Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations

Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations

Tourism Management 32 (2011) 88–97 Contents lists available at ScienceDirect Tourism Management journal homepage: www.elsevier.com/locate/tourman T...

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Tourism Management 32 (2011) 88–97

Contents lists available at ScienceDirect

Tourism Management journal homepage: www.elsevier.com/locate/tourman

Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations Zheng Xiang a, *, Bing Pan b,1 a b

School of Merchandising and Hospitality Management, University of North Texas, 1155 Union Circle, #311100 Denton, TX 76203-5017, USA Department of Hospitality and Tourism Management, School of Business and Economics College of Charleston, 66 George Street, Charleston, SC 29424, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 March 2009 Accepted 9 December 2009

Given the growing importance of search in online travel planning, marketers need to better understand the behavioural aspect of search engines use. Built upon a number of previous studies, the goal of this research is to identify patterns in online travel queries across tourist destinations. Utilizing transaction log files from a number of search engines, the analysis shows important patterns in the way travel queries are constructed as well as the commonalities and differences in travel queries about different cities in the United States. The ratio of travel queries among all queries about a specific city seems to associate with the ‘‘touristic’’ level of that city. Also, keywords in travelers’ queries reflect their knowledge about the city and its competitors. This paper offers insights into the way tourism destinations are searched online as well as implications for search engine marketing for destinations. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Internet Travel information search Travel queries Search engines Search engine marketing Destination marketing

1. Introduction Information technology (IT), particularly the Internet, has changed the way travel-related information is distributed and the way people search for and consume travel (Beldona, 2005; Buhalis & Law, 2008; Weber & Roehl, 1999; Werthner & Klein, 1999). In recent years, search engines have become a dominant source in consumers’ use of the Internet to access travel products. For example, research conducted by the Travel Industry Association of America found that a substantial number of travelers use search engines for travel planning (TIA, 2005, 2008). Another study showed that search engines serve as the number one online information source for American families in the context of vacation planning (eMarketer, 2008). A series of reports by the Internet research firm Hitwise have documented the importance of search engines in terms of generating upstream traffic to hospitality websites, leading to direct bookings for these businesses (e.g., Hopkins, 2008; Prescott, 2006). As such, search engines can be seen as a powerful ‘‘gateway’’ for online consumers to access travel-related information as well as an important distribution channel for tourism destinations and businesses (Xiang, Wo¨ber, & Fesenmaier, 2008). Without doubt, the focus of the marketing and promotional efforts of tourism destinations has been steadily shifting toward

* Corresponding author. Tel.: þ1 940 369 7680; fax: þ1 940 565 4348. E-mail address: [email protected] (Z. Xiang). 1 Tel.: þ1 843 953 2025; fax: þ1 843 953 5697. [email protected] 0261-5177/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2009.12.004

the online world, particularly in the increasingly important domain of search (Buhalis, 1998, 2000; TIA, 2006; Wang & Fesenmaier, 2006; Werthner & Ricci, 2004). As a result, search engine marketing (SEM) has become one of the important strategic tools for tourism destinations and businesses to compete for consumers’ attention on the Internet and engage direct conversations with their potential guests (Sherman, 2007; TIA, 2008). For example, tourism businesses have been using various forms of SEM programs and tactics including directory listing, keyword purchasing, meta tags, sponsored links, and search engine optimization (Google, 2006; Wang & Fesenmaier, 2006; Xiang & Fesenmaier, 2006). Especially, compared to traditional advertising channels SEM is growing much faster. For instance, advertisers in North America spent US$9.4 billion on search engines in 2006, showing a 62% increase from 2005 and 750% increase from 2002 (Elliot, 2006). There are also cases in which destination marketing organizations (DMOs) also successfully adopted SEM practices (Google, 2006). SEM is a controlled communication process with online travelers. It requires a thorough understanding of travelers’ needs and the ability to identify strategic responses in order to satisfy their needs. One of the conditions for search engine marketing is to understand search engine users’ behaviour, particularly queries they use to search and contexts wherein these keywords are used (Moran & Hunt, 2005). For example, one of the strategies in SEM is to focus on what online consumers are searching for in order to make certain their websites are visible in response to search queries. However, little research with high relevance has been

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conducted on this emergent marketing practice to offer useful insights for the tourism industry. Built upon a number of recent studies on travelers’ use of search engines (Pan, Litvin, & O’Donnell, 2007b; Wo¨ber, 2007; Xiang, Gretzel, & Fesenmaier, 2009; Xiang et al., 2008), the goal of this research is to provide an understanding of travelers’ search behaviour by identifying general patterns in travel-related queries on cities in the United States. By doing so, it is hoped that this study will offer useful strategic insights in search engine marketing for tourist destinations. The rest of the paper is organized as follows: the Research Background section critically reviews relevant literature on travelers’ use of search engines and travel queries; then, Research Questions are raised with respect to understanding general patterns in travel queries on cities; the Methods section explains the use of text mining techniques to extract and describe travel queries based on a number of search engine transaction log files; the Results section presents the findings of the study, followed by the section of Conclusions and Implications wherein the contribution of this study to our understanding of travel search behaviour and implications are discussed; finally, Limitations and Future Research directions are discussed. 2. Research background The primary task in tourism businesses’ marketing and promotional efforts is to ensure relevant information is made visible and accessible to potential visitors (Buhalis, 1998; Werthner & Klein, 1999). Within the context of online information search, search engines serve as an important tool that bridges the traveler and the tourism industry online. Like any other marketing practices, the success of search engine marketing, then, requires the marketer to have a good understanding of consumer behaviour in order to provide the information desired by these consumers. As such, it is argued that understanding how search engines work and how travelers use these tools provides one of the keys to successful search engine marketing programs for tourist destinations. This section reviews past literature on travelers’ use of search engines for travel planning as well as the nature of travel queries. Limitations of past studies are discussed to provide the rationale for the present study. 2.1. Travelers’ use of search engines and search engine marketing for destinations The use of search engines to access a repository of information has long been studied in fields such as information science, information retrieval, computer science, as well as human-computer interaction (Marchionini, 1997). In general, the process of using a search engine can be understood as consisting of three steps (Kim & Fesenmaier, 2008): first, the user enters a query into the interface. Research has shown that three factors determine 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. Given the growing important role of search engines in bridging the online consumers, especially travel information searchers, a subject has recently emerged with the emphasis on

