1. USING DATA MINING FOR
TRAVEL AND TOURISM RESEARCH
Hian Chye Koh
School of Business, SIM University
461 Clementi Road, Singapore 599491
School of Business, SIM University
461 Clementi Road, Singapore 599491
Data mining can be defined as the process of analyzing mostly large data sets to
explore and discover previously unknown patterns, trends and relationships to
generate information for better decision making. It has been argued that in today’s
fast-paced and competitive environment, for travel and tourism businesses to increase
their market share and maintain leadership, these businesses would be hard-pressed not
to use data mining tools and techniques to develop, manage and market tourism products
and services. In recent years, researchers and practitioners have called for the use of data
mining to aid destination management and planning and marketing, market
segmentation, customer relationship management and churn modeling. Without doubt,
data mining can help travel and tourism businesses to survive, gain a competitive
advantage and grow. The objective of this paper is to discuss and illustrate data mining
and its application in travel and tourism research.
Keywords: Data mining; association analysis; predictive modeling; travel and tourism
Data mining, as used today, can be considered a relatively recently developed
methodology and technology, coming into prominence only in 1994 (Trybula, 1997). It
uses techniques from the disciplines of statistics/mathematics, machine learning and
artificial intelligence. It aims to identify valid, novel, potentially useful and
understandable correlations and patterns in data (Chung & Gray, 1999) by combing
through copious data sets to sniff out patterns and relationships that are too subtle or
complex for humans to detect (Kreuze, 2001).
Since the mid-1990’s, data mining has been used intensively and extensively by
financial institutions (e.g., for credit scoring and fraud detection), marketers (e.g., for
direct marketing and cross-/up-selling), retailers (e.g., for market segmentation and
store layout) and manufacturers (e.g., for quality control and maintenance
scheduling), among others. The increasing popularity and application of data mining
can be explained by a few important developments in the last decade.
2. Firstly, advances in both computer hardware and software have made many data
mining applications more accessible and affordable to businesses now than ever
before. These include cheaper and more powerful computers and more user-friendly,
comprehensive and advanced data mining software. Secondly, with the data
explosion experienced by many organizations collecting increasingly larger amounts
of data (e.g., on transactions and customers), organizations have begun to realize that
data are not useful for decision making unless they can be transformed into
information, which is an important asset. In this respect, data mining provides the
means to analyze large databases to generate valuable business information. Thirdly,
success stories of data mining applications reported by businesses as well as
aggressive marketing by data mining consultants and software vendors have resulted
in increasing numbers of organizations jumping onto the data mining bandwagon. As
stated by Davis (1999), more companies are now using data mining as the foundation
for strategies that help them outsmart competitors, identify new customers and lower
The objective of this paper is to discuss and illustrate data mining and its application in
travel and tourism research.
DATA MINING METHODOLOGY AND TOOLS
The methodology of data mining can be broadly divided into three major stages: pre-
modeling, modeling and post-modeling (see Figure 1).
The first step in data mining is to identify the business problem. This is critical as one
important reason for failure in data mining is the overemphasis on data analysis at the
expense of the business problem to be addressed. However, stating a business
problem by itself does not automatically suggest a data mining application. For
business problems that are appropriate for data mining, it is necessary to translate the
business problem into a data mining application. This is the second step in the pre-
The third step is to assess the data needed and available for the data mining
application. Data can come from internal sources (e.g., existing customer or
transaction records) or external sources (e.g., data compiled from marketing surveys).
If the needed data are not available, then they will have to be obtained (e.g., by
purchasing the data from data providers) or generated (e.g., by running a test
The fourth and last step in the pre-modeling stage is also the most tedious step –
preparation of the data for mining. In many instances, the necessary data are available
but not in the same database or in the same standard format. Efforts will then have to
be made to extract and combine data from different databases or sources and make
them consistent. More importantly, existing data may be incomplete or may contain
errors. These data problems have to be dealt with too (say, by filling in missing data
from other sources and correcting erroneous data).
