1. A Procedure for Target Market Selection in Tourism
SooCheong (Shawn) Jang
Alastair M. Morrison
Joseph T. O’Leary
ABSTRACT. The primary objective of this research was to evaluate the attractiveness of travel
activity segments to assist with target market selection. Prior studies evaluating segment attractive-
ness have used ranking systems to determine the best markets. However, due to a lack of precision
in these ranking procedures, they have not effectively reflected the degree to which one segment is
more attractive than the others. This research attempted to provide more quantifiable and sophisti-
cated evaluation criteria. Using mean expenditure, expenditure risk, segment size, and segment
size risk as the evaluation criteria, the resulting segments were assessed and compared. Three
risk-adjusted indexes were also introduced to simultaneously consider expenditure level, market
segment size, market potential, and risk in the process of segment attractiveness evaluation. The
target market selection procedure suggested should be helpful to marketers who are most con-
cerned with the market and economic potential of available travel segments. [Article copies avail-
able for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address:
<docdelivery@haworthpress.com> Website: <http://www.HaworthPress.com> © 2004 by The Haworth Press,
Inc. All rights reserved.]
KEYWORDS. Target market selection, segment attractiveness, activity segmentation, travel
expenditure, cluster analysis
INTRODUCTION travelers. The main objectives in selecting tar-
get markets are to maximize the effectiveness
Target market selection is an important step of marketing programs, to more efficiently
in establishing a marketing strategy. A target utilize limited marketing resources and bud-
market in tourism is a market segment that a gets, and to produce the greatest economic
travel organization decides to serve and it con- benefits.
sists of travelers who share common charac- Target marketing is becoming more crucial
teristics (Kotler, Bowen, & Makens, 1999). in tourism today. With higher disposable in-
Rarely, if ever, can one tourism destination or comes and more time, coupled with a much
organization succeed in being all things to all greater variety of tourism offerings, travelers
SooCheong (Shawn) Jang is Assistant Professor, Department of Hotel, Restaurant, Institutional Management
and Dietetics, Kansas State University, Alastair M. Morrison is Professor, Department of Hospitality and Tourism
Management, Purdue University, Joseph T. O’Leary is Professor and Head, Department of Recreation, Park &
Tourism Sciences, Texas A&M University.
Address correspondence to: SooCheong (Shawn) Jang, Department of Hotel, Restaurant, Institutional Manage-
ment and Dietetics, Kansas State University, Manhattan, KS 66506-1404 (E-mail: jangs@ksu.edu).
The authors express their appreciation to the Canadian Tourism Commission (CTC) for providing the 1998
French Pleasure Travel Markets to North America data set. Price Waterhouse Coopers collected the data used in this
study. Neither the collector of the original data nor the CTC are responsible for the interpretations reported here.
Journal of Travel & Tourism Marketing, Vol. 16(1) 2004
http://www.haworthpress.com/web/JTTM
 2004 by The Haworth Press, Inc. All rights reserved.
Digital Object Identifier: 10.1300/J073v16n01_02 17

2. 18 JOURNAL OF TRAVEL & TOURISM MARKETING
now demand a much wider range of travel ex- compared. A study by Spotts and Mahoney
periences. The immediate challenge for tour- (1993) supported the proposition that there is a
ism organizations is how to meet these diversi- close relationship between travel activities
fied traveler needs. Target marketing requires a and expenditures. As such, it may be possible
strong focus on specific traveler groups and for tourism marketers to develop appropriate
tailor-making products to meet their unique products for their selected target markets and
demands. By targeting, tourism marketers with to estimate the economic benefits of each
limited resources increase their probability of product through activity segmentation. For
marketing success, and are more likely to these reasons, this research employed travel
achieve their marketing objectives. However, activities as the segmentation base to analyze
as travel markets continue to splinter and be- a specific travel origin market (France).
