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.

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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

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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

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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 human-

computer 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

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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 tourism-

related 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

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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 strategi

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.

es

;

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

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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).

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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

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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

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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 “non-

travel 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

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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

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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

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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.

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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

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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

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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

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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

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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

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(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

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Table 1 List of 18 U.S. Cities

Category City 2002 Population Small Cities

Americus 16,955 Myrtle Beach 24,832 Aiken 26,620 Bradenton 51,458 Champaign 71,987 Pueblo 103,679

Mid-Sized Cities

Chattanooga 156,067 Orlando 197,058 Las Vegas 507,461 Fort Worth 569,747 Baltimore 636,302 Memphis 676,323

Large Cities

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

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Table 2 Monthly Volumes of City-based Queries using Google

City Monthly

Search Vol.

Travel Related Query Only

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.

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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.

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Table 4 Search Volume for the Chicago CVB

Query Category

Average Monthly Search Volume

Site Occurrences

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.

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Figure 1 Visibility of 18 American CVB Websites on Google

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Figure 2 Weighted Visibility of 18 American CVB Websites in Google

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Figure 3 CVB Visibility in Relation to Search Domains

31