3. FIGURE 1. DATA MINING METHODOLOGY
1. Identification of Business Problem/Issue
2. Translation of Business Problem/Issue into Data Mining
3. Assessment of Data
4. Preparation of Data for Mining
1. Identification of Appropriate Tools/Techniques
2. Analysis of Data
3. Assessment of Results
4. Identification of Final Model
1. Deployment of Data Mining Model/Results
2. Tracking of Performance
The modeling stage can be deemed to be the core of data mining. This is the stage
where data analysis is performed. Generally, for any data mining application, several
data mining tools or techniques can be used (e.g., logistic regression, neural networks
or decision trees can be used for churn modeling to determine customer turnover).
Thus, the first step in the modeling stage is to identify the appropriate tools or
techniques to use. Once the appropriate tools or techniques are identified, the next
step is to perform the actual data analysis (or modeling).
After the analysis, it is necessary to assess the results (the third step of the modeling
stage). This relates to the objective of the data mining application. For example, if
the objective is to perform market segmentation, then it is necessary to assess if the
clustering results lead to interpretable, useful and actionable market segments.
Assessment of results can be statistical in nature too (e.g., evaluating statistical
significance). Data mining is an iterative process. Hence, the assessment may lead to
a re-selection of the variables or a re-run of the analysis … etc.
Finally, if two or more models give acceptable results, then there is a need to identify
the final (or best) model. Thus, the different acceptable models can be compared with
respect to their accuracy rates and the one that is most accurate can be selected as the
4. Post-modeling Stage
The post-modeling stage relates to the actions to be taken after the data analysis is
completed. The first step is the deployment of the data mining model or results.
What this involves depends on the objective of the data mining application. For
example, if the objective is market segmentation, then the clustering results may be
used as inputs for decision making (e.g., designing new products for different market
segments, reaching different market segments through different advertisement
channels, formulating different strategies for different market segments … etc.).
The last step is the tracking of performance. This is necessary because of changes in
the environment in which an organization operates. Such changes may lead to a
deterioration of the performance of data mining models, which may be dated. For
instance, the variables and relationships that help predict a target variable may change
over time (e.g., response to a mailing campaign) and a data mining model constructed
in the past may no longer be useful at present. Hence, tracking is important as
deteriorating performance may signal the need to look at the data mining model again
and to build an updated model if necessary.
Finally, the double-headed arrows connecting the three stages of the data mining
methodology in Figure 1 indicate that data mining is an interactive and iterative
process. Very often, it is necessary to move back and forth among stages or steps
when developing a data mining application. For example, poor modeling results may
mean looking at the data again. Hence, data mining is not a strictly sequential
Data Mining Tools
Data mining tools can be broadly classified based on what they can do, namely: (1)
description and visualization; (2) association and clustering; and (3) classification and
estimation (i.e., predictive modeling). Some authors (e.g., Berry & Linoff, 2000) have
classified data mining tools into more detailed categories.
Description and visualization can contribute greatly towards understanding a data set
(especially a large one) and detecting hidden patterns in the data (especially complicated
data containing complex interactions and non-linear relationships). As such, they are
frequently performed before modeling is attempted in order to understand and/or detect
relationships among variables. Description and visualization also help greatly in the
summarization of data and in the presentation and reporting of results.
Description refers to the summarization of data to facilitate understanding. An example
of description is the profiling of data sets in order to understand their characteristics,
similarities and differences. Standard description tools include summary statistics such
as measures of central tendency (e.g., mean), measures of dispersion (e.g., standard
deviation) and counts (e.g., cross-tabulation). Graphical approaches (e.g., distributions
and plots) can also help to describe data and the relationships in data.
Visualization can be considered an enhanced graphical approach that allows user input
and interaction. An example is a rotating multidimensional plot that permits the user to
define multiple dimensions (i.e., multiple variables) in the plot as well as the direction
5. and angle of rotation to facilitate viewing complex relationships. Colors can also
Association and Clustering
In association analysis, the objective is to determine which variables/items go together.
It is a tool that looks for groupings or patterns among a set of items. For example,
market basket analysis refers to a technique that generates probabilistic statements such
as: if a tourist visits Hong Kong, there is a 0.65 probability that he/she also visits China.
Such statements (or rules) are intuitive and easy to understand. Also, good association is
expected to have predictive value. However, many applications of association analysis
are only exploratory in nature, with a view to better understand groupings and patterns in
the data set. Association rules can be useful for items bundling, discount and promotion
decisions, cross-selling … etc.