come more complex, one of the greatest chal- Middleton and Clarke (2001) define market
lenges is to select the best set of market seg- segmentation as the process of dividing a total
ments. This research addressed this challenge market such as all visitors, or a market sector
by providing a procedure for evaluating the at- such as holiday travel, into sub-groups for mar-
tractiveness of individual travel market seg- keting management purposes. The resulting
ments. segments are assumed to have homogeneous
The process leading to target market selection travel behaviors. The other requirement of mar-
should involve five sequential steps: (1) deter- ket segmentation is to find meaningful differ-
mining segmentation variable or variables; ences among the segments within a total mar-
(2) conducting market segmentation; (3) pro- ket. This process provides tourism marketers
filing identified segments; (4) evaluating seg- with a greater understanding of individual mar-
ment attractiveness; and (5) selecting target kets and more precise ideas for product devel-
market(s) (Figure 1). opment. One of the common ways to identify
Many variables have been recommended as the differences is to profile the segments of the
viable segmentation bases, but researchers total market. Profiling helps by distinguishing
seem to agree that there is no single ideal seg- the attitudes, behaviors, socio-demographics,
mentation base that fits in every situation travel planning patterns, and trip-related char-
(Morrison, 2002). However, several research- acteristics of travel market segments.
ers have suggested that travel activity segmen- The evaluation of market segments is the
tation is one of the best segmentation bases for step before target market selection and is criti-
tourism (Choi and Tsang, 1999; Hsieh, O’Leary, cal to the potential success of a marketing
and Morrison, 1992; Rao, Thomas, and Javalgi, strategy. The key issue is which segments are
1992). They argue that activity segmentation most likely to lead to the achievement of mar-
helps with the bundling of travel activities into keting goals and objectives. The answer dif-
packages with greater market appeal. The fers based upon the criteria used to evaluate
bundles or packages of preferred activities can the relative merits of each market segment.
be considered sub-aggregates of the total travel The criteria for judging segment attractiveness
market (Romsa, 1973). Another major claim include: (1) market potential; (2) competition
about activity-based segmentation is that and segment structural attractiveness; (3) mar-
travel activities can be connected with the eco- keting organization’s vision, goals, and objec-
nomic benefits to the destination. For exam- tives; (4) serviceability; and (5) costs (Heath &
ple, the expenditures of people with shopping Wall, 1992; Kotler, 1991; Kotler, Bowen, &
and bird watching as travel activities can be Makens, 1999; McKercher, 1995).
FIGURE 1. Steps in Target Market Selection
Determining Conducting Profiling Evaluating Selecting
Segmentation Market Identified Segment Target
Variable(s) Segmentation Segments Attractiveness Market(s)
Note: Adapted from Pride and Ferrell (2000).

3. Jang, Morrison, and O’Leary 19
This research applied the first criterion, position (WTO 2001). This research is ex-
market potential. A target market must satisfy pected to provide useful insights into the
the condition of substantiality, meaning that it planning, development, and marketing for in-
must be large enough to be economically via- ternational travel planners and destination mar-
ble (Kotler et al., 1999). The underlying ratio- keters by identifying activity segments of
nale here is that a market with greater market French outbound travelers and then evaluating
potential is more attractive. Thus, for a travel the resulting segments using the market poten-
destination, the market potential of the target tial and risk concepts.
market in terms of expenditures should be
viewed as one of the most important selection Research Objectives
criteria. In addition to market potential, risk is
a second factor that should be evaluated, since The main objectives of this research were
risk negatively influences the level of ex- to: (1) identify the activity segments of French
pected expenditures as frequently noted in fi- outbound travelers, (2) profile the activity seg-
nance research (Board and Sutcliffe, 1991; ments, (3) determine if there were statistical
Cardozo and Wind, 1985). Risk in this case differences across the segments in terms of
means level of uncertainty as to whether or not socio-demographic and trip-related character-
a destination can have a certain level of travel istics, (4) evaluate the activity segments on the
expenditure. That is, if the probability of at- basis of market potential and the risk, and
tracting travel expenditures is low, or the level (5) recommend the activity segment with the
of the expenditures drastically varies within a greatest market potential bearing in mind their
year or between years due to fluctuating de- risk.