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understanding the travelers’ behaviour in the use of search engine. Following from the literature in fields such as information science, travelers’ use of search engines can be seen as the interaction between travelers’ information needs and the online tourism domain, which is facilitated and mediated by the technological interface (Pan & Fesenmaier, 2006; Xiang et al., 2008). A number of interesting models have been raised with respect to this interaction. For example, Pan and Fesenmaier (2006) argued that the congruence between the traveler’s mental model and the online tourism domain is an important indicator of the ‘‘fitness’’ between the traveler’s information needs and the information being searched. Xiang and Fesenmaier (2006) argue that travelers’ use of search engines can be seen as the initial step in the persuasion process for tourism organizations and destinations, and the effectiveness of their marketing and promotional programs depends upon the persuasiveness of the messages they deliver through search engines. Further, Kim and Fesenmaier (2008) posited and empirically tested that the use of search engines can have a significant impact on the formation of one’s first impression toward, and subsequently the overall evaluation of, a destination marketing organization’s website. As such, search engines have the potential to influence an online traveler’s impression, intention, as well as attitude toward the website owned by a tourist destination and its tourism-related businesses, or the destination and businesses themselves. Generally speaking, when exposed to a list of search results, a number of factors can influence the traveler’s evaluation and selection of search results. Particularly, the ranking of a specific search result link along with its relevancy to the search query is widely recognized as the most important factor in influencing the travel information searcher’s behaviour. For example, the majority of search engine users do not look beyond the first three pages of search results (Henzinger, 2007). Also, the rank position of a specific search result has been shown to determine whether it will be reviewed and evaluated by an information searcher (Pan et al., 2007a). Search engine marketing, or SEM, is a form of Internet marketing that seeks to promote websites by increasing their visibility in Search Engine Result Pages (SERPs) (Moran & Hunt, 2005). In fact, search engine marketing encompasses a number of techniques or strategies to improve and enhance the Website’s visibility in SERPs (Moran & Hunt, 2005), including: 1) search engine optimization involves adopting methods with the focus on organic search that improve the ranking of a website when a user types in relevant keywords in a search engine. These include creating an efficient website structure, providing appropriate web content, and managing inbound and outbound links to other sites; 2) paid inclusion involves paying search engine companies for inclusion of the site in their organic listings; 3) Search engine advertising, or paid placement, refers to buying display positions at the paid listing area of a search engine. Google AdWords or Yahoo! Precision Match are the two most popular programs, wherein paid placement listings are shown as ‘‘Sponsored Links’’; and, 4) Directory listing refers to the submission of the website to a directorybased search engine (e.g., Yahoo! Directory) to be shown under its subject category list. The success of all these endeavors requires a thorough understanding the way consumers use search engines for travel-related information. While search engines are becoming increasingly important for online travelers, studies have shown that the visibility of many tourism business’s websites to prospective visitors is diminishing. Recently, for example, Wo¨ber (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.