Association analysis can be extended to include more sophisticated applications. For
example, time sequence can be incorporated. To increase on-line purchases, an on-line
travel agency may try to isolate the sequences of web navigation that are likely or
unlikely to lead to on-line purchases. Actions (e.g., web re-design) can then be taken to
promote sales-generating sequences/behavior.
Clustering is an exploratory technique that attempts to discover natural groupings in
data. The objective is to group similar (homogeneous) objects into the same cluster and
dissimilar (heterogeneous) objects into different clusters. Clustering is usually used to
do market segmentation and to identify the cluster profile of the different segments.
Knowing how the market is segmented and the characteristics of the different segments
helps in decisions such as an organization’s competitive positioning, the products to
market to particular market segments, and the avenues and communications that can be
used to reach the targeted segments.
The most common and important applications in data mining usually involve predictive
modeling, which can be further categorized into two major categories. Classification
refers to the prediction of a target variable that is qualitative (i.e., categorical) in nature
(e.g., predicting response versus non-response to a mailing campaign). Estimation, on
the other hand, refers to the prediction of a target variable that is quantitative (i.e.,
continuous) in nature (e.g., predicting the amount spent). Generally, predictive modeling
attempts to predict a target variable on the basis of one or more input variables. For
example, multiple regression can be used to predict the amount of tourist expenditure
based on age, gender and income.
Neural networks can also be used for predictive modeling. They can often model
complex relationships in data well. Neural networks are modeled after the human brain,
which can be perceived as a highly connected network of neurons.
Finally, decision trees can be used for predictive modeling too. They divide observations
into mutually exclusive and exhaustive subgroups based on the levels of particular input
variables that have the strongest association with the target variable. The end product
can be graphically represented by a tree-like structure (called a decision tree), which is a
6. compact explanation of the data. The end product can also be represented by explicit
decision rules. Both representations are easy to interpret and use. Decision trees can
model complex relationships reasonably well.
In practice, it is common to construct all the regression, neural network and decision
tree models and then assess the competing models to identify a final (i.e., best) model.
DATA MINING AND TRAVEL AND TOURISM RESEARCH
Several factors favor and facilitate, if not necessitate, the use of data mining for travel
and tourism research. Firstly, the travel and tourism industry is already one of the
largest users of information technology (Sheldon, 1997). Advances in information
technology affect the services and amenities offered and how they are delivered and
promoted. They also affect the organizational structure and the interactions between
customers and service providers (Olsen & Connolly, 1999). In addition, potential and
actual travellers are increasingly using sophisticated internet and communication
technology to find tours that meet their expectations and needs. Conversely, the
internet provides travel and tourism businesses a low cost and more targeted approach
to seek customers.
Secondly, as concluded by Pyo, Uysal & Chang (2002), for travel and tourism
businesses to increase their market share and maintain leadership in today’s fast-paced
and competitive environment, these businesses would be hard-pressed not to use data
mining tools and techniques to develop, manage and market tourism products and
services. Destination management and planning, and marketing need to be more
precisely targeted and more aggressive. Buhalis (1999) suggested that an integrated
knowledge of tourist characteristics, images, attitudes and preferred destination attributes
should be used to market destinations more effectively. Magnini, Honeycutt & Hodge
(2003) further suggested that hotels can use data mining to create direct mailing
campaigns, plan seasonal promotions, plan the timing and placement of advertisement
campaigns, create personalized advertisements, define which market segments are
growing most rapidly, and determine the number of rooms to reserve for wholesale
customers and business travellers. Several other data mining applications for the travel
and tourism industry have also been suggested by Pyo, Uysal & Chang (2002).
Accordingly, data mining is one of the most important ways to help travel and tourism
businesses to survive, gain a competitive advantage and grow.
Thirdly, there is a data explosion in the travel and tourism industry. The proliferation
of centralized reservation and property-management systems has resulted in large
amounts of consumer data for hotel corporations (Magnini, Honeycutt & Hodge,
2003). At the same time, there is also more access to more data. Beirne (2000)
reported that Cendant marketers could access a data warehouse holding data gleaned
from the loyalty programs of Cendant’s eight hotel brands, Avis Rental Car and
Resort Condominiums International. They could also eventually gain access to
Cendant’s direct marketing companies such as AutoVantage, Privacy Guard,
Shoppers Advantage and Traveller Advantage. A similar initiative has earlier been
reported for Hilton Hotels Corporation and Hilton International (Anonymous, 1999).