mand, a market segment is not as attractive as
when it has a high probability and stable ex-
penditures. Therefore, it is suggested that mar- REVIEW OF RELATED LITERATURE
ket potential and risk of travel market seg-
ments should occupy a central position in Activity Segmentation in Tourism
evaluating segment attractiveness and select-
ing the most appropriate target markets (steps The use of travel activity as a segmentation
four and five in Figure 1). base is a relatively recent development in tour-
ism research. Using factor analysis, Bryant
The French International Travel Market and Morrison (1980) identified vacation activ-
ity preferences by six distinct traveler types:
Despite the importance of target marketing, young sports, outdoorsman hunter, winter/wa-
only a limited number of prior empirical stud- ter, resort type, sightseer, and nightlife activi-
ies were found that dealt with target market se- ties. To implement new marketing strategies
lection (Jang, Morrison, and O’Leary, 2002; in Michigan, the researchers analyzed the eco-
Loker and Perdue, 1992; McQueen and Miller, nomic impact of these activity segments using
1985). Additionally, a perusal of the literature travel expenditures and evaluated the past ad-
revealed that there has been no research on the vertising and promotional efforts. Rao et al.
evaluation of the market potential and risk of (1992) focused on the activity preferences and
travel market segments. To fill this research travel-planning behaviors of U.S. outbound
gap, while providing a useful activity segmen- pleasure travelers. Concluding that activity
tation of an important international travel mar- types might be associated with destination
ket, this research examined French outbound choices, the authors suggested that activity-
travelers. As one of the major economic pow- based segments provide destination marketers
ers in the world, France has played an impor- with valuable information on the best business
tant role through its economic contributions to opportunities and the most appropriate activi-
world tourism. The international travel expen- ties to include in product development. Using
ditures, excluding transportation, by French activity segmentation, Hsieh et al. (1992) clus-
outbound travelers amounted to $17.7 billion tered Hong Kong international pleasure trav-
in 1999, putting France on the world’s fifth elers into five groups: visiting friends and rela-

4. 20 JOURNAL OF TRAVEL & TOURISM MARKETING
tives, outdoor sports, sightseeing, full-house activity, travelers for combined business and pleasure
and entertainment. Significant statistical dif- purposes were the big spender segment.
ferences were found across the groups in These groups of studies show striking simi-
socio-demographic and trip-related variables larities in research methods, statistical analy-
such as age, education, occupation, and party ses, and even interpretation and implications.
size. The results suggested that activity seg- This past research on activity and expenditure
ments have unique socio-demographic and segmentation has provided a solid basis for fu-
trip-related characteristics, indicating the exis- ture research and marketing, but there is a
tence of distinct sub-markets. Choi and Tsang need for further innovative approaches to ex-
(1999) completed a more recent study and the tend the value of this approach. Another study
resulting segmentation scheme closely resem- seems to contribute to developing a more
bled the activity clusters of Hong Kong plea- practical understanding of activity segments
sure travelers found by Hsieh et al. (1992). The and to showing how to approach target market
researchers also used cluster analysis and they selection from the economic value perspec-
found four activity segments: sightseeing, out- tive. Spotts and Mahoney (1993) investigated
door sports, entertainment and outdoor activi- whether the characteristics of fall tourists dif-
ties, and visiting friends and relatives. As in fered from those of summer tourists. They
the Hsieh et al. study, statistically significant segmented fall tourists based on the combina-
differences were found among the activity tions of recreation activity participation and
segments in terms of socio-demographic and estimated the average per-trip and per-day
trip-related variables. The results showed that spending by activity market segment. The
most of Hong Kong’s private housing travel- study showed that it was possible to estimate
ers were young and had fairly high education the segments’ sizes and spending levels and
levels. thereby to calculate the potential economic
In relation to economic contribution to des- contributions of each segment. As Morrison
(2002) pointed out, the application of activity
tinations, a few studies using expenditure lev-
segmentation in vacation package develop-
els as their segmentation base have been con-
ment and marketing may improve profitability
ducted (Jang, Ismail, and Ham, 2002; Pizam by enhancing the appeal to specific target seg-
and Reichel, 1979; Spotts and Mahoney, 1991). ments.