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(2008), it was found that a handful of ‘‘big players’’ dominate search results in Google, leading to the diminishing visibility of numerous small and medium-size tourism enterprises. As such, it is increasingly important for tourism marketers to understand how travelers search information about destinations to improve their ranking and visibility in search engines by identifying and implementing effective strategies. 2.2. Travel queries in search engines Search queries are perhaps the most important behavioural aspect of the use of search engines. Studies of search engine user search behaviour have a long history in fields such as information and library science with the focus on the characteristics of search queries, such as the length and depth of search, types of search intent, search strategies, and changes of search characteristics over time (Jansen & Molina, 2006). Generally speaking, due to the way user-interface interaction has been designed, search queries are short strings of words or terms that reflect the user’s search intent, information needs, or goals as well as his/her search strategies (Jansen & Spink, 2005). Particularly important is the emphasis on understanding users’ goals through user queries on the Internet. According to the Taxonomy of Web Search (Broder, 2002; Jansen, Booth, & Spink, 2008; Rose & Levinson, 2004), there are basically three types of user goals possibly reflected in one’s queries: 1) navigational goal which demonstrates a desire by the user to look for the page of the organization or business in question. For example, if a user types in ‘‘Marriott hotel’’ in a search engine, it is very likely he/she intends to find the Web address of the hotel chain; 2) informational goal, which focuses on obtaining information about the query topic (e.g., a product). This includes a spectrum of many possible ways to ask questions to the search engine. For example, the question can be directed, with the intention to learn something in particular about the topic (e.g., ‘‘Chicago Art Institute admission price’’), or undirected, with the intention to learn about anything related to a topic (e.g., ‘‘Chicago Art Institute’’); and, 3) transactional goal, which focuses on carrying out certain action, e.g., downloading a podcast or purchasing a book. Recently, Jansen et al (2008) found that users queries in general are largely informational 81% (the other two categories: navigational 10%; and, transactional 9%). Recently, a number of studies have been conducted in the field of tourism with the aim to understand the nature of travel queries. One of underlying streams in this research is the analysis of search queries on places. A study of Excite search engine log data in 2001 on geographic searches found that 14.8% of searches are related to place names (Sanderson & Kohler, 2004). Another study on search engine log data in Yahoo revealed that searches on places mainly focus on city (84%), country (14%), and state (3%) (Jones et al., 2006). Recently, Pan et al. (2007b) examined the structure of query formulations in the context of searching for accommodations. Their study conducted four types of analysis, including types of query keywords, types of whole queries, sequence of query formulations, and associations of keyword types. Their results suggest that travelers most often search for their accommodations simultaneously with their search for other aspects of their travel, such as destinations, attractions, transportations, and dining; and that they most often begin their search for specific hotels in conjunction with the city they are considering for a visit. A sequential analysis also revealed that many users engage in a switching behaviour that swings between broad and focused research tactics. In addition, their study also demonstrated strong associations between place names, particularly city names, with ‘specific hotel’ and ‘hotel brand’; and have weaker connections with searches utilizing the keywords ‘hotel’ and ‘hotel type’. While

the inverse is of course true that ‘specific hotel’ and ‘hotel brand’ searches had the strongest connection with searches at the ‘city’ level, the study showed that they were weakly connected with searches that employed keywords coded as ‘state’, ‘country’, and ‘region’. Recently, Xiang et al. (2009) analyzed user queries pertaining to a specific tourist destination (i.e., Chicago) related to potentially all aspects of travel. Based upon a series of analyses of user queries from search engine transaction files, their study demonstrated that the majority of travel queries are short expressions of travelers’ information needs about different aspects of traveling to a place. Their study shows that, overall, there are relatively few distinct vocabularies in user queries that represent the majority of tourismrelated ‘‘things’’ (e.g., ‘‘Chicago hotel’’ and ‘‘Chicago attractions’’). However, there is also a ‘‘long tail’’ of words that represent users’ heterogeneous information needs and their own mental images of the tourism experience, which reflects the idiosyncratic nature of places. A study by Wo¨ber (2007) examined keywords and phrases extracted from the user log file in an Europe-based tourism website to assess the image of a particular city. Information which describes the users’ interests was extracted from entries into the fields ‘keyword’ and ‘city’. Text analysis in conjunction with multidimensional scaling was used to identify patterns of competition between a number of European cities. Instead of physical characteristics commonly used in competitive studies of tourism destinations, the approach considered the perception of consumers and their information needs reflected from their queries. The findings showed some interesting images of European cities perceived by travelers. For example, cities like Madrid, Budapest, Prague, Nice and Rotterdam were perceived as similar in travel queries expressing information needs for ‘guided tours’, ‘operas’, ‘museum’ and ‘art’. Another group, with cities like Heidelberg, Lyons, Tallinn, Bergen, Copenhagen, and Amsterdam, seemed to generate more substantial travelers’ interests in ‘attractions’ and ‘events’. This study reveals the ‘‘competition space’’ for destinations in online travel information search. As shown by these studies, travel queries are indeed expressions of travelers’ information needs with a number of distinctive characteristics. First, constrained by search engine interfaces, travel queries manifest a simple but distinctive semantic or linguistic structure in that they are usually short phrases, oftentimes formed by the combination of city name plus a specific travel-related keyword (Xiang et al., 2009). Also, the types of searched information appear to reflect a spectrum of information needs ranging from very general (the ‘‘head’’) to highly specific (the ‘‘long tail’’). These findings are consistent with Pan and Fesenmaier’s (2006) study which suggests that the majority of users search for information that is very general (e.g., ‘‘Chicago hotel’’), while a relatively small number of them directly search for specific information by including the name of the business (e.g., ‘‘Chicago Wyndham Hotel’’). Second, generally speaking information needs reflected in travel queries are basically ‘‘functional’’ with the emphasis on the utilitarian value. These findings appear to be consistent with previous literature on tourism information search whereby most information sought when planning a trip is functional rather than hedonic (Fodness & Murray, 1997; Gursoy & McLeary, 2004; Vogt & Fesenmaier, 1998). That is, travelers are much more likely to focus on product attributes such as location, price, and availability, instead of more experiential ones that are based upon sensory and emotional aspects of the product (e.g., smell, atmosphere, sensation, and emotions, etc.). Third, while they are expressions of travelers’ needs and wants, travel queries also reflect travelers’ experience, knowledge, and