Without doubt, these massive data have to be transformed into information for
decision-making and business intelligence before they can be an asset to businesses.
7. The recent travel and tourism literature documents several cases of data mining
applications. The range and scope of these applications are rather diverse. Similar to
the earlier classification of data mining tools, these applications can be classified into
two broad categories: association and clustering; and classification and estimation (i.e.,
No application is classified under the category of description and visualization as this is
seldom an end in itself. Description and visualization results are frequently used to
understand the data and the patterns and relationships among them better so as to
facilitate further analysis. Despite this, description and visualization are very important
data mining tools. Small (2000), for example, used pie charts and histograms to show
the demographic profile of leisure travelers who use travel agents, the travel
characteristics of leisure travellers, and the various sources of information used by
travellers when planning for a trip.
Association and Clustering
Dev, Klein & Fisher (1996) and d’Hauteserre (2000) provided two interesting
applications of association analysis. The first relates to the market basket analysis of
hotels, airlines, car rental companies and other products and services among tourists
for the purpose of partner selection for marketing alliances. The second involves
casino resort destinations supplying various amenities and attractions in addition to
gaming facilities. In this situation, the market basket analysis of the preferred
products among potential visitors is an important analysis to perform before an
investment is made.
On clustering, Lau, Lee, Lam & Ho (2001) suggested segmenting potential travellers
into different clusters based on personal information mined from their personal web
sites. This would enable travel and tourism businesses to understand the potential
travellers’ interests and needs, and hence be able to offer (via email) specially
Predictive modeling applications in the travel and tourism industry include customer
relationship management, cross-/-up selling and churn modeling. For example, data
mining can be performed to identify potential tourists who are likely to respond to
direct mailing campaigns, guests who under- or over-stay their reservation, or the type
of rooms/services that a guest prefers (Kasavana & Knutson, 1999). Also, as reported
by Magnini, Honeycutt & Hodge (2003), data mining has been used by Harrah’s
hotels and casinos to predict the potential value of each customer, design marketing
strategies to retain customers and generate information for customer relationship
8. Pyo, Uysal & Chang (2002) discussed a data mining application to identify visitors to
Cheju Island (South Korea) who would, upon returning home, recommend to their
friends and relatives to visit the island. Stepwise regression was used to regress the
intention to recommend (i.e., the target variable) on input variables such as
expectations, perceptions, image, experience and demographic variables. The model
would be used by the Department of Tourism of the Cheju Provincial Government to
distribute appropriately designed promotional material to selected visitors.
Finally, Min, Min & Emam (2002) presented a case study involving 11 luxurious
hotels in Seoul (South Korea). It detailed the use of decision trees to understand
customers’ preferences and interactions with customers in order to build and maintain
a loyal customer relationship.
ILLUSTRATIONS OF DATA MINING APPLICATIONS
From the above, it can be concluded that data mining can contribute greatly to travel
and tourism research and to the success of the industry. To illustrate the use of data
mining, consider a fictitious company Best Travel Agency (abbreviated as Best from
this point to facilitate discussion).
Illustration One: Developing Tour Packages
Best has observed an increasing number of tourists going to Beijing (China) from the
US and wants to capture a greater share of this tourist segment. Currently, the
itineraries for Beijing tours cover a long list of the most common and popular tourist
attractions. Recent feedback from Best’s customers has indicated that not all these
attractions appeal to all customers. Subgroups of customers seem to be interested in
only particular subsets of attractions.
In addition, many of Best’s customers who are keen to visit other Chinese cities want
to have a brief 1- or 2-day stopover in Beijing. To improve its China tour packages,
Best wants to find out which tourist attractions in Beijing can be grouped into subsets
that would appeal of different subgroups of customers. Best is considering offering a
basic China tour package (outside of Beijing) that incorporates different options to
additional attractions in Beijing for different subgroups of customers. For this brief
stopover in Beijing, only two or three tourist attractions are feasible.
After conducting several focus group sessions, the following attractions appear to be
the ten most popular places of interest for the Beijing options: (1) Forbidden City; (2)
Great Wall of China; (3) Temple of Heaven; (4) Summer Palace; (5) Ming Tombs; (6)
Hutong and Courtyard; (7) Cultural Village; (8) Beihai Park; (9) Tiananmen Square;
and (10) Beijing Art Museum.