Pizam and Reichel (1979) first identified de-
mographic and socioeconomic variables that Evaluation of Market Segment
differentiate between big and small spenders Attractiveness
on domestic travel in the U.S., and found that
several variables including education, marital Little previous research has been conducted
status, market value of owned home, and num- on the evaluation of travel segment attractive-
ber of cars helped to discriminate the spend- ness to support target market selection. As
ers’ segments. Spotts and Mahoney (1991) at- mentioned earlier, Bryant and Morrison (1980)
tempted to group visitors to Michigan’s Upper utilized travel expenditures to evaluate the
Peninsula into three groups and discovered levels of economic value of different activity
that heavy spenders were distinguishable in types in Michigan. They suggested that expen-
some variables such as party size, length of ditures should function as a key barometer of
travel, level of involvement in recreation ac- the level of economic contributions to a desti-
tivities, and use of information. A recent study nation. To determine segment attractiveness,
by Jang, Ismail, and Ham (2002) investigated McQueen and Miller (1985) considered the
the expenditure level of Japanese outbound profitability, variability, and accessibility of
pleasure travelers. The results showed a few segments. Profitability was calculated as the
interesting points that Japanese travelers to the relative weighted population size times the
U.S. mainland, Canada, Europe, and Oceania mean expenditures of each group. The proba-
showed greater propensity to spend when bility of revisiting the destination represented
compared to those to Asian countries, Hawaii, variability. The researchers attempted to de-
and Guam, and that honeymooners and the scribe a systematic approach for selecting tar-

5. Jang, Morrison, and O’Leary 21
get markets. Using vacation benefits sought, METHODOLOGY
Loker and Perdue (1992) applied three evalua-
tion criteria for target market selection: profit- Data Set and Sample Selection
ability, accessibility, and reachability. There
were three measures of profitability for the This research used data from the Pleasure
non-resident summer travel market in North Travel Markets Survey for France collected
Carolina: the percentages of total expenditures by the Coopers & Lybrand Consulting Group
related to percentages of respondents for each in 1998 under the joint sponsorship of the Ca-
of the identified segments, the percentages of nadian Tourism Commission and the Interna-
total person-nights, and average expenditures tional Trade Administration-Tourism Indus-
per person per night. Each segment was ranked tries of the U.S. With random sampling using
on its relative performance on all three evalua- the birth date method, a total of 1,221 personal
tion criteria; the lowest ranking was assigned a interviews in French households were con-
value of 1 and the highest the same value as ducted. All respondents were 18 years or older
the number of segments. The overall ranking and had taken overseas vacations of four
for each segment was determined by summing
nights or more by plane outside of Europe and
the scores across the criteria.
The main limitation of these previous stud- the Mediterranean region in the past three
ies was in the lack of precision in the ranking years or were planning to take such a trip in the
procedure. With these ranking systems, it was next two years. This comprehensive survey
difficult to determine the degree to which one collected information on socio-demographic
segment was superior over another. The devel- characteristics (e.g., age, gender, marital sta-
opment of a more precise quantitative method tus, education, occupation, income), trip-re-
for evaluating market segments was still lated characteristics (e.g., travel expenditures,
needed. A most recent research study by Jang travel activity participation, travel regions,
et al. (2002) provided a breakthrough in this month of travel), benefits sought, travel phi-
respect. These researchers introduced the prof- losophies, and levels of trip satisfaction.
itability and risk concepts that have been well The sample used in this research was French
developed in the finance field and attempted pleasure travelers who took non-package trips.