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perception of a destination. That is, travelers search for what they know, or heard of, about the destination by using these queries. Therefore, travel queries provide the ‘‘window’’ whereby the image of a destination can be possibly understood and constructed within a specific task context. While past studies generated useful insights into the nature of travel queries on the Internet, each of them has its own limitations, particularly in their scopes of analysis. Specifically, while Pan et al.’s (2007b) analysis of search strategy (i.e., the association between place names and hospitality related keywords) provides insight into the way travel queries are formulated, it only examined one sub-domain of tourism, that is, the lodging sector; the Xiang et al. (2009) study, while covering the tourism sectors in a more comprehensive way (i.e., by including attractions, dining, parks, nightlife, and shopping, etc in the analysis), solely focused on one city as a tourist destination. Overall, the generalizability of these studies is limited, particularly because they did not provide a comparative analysis across different destinations. The Wo¨ber (2007) study compared destination image in relation to search queries among a number of European cities. However, it was also limited because the data used were from a tourism portal website, instead of a general purpose search engine. As a result, its findings cannot be generalized to a completely different task setting and, thus, its insight is less valuable from a search engine marketing standpoint. Therefore, it is necessary to gain a better and more comprehensive understanding of information search behaviour through travel queries in order to help tourist destinations improve their marketing programs through search engines. 3. Research questions Tourist destinations must understand what online travelers are searching for as well as what destinations they are competing with within the search context. It is argued that travel queries provide the means to understand travelers’ search behaviour, particularly travelers’ needs and wants as well as their knowledge about destinations. In order to address the limitations in previous literature and provide more useful insights into search engine marketing strategies for destinations, three research questions were formulated to guide this study: Q1. What keywords/phrases do travelers use to form queries to search for information about cities in the United States? Q2. What are the commonalities and differences in keywords used to search for those cities? Q3. How these keywords are associated with those cities? From the marketing perspective, these questions can be rephrased as: what keywords should a city-level DMO focus on or bid on? How should a DMO compete with similar cities in search engines? 4. Methods Research methods employed in this study consisted of a series of text analyses performed on user queries extracted from a number of transaction logs from search engines including Excite (from the years of 1997, 1999, and 2001), AllTheWeb (from the years of 2001 and 2002), and AltaVista (from the year of 2002). These search engines were selected because they shared the same user interface typical for mainstream search engines. That is, like today’s Google, these search engines all employed a textbox to allow users to enter search queries while also providing search results in a list format. Although these transaction logs were fairly dated, they were considered appropriate sources to understand search behaviour, because search engine

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queries have not changed drastically in the past, particularly with respect of the number of words searchers used to form queries (Jansen & Molina, 2006). Furthermore, due to recent controversies on violation of user privacy in releasing search engine log data (Barbaro & Zeller, 2006; Fisher, 2006; Sanchez, 2006), the current search engine logs are the best option available (Pan et al., 2007b). 4.1. Data As a general practice, search engine transaction files usually include a number of fields such as the user query (i.e., the keywords/phrases a user types into the search box), the time stamp, the user’s IP address, and links of the search results which the user clicked on. While this research primarily focused on the user queries, other types of information such as time stamps and IP addresses were also used to infer about searching sessions for a specific individual user. As suggested by Spink et al. (2002), search keywords were defined as strings of characters with no space inbetween; the combination of keywords typed by a user defined a search query; and user sessions were defined as sequences of search queries in which the time between any two consecutive queries was less than one hour. Fig. 1 provides an example of a user search session (Pan et al., 2007b). Because these transaction log files contain virtually all kinds of queries about potentially any place in the world, a set of 18 tourist destination names in the United States (e.g., ‘‘New York City’’ and ‘‘Chicago’’) were used as the sample of tourist cities. These 18 cities include three tiers with six small cities, six medium-sized cities, and six large cities based upon their 2002 U.S. Census population. While it was a relatively small sample of all possible tourist destinations, the rationale for this selection was to have a sample that, to a certain degree, reflects the geographic and demographic diversity and allows the researchers to examine commonalities and potential nuances in travel queries. As such, cities within all three tiers were picked from different census regions including the Northeast, South, Midwest, and West. Specifically, these city names were used as the ‘‘seed’’ words to extract all the queries containing these destinations. These queries were then grouped together by unique sessions by aforementioned criteria. In total, there were 54,840 queries and 13,649 query sessions which contain the names of those 18 cities. Among them, many were about travel, consumer products, and others (e.g., pornography searches). 4.2. Analysis methods To answer the research questions, data analysis involved three steps. The first step focused on identifying travel queries. In order to establish the validity of the study, one of the key considerations in this analysis was to determine, among around half a million of search engine transaction records on cities, which queries were indeed travel-related queries. Two content coders were hired to determine whether a specific query should be considered a travel query within the context of one search session. Since there is no existing rules to determine this, coders were asked to use their Query ID

User Session ID

Time

50000

000000000000283b

192749

50101

000000000000283b

194447

50102

000000000000283b

194831

50103

000000000000283b

195826

50104

000000000000283b

200749

Query Keywords orlando motel hotel crestwood crestwood hotel crestwood hotel orlando crestwood orlando hotel

Fig. 1. An example of a user search session.

