guidance for the industry. This study builds upon previous studies on travelers use of search engines for trip planning (e.g., Wöber, 2006; Xiang, Gretzel et al., 2009; Xiang & Pan, 2009; Xiang, Wöber et al., 2008) and represents the first attempt to examine to the visibility of destination websites one of the key aspects of search engine marketing for destination marketing organizations. The devised process represents a methodologically sound approach which enables destination marketing organizations to measure the effectiveness of their search engine marketing program. Third, the analysis of CVB website visibility further demonstrates the overall level of competitiveness in search engines like Google and possible challenges DMOs are facing when making their information available to travelers online. Considering among hundreds and thousands of search results retrieved by Google, the 18 CVBs only occurred less than one percent along with all search results on the first three SERPs. In addition, less than one third of all these occurrences took place among the top three search results on the first SEPR. While this indicates that today s Internet, indeed, offers consumers with abundance of choices, it also creates huge challenges for DMOs to attract and engage consumers in a very short time span (Kim & Fesenmaier, 2008). Generally speaking, DMO websites are not necessarily seen by Google as the primary information source for online travelers. Finally, this study offers a number of managerial implications for DMOs to improve their search engine marketing programs. There are several recent studies that emphasize the utility of the long tail in tourism marketing (Anderson, 2006; Lew, 2008; Xiang, Pan, & Fesenmaier, 2008). However, the analysis of Google queries in this study reveals the dominance of the hits 18

(i.e., search terms with high frequencies) in the distribution of potentially travel related queries in search engines. While a long tail might still exist, it is questionable whether the investment in making sure the DMO website is visible to queries in this long tail is valuable and worthwhile. Additionally, the analysis shows that are important gaps in the search areas wherein DMO websites are visible in relation to search volume in these areas. While this may reflect the actual outcome of DMOs rational and conscious choice in investing in these areas, it might also indicate potential strategic misses of opportunities whereby they may potentially have a higher impact, e.g., in terms of generating more impressions and hits by investing in those high volume search areas. This suggests DMOs may need to re-plan their strategies when choosing and targeting the segments in the search market. Finally, this study clearly shows that that the visibility of CVB sites varies considerably across destinations. For example, it is quite interesting to see that CVB websites of some of the middle-sized cities, e.g., Fort Worth and Chattanooga, have higher visibility in Google while the opposite is true for more touristic places like Las Vegas and Orlando. The visibility also varies substantially among large metropolitan areas, as in the cases of New York City vs. Dallas. Although this study cannot determine whether this should be attributed to the level of competitiveness of the information domain for a specific city or it is an outcome of the online marketing efforts by a specific CVB, it provides DMOs sufficient motivations for planning for their search engine marketing strategies. This study has a number of important limitations. First, the 18 American cities (and the CVB and their destination marketing websites) were selected as cases from potentially hundreds and thousands of cities (or other tourist destinations). Second, this study employed a cross sectional examination of what consumers search for and how one of the most important search 19

engines responds to consumers queries. As a result, the richness and dynamics in the online search world was not fully captured, and variables such as seasonality could have a huge impact on the results. As such, the findings of this study should be interpreted with caution. Third, the data used in this study were collected through a secondary source, i.e., Google Adwords Keyword Tool. User queries were provided based upon their frequencies in Google and, consequentially, there was very little contextual information about the nature of these queries. Content coding of these queries was done independently to decide whether a specific term was, indeed, related to travel. This could have left more room for errors. Fourth, this study focused primarily on the rankings of CVB websites among Google search results. The results of their visibility cannot be attributed to their search engine marketing effectiveness because of the potentially different levels of competitiveness within these search domains. In addition, it must be pointed out that ranking and visibility, while extremely important, should not be the only focus for search engine marketing for destinations. As shown in several recent studies (Kim & Fesenmaier, 2008; Xiang, Kim, & Fesenmaier, 2009), persuasive communication can have a huge impact on travelers perception of the relevance of information contained in search engine results as well. Nonetheless, it is argued that this study provides a meaningful understanding of the visibility of DMO websites in search engines, and therefore, useful insights into DMOs search engine marketing programs. There are a number of areas of interest for future research in order to improve the generalizablility of this stream of research, and potentially, lead to better theory construction. For example, the visibility issue should be explored further across a number of search engines (e.g., Yahoo! and Ask.com) in order to assess the consistency of these tools. Second, a longitudinal analysis of the change in website rankings is also important so as to fully 20