to simultaneously analyze segment profitabil- The reason for this choice was the assumption
ity and risk in evaluating travel market seg- that expenditures associated with package
ment attractiveness. Mean expenditures were tours would not be a good indicator of the
used as a proxy for profitability and the stan- more general expenditure behavior of pleasure
dard deviation of a segment’s mean expendi- travelers. Given the nature of a package tour,
tures was employed as the risk variable. The most of the expenditures are incurred as pre-
Risk-adjusted Profitability Index (RPI: the payments for pre-determined itineraries re-
mean expenditure divided by the standard de- sulting in package travelers having different
viation times one hundred) and Relative Seg- expenditure patterns than non-package travel-
ment Size (RSS: mean expenditure multiplied ers (Sung, Morrison, Hong, and O’Leary, 2001).
by the probability of the occurrence of a spe-
Of the 1,221 interviews conducted, 984 re-
cific segment) were applied for the overall
evaluation of market segments and for target spondents reported expenditures for their
market selection. Despite the freshness of this trips, and 475 respondents with package ex-
idea, the weakness of the approach was that penditures were eliminated from consider-
the risk concept had very limited scope and ation. In addition, length of travel was checked
did not address one the most serious risks in for outlier detection, and 13 respondents fall-
tourism: seasonal risk or seasonality. This re- ing outside the four standard deviations (more
search is intended to advance the earlier re- than 90 days) were taken out of the data set
search by addressing this weakness and creat- (Hair, Anderson, Tatham, and Black, 1998,
ing an easier-to-use procedure for evaluating pp. 65). A total of 496 respondents were used
travel market segments from an economic for the analysis of French non-package over-
viewpoint. seas travelers in this research.

6. 22 JOURNAL OF TRAVEL & TOURISM MARKETING
Analysis Methods penditure levels have a direct relationship
with market potential, it appears reasonable to
The data analysis procedures followed the use a segment’s mean expenditures as a proxy
steps shown in Figure 1 leading to target mar- for the segment’s market potential. More ana-
ket selection. First, after travel activity partici- lytically, as suggested by Kotler (1991), the
pation was selected as the segmentation vari- equation of market potential is Q = nqp, where
able based on the objective of this research, Q is total market potential, n is number of buy-
two different types of cluster analysis were ap- ers, q is quantity purchased by average buyer,
plied for market segmentation. The data were and p is average unit price. The equation can
initially analyzed using a hierarchical cluster- be rewritten as Q = nR, where R is average
ing procedure with squared Euclidean dis- revenue per buyer, or average expenditure. If
tance as the similarity measure between cases. the equation is applied to tourism, the R, or av-
Ward’s Method was used to maximize within- erage expenditure per visitor or travel party,
cluster homogeneity and the number of clus- functions as a determinant factor for travel
ters was determined based on an agglomera- market potential, Q. However, the average ex-
tion schedule and dendrogram. The K-Means penditure alone does not effectively represent
clustering technique as a nonhierarchical pro- the market potential of a segment, and the seg-
cedure was then employed to fine-tune the re- ment size is another critical indicator as shown
sults even further by utilizing the hierarchical in Kotler’s equation, where the number of
results as a basis for the cluster seed points. buyers, n, is the segment size. Thus, the mar-
According to Hair et al. (1998, pp. 498), nonhier- ket potential of a travel segment was defined
archical methods have advantages and gain in this research as the mean expenditure per
increased acceptability in that the results are travel party times segment size (Figure 2). The
less susceptible to the outliers and the dis- number of respondents in each segment was
tance measure used. The advantages can be used as the indicator of segment size.