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judgment by ‘‘thinking’’ like a traveler who was looking for destination related information. For example, a query like ‘‘Chicago museums’’ within a short search session (usually with less than four queries) was more likely to be a travel-related query. Intercoder reliability was checked using Krippendorf’s Alpha (2004), which was .86, indicating there was a high level agreement between the two coders. One of the researchers then went through those queries in disagreement and assigned them as either travelrelated or non-travel-related according to the session contexts. The second step of analysis aimed at identifying keywords in query sessions for those cities. Text mining software CATPAC (Woelfel, 1998) was used as the main tool to calculate word frequencies because it allows the researcher iterate through the data set easily. A number of important decisions were made in this process: first, certain keywords were stemmed (e.g., converting ‘‘parties’’ into ‘‘party’’) and tokenized (e.g., converting ‘‘bed breakfast’’ and ‘‘bed and breakfast’’ into ‘‘bnb’’). The goals of these treatments were to make certain words in different tenses and numbers were treated as the same (stemming) and phrases such as (‘‘bed and breakfast’’) in its entirety instead of three different words (tokenization), respectively. Second, stop words (e.g., ‘‘the’’, ‘‘a’’) were identified and dropped from the analysis because they do not add meanings to search queries. Third, destination specific words (e.g., ‘‘Chicago’’ in ‘‘Chicago Museum’’) were also dropped, because they do not provide the ground for comparison between destinations. After the data were ‘‘cleaned’’, the number of queries was calculated for each of the pre-identified cities and plotted against their 2002 population to explore possible relationships. The third step aimed at examining the associations between common keywords in travel queries and destinations. This was achieved using correspondence analysis based upon a co-occurrence matrix of destinations and keywords. The top 10 destinations with the highest number of travel queries were selected. Also, the top 60 keywords were used because their total frequencies represented approximately 45% of the total frequencies of all unique words. All the query sessions were manually examined to make sure extreme cases were neither included nor overrepresented. For instance, because the word ‘‘gift’’ occurred more than 50 times in several query sessions about New York City from a single IP address, those specific query sessions were dropped from the analysis. In addition, each unique word was calculated only once when building the co-occurrence matrix to control the effect of potential overrepresentation of certain keywords; for example, some sessions may contain the same keywords multiple times and, sometimes, within the same query. Scripts written in Perl programming language were used to calculate the frequencies of co-occurrences, and correspondence analysis was conducted to examine the association between keywords and destinations based upon the 10 by 60 matrix.

Fig. 2. Distribution of session length in number of queries.

consistent with the literature which shows that, in general, search sessions are usually fairly short, consisting of only a few queries (e.g., Jansen & Spink, 2005). As shown in Fig. 2, more than half (52.3%) of all travel search sessions consisted of only one query and more than 80% of all travel search sessions consisted of no more than three queries. This indicates that, although travel information search is often a complex task, the use of search engines in one session only takes a very short time. Also, travel queries are comprised of relatively a small number of keywords or terms. As shown in Fig. 3, the majority (approx. 91%) of travel queries were formed with no more than four keywords or terms. This is also consistent with previous findings that show search queries in general and travel queries in particular are, indeed, short expressions of user needs (Jansen & Spink, 2005; Xiang et al., 2009) Table 1 lists the number of travel query sessions for the preidentified 18 cities in the United States. These cities were categorized into three tiers: Small Cities (with population less than 150,000), Mid-Sized Cities (with population between 150,000 and 700,000), and Large Cities (with population more than 700,000). As can be seen, a number of cities, including Las Vegas, Chicago, Orlando, and New York City, stood out as the ones with relatively larger numbers of travel queries. Cities such as Americus, Aiken,

5. Results The findings of this study are presented in three sections: the first section provides the descriptive statistics on identified travel query sessions, especially on the ratios of travel query sessions among all sessions for all the cities; the second section shows the keywords identified in travel queries especially the most frequently used; and, the third section provides the results of the correspondence analysis to show the association between keywords and destinations. 5.1. Search sessions of travel queries for cities In total, 5032 travel search sessions and 19,016 travel queries were identified, resulting in approx. 3.8 queries per session. This is

Fig. 3. Distribution of query length (number of keywords).

Z. Xiang, B. Pan / Tourism Management 32 (2011) 88–97 Table 1 Number of travel query sessions for 18 U.S. cities. Destination City category

Small cities

Americus Myrtle Beach Aiken Bradenton Champaign Pueblo

Mid-sized cities

Chattanooga Orlando Las Vegas Fort Worth Baltimore Memphis

Large cities

San Francisco Indianapolis San Jose Dallas Chicago New York City

Num of Number of Ratio of 2002 Travel query non-travel travel query Population sessions query sessions sessions 0 183 1 0 11 34

5 87 34 29 65 81

0 0.68 0.03 0 0.14 0.30

16,955 24,832 26,620 51,458 71,987 103,679

31 554 1,997 35 169 112

77 485 774 111 459 307

0.29 0.53 0.72 0.24 0.27 0.27

156,067 197,058 507,461 569,747 636,302 676,323

469 82 123 248 640 343

1205 321 351 1530 2060 636

0.28 0.20 0.26 0.14 0.24 0.35

763,400 782,538 898,713 1,205,785 2,889,446 8,106,876

and Bradenton almost did not attract any travel queries. The ratio of travel queries over all queries was also calculated and presented in this table. Interestingly, it seems that this ratio reflects the degree of ‘‘touristic’’ of a city, i.e., the status of being a tourist destination, which is not necessarily related to the size of the city. For example, the travel query ratio for the small city Myrtle Beach is extremely high (.68), while that for a big city like Dallas is fairly low (.14) compared to other cities with similar population. To further illustrate this point, the number of travel queries was plotted against its 2002 population in a 2-dimensional space (Fig. 4). Obviously, the ratios of travel queries are positively related to the status of the cities as a tourist destination. In other words, this shows how ‘‘touristic’’ the cities are. There are no search queries for some small cities which have little tourism content, for example, Americus or Bradenton. This graph clearly indicates Las Vegas, Orlando, Myrtle Beach, located in the upper-left half the graph, are the most ‘‘touristic’’ cities, with Las Vegas being the extreme case. Orlando, as a mid-sized city, also enjoys a high level of being ‘‘touristic’’ with almost equivalent number of travel query sessions with those of San Francisco, Chicago, and New York City. However, on the other side,