document the dynamics of search on the Internet. Finally, a more generalizable set of metrics need to be established in order to evaluate the visibility and effectiveness of DMOs search engine marketing efforts across different destinations. 21

REFERENCES Anderson, C. (2006). The Long Tail: Why the Future of Business is Selling Less for More. New York: Hyperion. Buhalis, D. (2000). Marketing the competitive destination of the future. Tourism Management, 21, 97-116. Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet The state of etourism research. Tourism Management, 29(4), 609-623. Buhalis, D., & Licata, M. C. (2002). The future of etourism intermediaries. Tourism Management, 23(3), 207-220. Connolly, D. J., Olsen, M., & Moore, R. G. (1998). The Internet as a distribution channel. Cornell Hotel and Restaurant Administration Quarterly, 39, 42-54. emarketer. (2008). First Summer Vacation Stop: The Internet. Retrieved June 2, 2008, from http://www.emarketer.com/article.aspx?id=1006344&src=article1_newsltr Enquiro. (2006). Enquiro Eye Tracking Report II: Google, MSN and Yahoo! Compared. Retrieved August 25, 2009, from http://www.enquiro.com/research/eyetrackingreport.asp Fesenmaier, D. R., Xiang, Z., Pan, B., & Law, R. (2010). An analysis of search engine use for travel planning. Paper presented at the Information and Communication Technologies in Tourism ENTER 2010, Lugano, Switzerland. Fodness, D., & Murray, B. (1998). A typology of tourist information search strategies. Journal of Travel Research, 37(2), 108-119. Google. (2006). Seattle's Convention and Visitors Bureau found 30% ROI with Google AdWords. Retrieved December 15, 2006, from http://www.google.com/ads/scvb.html Gretzel, U., Fesenmaier, D. R., Formica, S., & O'Leary, J. T. (2006). Searching for the future: Challenges faced by destination marketing organizations. Journal of Travel Research, 45(2), 116-126. Gursoy, D., & McLeary, K. W. (2004). An integrated model of tourists' information search behavior. Annals of Tourism Research, 31(2), 343-373. Henzinger, M. (2007). Search technologies for the Internet. Science, 317(5837), 468-471. Hopkins, H. (2008). Hitwise US Travel Trends: How Consumer Search Behavior is Changing. from http://www.hitwise.com/registration-page/hitwise-report-travel-trends.php Hwang, Y. H., Xiang, Z., Gretzel, U., & Fesenmaier, D. R. (2009). Assessing structure in travel queries. Anatolia: An International Journal of Tourism and Hospitality Research, 20(1). Jansen, B. J., & Spink, A. (2003, June 23-26, 2003). An analysis of web documents retrieved and viewed. Paper presented at the the 4th International Conference on Internet Computing, Las Vegas, Nevada. Kim, H., & Fesenmaier, D. R. (2008). Persuasive design of destination Websites: an analysis of first impression. Journal of Travel Research, 47(1), 3-13. Kotler, P., Bowen, J., & Mackens, J. C. (2009). Marketing for Hospitality & Tourism (5th Edition). Boston, MA: Prentice Hall. Lew, A. A. (2008). Long Tail tourism: New geographies for marketing niche tourism products. Journal of Travel & Tourism Marketing, 25(3/4), 409-419. Marchionini, G. (1997). Information Seeking in Electronic Environments. Cambridge, UK: Cambridge University Press. 22