To develop the Beijing options further, Best has engaged a marketing research firm in
China to conduct a survey of 2000 tourists who have visited Beijing on tour packages
of at least five days. As the survey is conducted at the Beijing International Airport, it
is kept very simple by merely asking the respondents if they have greatly enjoyed
visiting the ten tourist attractions listed above to the extent of wanting to visit them
again and/or to recommend them to their relatives and friends. A favourable response
9. for an attraction is taken as an indication of the appropriateness of the attraction for
inclusion in the Beijing options. The data collected are then entered into an SPSS file
for analysis. SPSS Clementine (a very user-friendly data mining software) is used to
do the data mining.
The distribution results (see the top panel in Figure 2) show that the five most popular
attractions (in descending order of popularity) are: (1) Forbidden City, (2) Summer
Palace, (3) Cultural Village, (4) Great Wall of China, and (5) Ming Tombs. Further
analysis is performed using the SPSS Clementine Apriori algorithm. The association
analysis results are shown in the bottom panel in Figure 2.
The association rules can be interpreted as follows. Consider the first association rule
listed, which is reproduced below:
Instances Support Confidence Lift Consequent Antecedent
1014 50.70 90.500 1.772 Summer Palace Cultural Village
The rule can be translated to mean that:
(1) Out of the 2000 tourists surveyed, 1014 (or 50.70%) would want to visit
Cultural Village again and/or would recommend Cultural Village to their
relatives and friends.
(2) Out of these 1014 tourists, about 90.50% (or approximately 918 of them)
would also want to visit Summer Palace again and/or would recommend
Summer Palace to their relatives and friends.
(3) Out of the 2000 tourists surveyed, 1022 (or 51.10%) would want to visit
Summer Palace again and/or would recommend Summer Palace to their
relatives and friends [this can be seen from the second association rule in the
panel]. Out of the 1014 tourists mentioned in (2) above, 90.50% would do so.
Hence, the lift value is 90.50/51.10 or 1.772. Generally, a higher lift value
indicates a more effective association rule.
The other association rules can be interpreted similarly.
Overall, the association rules suggest the following two Beijing options: (1) Summer
Place, Cultural Village and Ming Tombs, and (2) Great Wall of China and Forbidden
City. These two options are expected to add the greatest value as a Beijing extension
to the regular China tour packages outside of Beijing.
Illustration Two: Target Mailing Campaign
Best has conducted a mailing campaign a year ago to promote a new tour package to
China. The promotion brochure was sent randomly to 2500 customers and 831
customers responded, giving a very good response rate of 33.24%. The data related to
this mailing campaign have been entered into a SPSS data set. Best is now
considering a target mailing campaign for a similar but new tour package to China. It
believes that the patterns and relationships in the data can help it improve the response
rate and reduce the cost of the campaign by targeting potential customers better.
10. FIGURE 2. GROUPINGS OF ATTRACTIONS IN BEIJING
Note: Reproduced by permission of SPSS Inc.
The following demographic characteristics are captured in the database: (1) gender;
(2) age group, (3) educational level, and (4) occupation. In addition, the following
data are also captured: (5) response to the mailing campaign, (6) whether the
customer has taken any tour to China within six months before the mailing campaign,
(7) whether the customer has taken any tour to Hong Kong within six months before
the mailing campaign, (8) whether the customer has taken any tour within six months
before the mailing campaign to countries other than China and Hong Kong, and (9)
the number of times the customer has traveled with the agency during the past three
Given the ease of interpretation of decision tree results, Best has decided to construct
a decision tree that can explain/predict customer response. SPSS Clementine is used
to construct the decision tree. The results are summarized in Figure 3.
11. FIGURE 3. DECISION TREE RESULTS
Note: Reproduced by permission of SPSS Inc.
The results show that customers who have responded positively to the last mailing
campaign for the China tour package are likely to be: (1) customers who are above 45
years old, female and who have taken a tour within six months before the mailing
campaign to countries other than China or Hong Kong [node 10 in Figure 3]; (2)
customers who are above 45 years old and who have neither taken a tour within six
months before the mailing campaign to countries outside of China or Hong Kong nor
to China [node 8]; and (3) customers who are above 45 years old, female and who
12. have taken a tour within six months before the mailing campaign to China but not to
countries outside of China or Hong Kong [node 12].