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cities like New York, Fort Worth, and Indianapolis seem to be less ‘‘touristic’’ with disproportionate low number of travel-related queries compared to their relatively large populations. The ‘‘touristic’’ level of a city can be roughly defined as the levels of reliance of the destination’s economy on the tourism economy. The concept is similar to the concept of ‘‘Tourist Ratio’’: ratio of the number of tourists to the number of residents in a specific city, which is an indication of tourist influx and has been shown to be associated with different stages of destination lifecycle (Butler, 2006; Faulkner & Tideswell, 1997; Russo, 2002; Saveriades, 2000). ‘‘Touristic’’ level refers the ratio of the relative size of the tourism industry to the local economy, while Tourist Ratio is about the ratio of the number of visitors to that of local people. The results of this study show the connections between the volume and the ratios of travel queries to the ‘‘touristic’’ level of a city. For a city which is largely depending on tourism industry, the ratio and volumes of travel queries will be higher than other less touristic cities. 5.2. Keywords in travel queries After discounting stop words and destination specific words, there are, in total, only 372 unique keywords in identified travelrelated queries. This indicates that travelers tend to use very similar keywords to form their queries. Also, the cumulative frequencies of the most frequently used keywords constitute large proportions of all the unique keywords. For instance, the top 30 most frequently used keyword represent approximately 35% of the total frequency of all unique keywords; the top 40 approximately 40%; the top 60 approximately 45%. As reflected in previous studies (Pan et al., 2007b; Xiang et al., 2009), this indicates that travelers have a great deal in common when they search for tourism products online. Among all the 18 cities, ‘‘hotel’’ stands out as the most searched keywords. The volume for ‘‘hotel’’ is almost three times than the next keyword ‘‘airport’’. ‘‘Airport’’, ‘‘casino’’, and ‘‘beach’’, all appear more than 300 times. Following those keywords are some general travel-related keywords, such as ‘‘map’’, ‘‘vacation’’, ‘‘travel’’, ‘‘park’’, and specific destinations, such as ‘‘Disney World’’. Interestingly, ‘‘restaurant’’ has only been searched around 100 times, much less than other aspects of the trip. The results indicate that accommodation and transportation are most searched aspects of a trip, followed by attractions. Dining and shopping are a lot less frequently searched. Due to space limit, the top 30 keywords are listed in Table 2. 5.3. Associations of keywords with Cities A correspondence analysis was conducted to examine the associations between search keywords and the city names. The coTable 2 Top 30 most frequently used keywords in user queries.

Fig. 4. Relationship between volumes of travel query sessions and destinations.

Keyword

Frequency

Keyword

Frequency

Hotel Airport Casino Beach Map Vacation Basketball Disney world Shuttle Volleyball Show Museum Football Airline Travel

1144 394 347 320 173 170 159 138 138 135 128 127 121 118 117

Park Resort Restaurant Art International Ticket Discount Golf Club Motel Picture Rental Theatre Bar Entertainment

112 108 106 98 91 89 88 83 73 68 68 66 64 60 60

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Fig. 5. Correspondence map of association between keywords and destinations.

occurrence between those keywords and cities names was used to measure the level of associations, and a co-occurrence matrix was constructed using the top 60 keywords and top 10 city names. The results (see Fig. 5) clearly indicate that certain destinations are associated with specific keywords, reflecting travelers’ knowledge about the cities as well as the competing space for those. It seems that these 18 destinations can be roughly grouped into three clusters in terms of the ways they are related to these keywords in a two dimensional space: the first type is Las Vegas, which is more closely associated with ‘‘show’’, ’’casino’’, ’’palace’’, and ‘‘entertainment’’; the second type is Orlando, which is highly associated with ‘‘disney world’’, ’’camp’’, ’’shuttle’’, and ‘‘airport’’; the third type, including cities such as Chicago, San Francisco, New York City, Dallas, Myrtle Beach, San Jose, Memphis, and Baltimore, are all large or medium-sized cities with similar attractions, for example, museum, art, history, festival, and events. Thus, they are clustered together on the map. In addition, the two dimensions, namely F1 and F2, seem to reflect the types of tourist attractions within these cities and their touristic level, respectively. For instance, along the first dimension (F1) Orlando represents family-oriented tourism products while Las Vegas entertainment-based ones. The majority of the cities are situated in-between, suggesting the diversity of their attractions. Along the other dimension (F2) Orlando and Las Vegas are located far away from the rest, indicating these two destinations are more ‘‘touristic’’ than others. To further show how these destinations are similar to, and different from, each other, the top search keywords for the 18 destinations were identified and presented in Table 3. Due to the limitation of the space, it was decided to list a maximum of 15 keywords. Please note that the numbers of top keywords are different for each city, since keywords with equal number of frequencies were also kept. The first observation on these keywords is that ‘‘hotel’’ is the most frequently searched one across all the large cities except in the cases of Dallas and Indianapolis. If