Moran, M., & Hunt, B. (2005). Search Engine Marketing, Inc.: Driving Search Traffic to Your Company's Web Site. Lebanon, IN: IBM Press. O'Connor, P. (1999). Electronic Information Distribution in Tourism and Hospitality. Wallingford: CABI. O'Connor, P., & Frew, A. (2002). The future of hotel electronic distribution: expert and industry perspectives. Cornell Hotel and Restaurant Administration Quarterly, 43, 33-45. O'Connor, P., & Murphy, J. (2004). Research on information technology in the hospitality industry. International Journal of Hospitality Management, 23, 473-484. Pan, B., & Fesenmaier, D. R. (2006). Online information search: vacation planning process. Annals of Tourism Research, 33(3), 809-832. Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In Google we trust: Users decisions on rank, position and relevancy. Journal of Computer-Mediated Communication, 12(3), 801-823. Pan, B., Xiang, Z., Fesenmaier, D. R., & Law, R. (accepted). The dynamics of search engine marketing for tourist destinations. Journal of Travel Research. Prescott, L. (2006). Hitwise US Travel Report. from http://www.hitwise.com/registrationpage/hitwise-us-travel-report.php Spink, A., & Jansen, B. J. (2004). Web Search: Public Searching of the Web. New York: Kluwer. Thurow, S. (2003). Search Engine Visibility. Indianapolis, IN: New Riders. TIA. (2005). Travelers' Use of the Internet. Washington, DC: Travel Industry Association of America. TIA. (2008). Travelers' Use of the Internet. Washington D.C.: Travel Industry Association of America. Vogt, C. A., & Fesenmaier, D. R. (1998). Expanding the functional information search model. Annals of Tourism Research, 25(3), 551-578. Wang, Y., & Fesenmaier, D. R. (2006). Identifying the Success Factors of Web-Based Marketing Strategy: An Investigation of Convention and Visitors Bureaus in the United States. Journal of Travel Research, 44, 239-249. Weber, K., & Roehl, W. S. (1999). Profiling people searching for and purchasing travel products on the World Wide Web. Journal of Travel Research, 37(3), 291-298. Werthner, H., & Klein, S. (1999). Information Technology and Tourism: A Challenging Relationship. Vienna: Springer. Wöber, K. (2006). Domain specific search engines. In D. R. Fesenmaier, K. Wöber & H. Werthner (Eds.), Destination Recommendation Systems: Behavioral Foundations and Applications. Wallingford, UK: CABI. Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179-188. Xiang, Z., Gretzel, U., & Fesenmaier, D. R. (2009). Semantic representatin of the online tourism domain. Journal of Travel Research, 47(4), 440-453. Xiang, Z., Kim, H., & Fesenmaier, D. R. (2009). Modeling the persuasive effect of search engine results. Paper presented at the the International Society of Travel and Tourism Educators annual conference, San Antonio, TX. Xiang, Z., & Pan, B. (2009). Travel Queries on Cities in United States: Implications for Search Engine Marketing in Tourism. In Proceedings of the 16th International Conference on Information and Communication Technologies in Tourism - ENTER 2009. Amsterdam, Netherland: Springer. 23

Xiang, Z., Pan, B., & Fesenmaier, D. R. (2008). Developing SMART-Search: A search engine to support the long tail in destination marketing. Paper presented at the Annual Conference of the Travel and Tourism Research Association (TTRA), Philadelphia, PA. Xiang, Z., Wöber, K., & Fesenmaier, D. R. (2008). Representation of the online tourism domain in search engines. Journal of Travel Research, 47(2), 137-150. Yuan, Y., & Fesenmaier, D. R. (2000). Preparing for the new economy: The use of the Internet and Intranet in American Convention and Visitors Bureaus. Information Technology and Tourism, 3(2), 71-86. Yuan, Y., Gretzel, U., & Fesenmaier, D. R. (2003). Managing innovation: The use of Internet technology by American convention and visitors bureaus. Journal of Travel Research, 41(3), 240-256. Zhang, J., & Dimitroff, A. (2005a). The impact of metadata implementation on webpage visibility in search engine results (Part II). Information Processing and Management, 41(3), 691-715. Zhang, J., & Dimitroff, A. (2005b). The impact of webpage content characteristics on webpage visibility in search engine results (Part I). Information Processing and Management, 41(3), 665-690. 24