realized only with the use of specified initial Risk is another important measure for eval-
seed points where clusters are built around these uating segment attractiveness. Risk usually
points. Second, cross tabulations were used to measures the probability that something unfa-
profile the socio-demographic and trip-related vorable will occur. The risk concept has been
characteristics of the resulting clusters. Chi- extensively applied in the field of finance and
square analyses and Analysis of Variance in the most basic sense, risk is the chance of a
(ANOVA) determined whether statistically financial loss. Projects having greater chances
significant differences existed among the clus- of loss are viewed as more risky than those
ters. Third, the market potential and risk levels with lesser chances. In the marketing field,
of the clusters were calculated and then used risk may represent the likelihood that a seg-
to determine segment attractiveness and to se- ment may have less market potential than the
lect activity segments with the greatest poten- mean. According to Brigham and Gapenski
tial. (1988), a popular measure of risk is the stan-
Evaluation of Market Segment FIGURE 2. Formula for the Evaluation of Segment
Attractiveness Attractiveness
The market potential and risks of activity Name Formula
segments were used as the measures of seg- 1. Market Potential of a Seg- Mean Expenditures ¥ Segment
ment attractiveness. Travelers’ expenditure ment Size
levels within a destination are directly related 2. Risk (1) Expenditure Risk
(2) Segment Size Risk
to the revenues that each segment can gener-
3. Risk-adjusted Expenditure In- (Mean Expenditures/Expendi-
ate. In other words, the higher the travelers’ dex (REI) ture Risk) ¥ 100
expenditures, the greater the business reve- 4. Risk-adjusted Segment Size (Segment Size/Segment Size
nues that could be generated from that seg- Index (RSSI) Risk) ¥ 100
ment, and the greater economic potential the 5. Risk-adjusted Market Poten- (REI ¥ RSSI)/100
tial Index (RMPI)
market segment has for the destination. As ex-

7. Jang, Morrison, and O’Leary 23
dard deviation, which means the tightness of risk. REI, RSSI, and RMPI were used for the
the probability or frequency distribution. The overall evaluation of market segments and for
tighter the distribution, the smaller the stan- target market decision. The formulas for the
dard deviation, and, accordingly, the lower the evaluation of segment attractiveness are sum-
risk of a business loss. Two types of risk were marized in Figure 2.
employed in this research: expenditure risk
and segment size risk. Expenditure risk mea-
sured how far a segment’s expenditures were ACTIVITY SEGMENTATION
from their mean. If the observations were
close to the mean so that the expenditure Clustering and Labeling
distribution was tight, it indicated a high of Market Segments
probability that the mean expenditures would
be attained. The number of travelers in each The French travelers were grouped into ac-
segment represented segment size. Monthly tivity participation segments using the sur-
frequencies were used to compute the segment vey’s 44 activities as the clustering variables.
size risk of the activity segments in this re- Because the variables that are multicollinear
search. If a segment’s travelers were concen-
are weighted more heavily in clustering pro-
trated in only a few months of a year, this in-
troduced greater uncertainty about the mean cess, multicollinearity was checked with Vari-
monthly frequency of these visitors. Seasonal- ance Inflation Factor (VIF). The results of VIF
ity in a tourism destination causes economic range from 1.111 to 2.048, which are well be-
and social problems including instability of low a usual threshold of 10.0 (Hair et al., 1998,
employment, income, and tax revenue. The pp. 220). Thus, it is reasonable to decide that
best scenario is to have a segment with consis- the clustering variables were not seriously af-
tent month-to-month demand throughout the fected by multicollinearity. The four clusters,
year. In this case, segment size risk becomes consisting of 140 (28.2%), 125 (25.2%), 86
minimal. (17.4%), and 145 (29.2%) respondents, were
To better evaluate segment attractiveness, it identified. To label the four clusters, activity
was necessary to simultaneously consider the participation rates were computed (Table 1).
expenditure and risk concepts, and two in- The labels were determined based on the most
dexes developed in the Jang et al. (2002) study popular activities within each cluster.
were adopted: the Risk-adjusted Expenditure
Index (REI) and Risk-adjusted Segment Size Cluster 1: Beach and Sunshine Lovers
Index (RSSI). REI represented the mean ex-
penditure divided by the standard deviation The travelers in this cluster enjoyed swim-
times one hundred, and indicated the relative ming (88.6%), sunbathing or other beach ac-
expenditure level per unit of risk. This pro- tivities (85.7%), together with sampling local
vided a more meaningful basis for multiple foods (90.7%). They preferred informal or ca-
comparisons when the risk levels of segments
sual dining with table service (73.6%) for meals
involved were not the same. The RSSI was the
segment size divided by the standard devia- and had a high participation rate in shopping
tion times one hundred, representing the rela- (83.6%).