discounting the state name (i.e., ‘‘Texas’’ and ‘‘Indiana’’) in the searches related to these two cities, ‘‘hotel’’ then becomes the number one most frequently used keyword among all large cities. Even among the medium-sized cities, after discounting the state names, ‘‘hotel’’ remains the number one search for cities like Memphis, Las Vegas, and Orlando. The second observation is that other top keywords are different from each other among these cities. For example, it is ‘‘casino’’ in the case of Las Vegas, ‘‘golf’’ in Myrtle Beach, and ‘‘museum’’ in Chicago. Thirdly, it seems that, for those ‘‘touristic’’ cities, such as Las Vegas, Orlando, and Myrtle Beach, there are more searches on their specific attractions, while for the large cities, such as New York City, Chicago, and San Francisco, there are more searches on the location, transportation, map, and general attractions (park, museum, city, etc.). Lastly, travel were also searched in combination of our searches such as ‘‘attorney’’, ‘‘furniture’’, or ‘‘jobs’’, indicating that searchers might be multi-tasking within one query session or these words reflected their search goals (e.g., one might go to New York city to visit an attorney) while planning trips. 6. Conclusions and implications Due to their increased use for travel planning, search engines are becoming an important channel for tourist destinations and businesses to communicate with their potential visitors (TIA, 2005, 2008). And, search engine marketing is gaining the status as one of the major online marketing strategy for many destinations. A successful SEM program requires a thorough understanding of travelers’ search behaviour on the Internet. Built upon a number of previous studies, this study examined the nature of travel queries within a multiple destination setting by comparing and contrasting travel queries on a set of U.S. cities extracted from transaction logs on a number of general purpose search engines. As a result, this study provides new insights into the nature of travel queries in

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Table 3 Top queries for different cities. Large cities New York City

Chicago

Dallas

San Jose

Indianapolis

San Francisco

Hotel New york Map Museum Park City Vacation Guide Manhattan Restaurant Lawyers Dallas Rentals Attorneys Brooklyn

Hotel Art Museum Illinois Airport City Restaurant Theatre Institute Map Airline Park Cleveland Travel American

Texas Hotel Tx Attorneys Lawyers Fort worth Orlando Malpractice Airline Vacation Airport Surgery New City County

Hotel California Airport City Zoo Map Costa rica Ca Arena New Park Ticket State Ice Jobs

Indiana Hotel Airport Address Map American Wedding Flags Usa Directory Banquet Theatre Email Search History

Hotel California Bay Airport Area International City Map Sea Tourist Travel Lions Restaurant Ticket Theatre

Medium Cities Memphis

Baltimore

Fort Worth

Las Vegas

Orlando

Chattanooga

Tn Hotel Tennessee Nl Map Washington Motel Sale Hall Luxery University International West State Georgetown

Maryland Airport Hotel Harbor Washington City Aquarium Inner County Bwi Club Sun Ticket Art Zoo

Texas Dallas Museum Airport Tx Hotel Airline Art Club Fishing American Settlement White Sea Inn

Hotel Casino Miami Nevada Show Vacation Paris Entertainment Travel City Circus Palace Airline Hilton Packages

Hotel Airport Florida Shuttle Disney world Credit Furniture Office Discount Vacation Used Repair New orleans Las vegas

Tn Tenn Inn Travel Chatanooga Lauderdale Online Cvb Hotel Tennessee Park Beach Motel Restaurant Car

Small cities Pueblo

Champaign

Bradenton

Aiken

Myrtle Beach

Americus

Hotel Colorado Puerto rico Bonito Mexico Resort Anasazi Castles Indians Wireless Beach Elisa Magellan Pacheco Rentals

Il Illinois Leonardo Foster Bifurcate Expand Piano Serviette Heartbreak

Florida Coast Gulf Tampa Newspaper Herald Vacation Resort Beach Businesses North Bungalow Cvb

Lodging Woodside plantation Sc Phh Leasing Motel

South carolina Golf Resort Hotel Beach Vacation Rentals North Courses Ocean Spas Casino Inn Airport

None

search engines and offers important implications for search engine marketing. 6.1. Nature of travel queries First, this study further elucidates the nature of travel queries through the analysis of search engine log data on multiple tourist destinations. Search sessions containing these queries are usually very short and consist of one to three queries. The study also reveals the commonalities and differences of travel queries on cities in the United States. Generally speaking, there are a relatively small number of keywords commonly used by online travelers to look for information about these destinations. This is consistent with Xiang et al.’s (2009) finding that there is a ‘‘core’’ of the ‘‘tourist things’’ searched by travelers about a destination, which is commonly

shared by different destinations and reflects the multi-faceted nature of travel decisions (Dellaert, Ettema, & Lindh, 1998; Sirakaya & Woodside, 2005). In this particular case, accommodations and transportations are the most searched information. However, there seems to be differences in the ‘‘things’’ being searched depending upon the size of the destination and the ‘‘touristic’’ level. For large metropolitan cities, in addition to accommodation related queries, searches for transportation, maps, parks, and attractions and other general keywords dominate travel queries; for middle to smaller sized tourist cities, more searches are focusing on specific attractions on those cities. This demonstrated that a traveler would like to know first how to ‘‘get around’’ a big city like New York or Chicago when planning a trip. Consistent with Wo¨ber’s (2007) findings, there are strong associations between keywords used by online travelers and specific destinations, reflecting the knowledge about