Table 1 List of 18 U.S. Cities Category City 2002 Population Small Americus 16,955 Cities Myrtle Beach 24,832 Aiken 26,620 Bradenton 51,458 Champaign 71,987 Mid-Sized Cities Large Cities Pueblo 103,679 Chattanooga 156,067 Orlando 197,058 Las Vegas 507,461 Fort Worth 569,747 Baltimore 636,302 Memphis 676,323 San Francisco 763,400 Indianapolis 782,538 San Jose 898,713 Dallas 1,205,785 Chicago 2,889,446 New York City 8,106,876 25

Table 2 Monthly Volumes of City-based Queries using Google Travel Related Monthly Query Only City Search Vol. Vol. Pcnt Las Vegas 72,599,890 27,287,690 37.6% Chicago 57,985,600 3,604,830 6.2% Orlando 38,751,200 11,121,000 28.7% Dallas 38,447,200 3,818,000 9.9% San Francisco 30,698,690 5,937,090 19.3% Baltimore 17,339,530 2,020,090 11.7% Indianapolis 14,642,010 1,904,129 13.0% San Jose 14,445,160 2,003,740 13.9% New York City 12,609,380 3,976,580 31.5% Memphis 11,993,010 1,426,540 11.9% Myrtle Beach 10,994,330 3,604,830 32.8% Fort Worth 9,055,420 1,079,110 11.9% Chattanaooga 4,345,390 553,259 12.7% Bradenton 2,372,786 337,401 14.2% Champaign 2,184,390 65,230 3.0% Pueblo 2,094,690 357,480 17.1% Aiken 1,884,492 208,549 11.1% Americus 2,18750 20,560 9.4% Total/Average 342,661,918 69,326,108 20.2% * Note: Data represents queries to Google during the June 1 May 31, 2009. 26

Table 3 Search Volumes of Travel Related Queries using Google Type of query Monthly Search volume * Percentage Cumulative percentage City name 207,732,900 67.6% 67.6% City name with state name 47,683,784 15.5% 83.1% Accommodation 27,836,419 9.1% 92.1% Attraction 7,339,412 2.4% 94.5% Deal 3,596,210 1.2% 95.7% Transportation 3,183,430 1.0% 96.7% Restaurant 1,824,460 0.6% 97.3% Activity 1,816,555 0.6% 97.9% Entertainment 843,019 0.3% 98.2% Rental 813,080 0.3% 98.4% Event 806,811 0.3% 98.7% Map 683,197 0.2% 99.2% Dining 610,519 0.2% 99.4% Travel info 468,800 0.2% 99.5% Shopping 394,643 0.1% 99.7% Convention 291,740 0.1% 99.8% Ticket 233,440 0.1% 99.8% Photos 142,816 0.0% 99.9% Culture 126,070 0.0% 99.9% Review 122,500 0.0% 99.9% * Note: Data represents queries to Google during the June 1 May 31, 2009. 27

Table 4 Search Volume for the Chicago CVB Query Average Monthly Search Volume Site Occurrences Category N Percent N Percent attraction 57,986 2.5% 8 24% travel info 32,472 1.4% 7 21% city + state name 371,108 16.0% 4 12% activity 18,555 0.8% 3 9% accommodation 220,345 9.5% 3 9% dining 11,597 0.5% 2 6% city name 1,577,208 68.0% 2 6% events 11,597 0.5% 2 6% map 11,597 0.5% 1 3% shopping 6,958 0.3% 1 3% total 2,319,424 100% 33 100% * Note: Data represents queries to Google during the June 1 May 31, 2009. 28

Figure 1 Visibility of 18 American CVB Websites on Google 29

Figure 2 Weighted Visibility of 18 American CVB Websites in Google 30

Figure 3 CVB Visibility in Relation to Search Domains 31