On the other hand, the results show that customers who have responded negatively to
the last mailing campaign for the China tour package are likely to be: (1) customers
who are aged 45 years and below [node 2]; (2) customers who are above 45 years old
and who have taken a tour within six months before the mailing campaign to Hong
Kong as well as countries outside of China and Hong Kong [node 5]; (3) customers
who are above 45 years old and male and who have taken a tour within six months
before the mailing campaign to countries outside of China or Hong Kong but not to
Hong Kong [node 9]; and (4) customers who are above 45 years old, male and who
have taken a tour within six months before the mailing campaign to China but not to
countries outside of China or Hong Kong [node 11].
Except for the variables appearing in the decision tree, the other input variables (i.e.,
educational level, occupation and number of tours in the past three years) do not
appear to be associated with the response to the last mailing campaign.
The decision tree results are expected to be useful to Best in its next mailing
campaign in targeting the right customers. In this application, it is assumed that Best
has an up-to-date database of its customers with respect to the variables captured in
the decision tree.
Data mining can be defined as the process of analyzing mostly large data sets to
explore and discover previously unknown patterns, trends and relationships to
generate information for better decision-making. It is a very powerful and useful
methodology and technology. For the travel and tourism industry, data mining can
contribute greatly to the ability of travel and tourism businesses to gain a competitive
advantage and grow.
However, data mining is not without limitations. Some of the major ones are
highlighted below. Firstly, the quality of data mining results and applications depends
on the availability and quality of data (Chopoorian, Witherell, Khalil &, 2001). Also,
the data needed for data mining often exist in different settings and data systems.
Hence, they have to be collected and integrated before data mining can be done. In
addition, for the data available, problems such as missing data, corrupted data,
inconsistent data … etc. have to be resolved before mining is done. It has been
estimated that data preparation comprises about 75% of the resources needed for a data
Secondly, a sufficiently exhaustive mining of data may throw up patterns of some kind
that are a product of random fluctuations (Hand, 1998). This is especially so for large
data sets with many variables. Murray (1997) and Hand (1998) have warned against
using data mining for data dredging or fishing (i.e., randomly trawling through data in
the hope of identifying patterns). Data mining performed in a mechanical manner will
not guarantee results or success. There is a need for human intervention,
interpretation and judgment (Pyo, Uysal & Chang, 2002).
13. Thirdly, the successful application of data mining requires the user to be knowledgeable
in the domain area of application as well as in the data mining methodology and tools.
Domain area knowledge is important as it is critical to identifying the appropriate
business issues for which to develop data mining applications (Pyo, Uysal & Chang,
2002). Also, it is essential for specifying the appropriate models and correctly
interpreting the results. In addition, without a sufficient knowledge of data mining
methodology and tools, the user may not be aware of or be able to avoid the pitfalls of
data mining (see, for example, McQueen & Thorley, 1999).
IT (i.e., information technology) expertise is also necessary for tasks such as the
extraction and preparation of data for mining and the deployment of data mining models
(e.g., to embed the models into transactional systems). Further, statistical and research
expertise is essential as a data mining project can be considered as a large-scale research
project that involves a lot of statistical and research issues. Therefore, collectively, the
data mining team should possess the following knowledge and skills: domain
knowledge, data mining knowledge and skills, IT expertise, and statistical and research
expertise (see also Magnini, Honeycutt & Hodge, 2003).
Finally, organizations developing data mining applications need to make a substantial
investment of their resources (e.g., time, effort and money) in data mining. Data mining
projects can fail for a variety of reasons such as lack of management support and
organizational commitment, unrealistic user expectations, poor project management,
inadequate data mining expertise … etc. Data mining requires intensive planning and
technological preparation work. In addition, top management has to be convinced of the
usefulness of data mining and be willing to change work processes, if necessary.
Further, all parties involved in the data mining effort have to collaborate and cooperate
Despite the limitations highlighted above, there is no doubt that data mining can play
a critical role in travel and tourism research. What remains is for the travel and
tourism industry and businesses to realize the potential benefits and usefulness of data
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