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a particular destination. As shown in the correspondence analysis, the results indicate that those cities can be distinguished by the activities they offer and their ‘‘touristic’’ level. Second, this study established a preliminary understanding of the ‘‘search economy’’ on the Internet. As can be seen from the results, the volume of searches seems to be a direct indicator of how ‘‘touristic’’ cities are. The study also shows the interesting relationship between the ratio of travel queries (i.e., volume of travel queries to volume of all queries about a city) and city population. That is, the more ‘‘touristic’’ a city is, the more likely there are higher percentages of travel-related queries about that city. It makes intuitive sense in that more travelers will search for those cities, compared to the residents in that city. Logically, the travel search volume apparently reflects the ‘‘size’’ of tourism industry. Thus, there seems to be a direct link between the ‘‘search economy’’ on the Internet and the tourism industry. Third, this study further demonstrated the emergent role of travel queries as data to understand online travel behaviour. Travel queries are a valuable source of rich and authentic data that reflect travelers’ experience, knowledge, as well as their information needs pertaining to a specific destination. Considering that today’s Internet based technologies, particularly the so-called Web 2.0, support an exponential growth in text data created in the context of tourism, this study shows that there are many opportunities for the tourism research community as well as the industry to understand travel behaviour through many existing and emergent forms of ‘‘language of tourism’’ (Dann, 1997).

6.2. Implications for search engine marketing for destinations It is argued that travel queries hold the key to understanding search engine marketing strategies because they reflect travelers’ information needs, search intent, and search strategies. This study revealed the general patterns in travel-related queries, and it offers several useful insights into search engine marketing for tourist destinations, such as focusing or bidding on certain queries to increase visibility and differentiate the positioning of certain metropolitan cities. First, travel search sessions are usually very short, consisting of fewer than four queries for most sessions. This indicates that it is extremely important for marketers to attract search engine users’ attention in very short period of time (Gladwell, 2005). This confirms the importance of ‘‘first impression’’, as suggested by Kim and Fesenmaier (2008). Those first impressions are created during online travelers’ exposure to the persuasive messages in search results retrieved by search engines. Second, this study reveals that accommodation related keywords are among the most frequently used queries by online travelers. While providing information about accommodation is, perhaps, not the task with the highest priority for destination marketing organizations (DMOs), the results of this study indicate that DMOs may need to reconsider their both search engine optimization and advertising strategies. For example, incorporating and embedding accommodation related content into their websites should increase the rankings of their websites in search engines and, thus, the likelihood to be visited by online searchers. From a search engine advertising standpoint, it might be worthwhile for them to ‘‘retool’’ their keywords purchasing or ads placement practices. Third, this study shows that it requires different types of destinations to adopt different strategies that best suit the information needs of online travelers. As shown by this study, large scale and less ‘‘touristic’’ cities need to focus more on functional searches on transportation, map, and general keywords on attractions; more ‘‘touristic’’ cities (e.g., Las Vegas, Orlando, and Myrtle Beach) can

focus on keywords on specific attractions and their tourist highlights. Fourth, the analysis of query keywords in their associations with destinations revealed the competing space in online search for certain tourist destinations. Particularly, for metropolitan cities with historically and naturally endowed resources (e.g., New York City, Chicago, and Philadelphia, etc) there are potentially a large number of destinations with similar products. They are similar to each other in the minds of online travelers and, consequently, the queries they use to search. These destinations must identify ways to better position and/or distinguish themselves in the search engine market. Finally, due to possible links between the volumes of search and the tourism economy, tourist destinations and DMOs need to track and monitor search volume carefully. Potentially, this allows DMOs to gain a better understanding of the dynamics in online search, e.g., how travelers information needs and interests change over time (e.g., due to seasonality) and in what ways they respond to change in the tourism supply (e.g., due to certain events). This allows DMOs to plan, both in short term and long term, for more effective search engine marketing strategies with better informed market intelligence. 7. Limitations and future research Given its exploratory nature this study has a number of limitations. Particularly, this study utilized a fairly small sample of cities to represent tourist destinations in the United States. As a result, the generalizability of the study is limited and the results should be interpreted with caution. Also, this study utilized cross-sectional data from a number of search engine transaction logs generated at different times across a number of years. As such, there might be a certain degree of ‘‘noise’’ in the data that reflects the seasonality in travel queries. In addition, some datasets are almost nine years old. Although the nature of travel queries, particularly in terms of its format and basic utilitarian focus, might have not changed, it is more desirable to validate the findings of the study, especially specific claims about the types of travel-related information. In order to address these limitations, future research can utilize a larger sample of destinations that can truly represent the population of cities in the United States. More current search engine log data need to be incorporated into the inquiry of travelers’ use of search engines. For example, Google Keyword Tool (https:// adwords.google.com/select/KeywordToolExternal) could be one method to study recent keywords volumes. In addition, to help tourist destination improve their search engine marketing programs, certain tools, both theoretical and practical, must be developed to allow DMOs to keep track the dynamics in online search market, particularly the use of keywords in the competing information space on search engines, in order to effectively respond to market competition and change. Acknowledgement We would like to thank Dr. Daniel Fesenmaier for generously sharing his insights and critical comments when we were working on this paper. References Barbaro, M., & Zeller, T. (2006). A face is exposed for AOL searcher no. 4417749. New York Times. http://www.nytimes.com/2006/08/09/technology/09aol.html?ex¼131277 6000# Retrieved August 9, from. Beldona, S. (2005). Cohort analysis of online travel information search behavior: 1995–2000. Journal of Travel Research, 44(2), 135–142. Broder, A. (2002). A taxonomy of web search. SIGIR Forum, 36(2), 3–10.

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