tive seasonal risk-adjusted frequency of each
segment. Finally, to consider market potential Cluster 2: City Sightseers
and its risk simultaneously, multiplying REI
times RSSI and then dividing by one hundred This group had high proportions of sight-
produced the Risk-adjusted Market Potential seeing in cities (80.8%), seeing big modern
Index (RMPI). This represented the segment’s cities (84.8%), and sampling local foods (88.0%).
market potential after adjusting for both ex- Both informal or casual dining with table ser-
penditure and segment size risks. Thus, the vice (84.0%) and dining in fast food restau-
higher the RMPI, the better the segment per- rants or cafeterias (77.6%) were important for
formed in terms of both market potential and travelers in this segment.

8. 24 JOURNAL OF TRAVEL & TOURISM MARKETING
TABLE 1. Travel Activity Participation Rates of Clusters
(Unit:
Percentage)
Activity Participation* C1 C2 C3 C4 Overall
Sampling local foods** 90.7 88.0 95.4 55.2 80.4
Swimming 88.6 21.6 66.3 17.9 47.2
Sunbathing or other beach activities 85.7 7.2 62.8 16.5 41.7
Shopping 83.6 73.6 83.7 50.3 71.4
Getting to know local people 73.6 78.4 96.5 41.4 69.4
Informal or casual dining with table service 73.6 84.0 82.6 44.8 69.4
Walking tours 73.6 56.8 88.4 36.6 61.1
Visiting small towns and villages 72.9 66.4 95.4 21.4 60.1
Seeing local crafts and handiwork 72.1 63.2 89.5 25.5 59.3
Sightseeing in cities 62.1 80.8 91.9 35.9 64.3
Enjoying ethnic culture/events 54.3 66.4 76.7 33.1 55.1
Seeing people from different ethnic background 53.6 56.8 76.7 24.1 49.8
Visiting friends or relatives 52.4 51.2 48.8 59.3 53.4
Visiting night clubs 49.3 46.4 50.0 31.7 43.6
Visiting remote coastal attractions 47.9 17.6 67.4 5.5 31.3
Outdoor activities such as climbing, hiking, etc. 37.9 19.2 67.4 8.3 29.6
Visiting national, state or provincial parks and forests 34.3 43.2 82.6 21.4 41.1
Dining in fine restaurants 33.6 38.4 50.0 31.7 37.1
Observing wildlife/bird watching 33.6 25.6 73.3 4.8 30.0
Visits to appreciate natural ecological sites 30.0 29.6 76.7 15.2 33.7
Dining in fast food restaurants or cafeterias 28.6 77.6 46.5 32.4 45.2
Taking a cruise for a day or less 27.1 5.6 26.7 6.2 15.5
Diving/surfing 25.7 3.2 22.1 2.8 12.7
Visiting arts and cultural attractions 25.7 44.0 53.5 17.9 32.9
Water sports 25.7 3.2 22.1 2.8 13.9
Seeing big modern cities 25.0 84.8 70.9 37.9 51.8
Visiting protected lands/areas 24.3 16.0 64.0 6.9 24.0
Visiting mountainous areas 20.7 23.2 72.1 11.0 27.4
Visiting places with religious significance 20.7 40.8 68.6 14.4 32.3
Visiting museums/galleries 19.3 67.2 77.9 31.7 45.2
Hunting/fishing 17.2 0.8 10.5 3.5 7.9
Seeing unique aboriginal or native groups 17.1 19.2 47.7 4.8 19.4
Taking a nature and/or science learning trip 15.0 18.4 52.3 8.9 20.6
Visiting places of historical interest 15.0 52.8 86.1 20.0 38.3
Visiting scenic landmarks 15.0 67.2 86.1 17.2 41.1
Short guided excursions/tours 14.3 32.8 57.0 12.4 25.8
Golfing/tennis 10.7 4.0 9.3 2.1 6.3
Attending local festivals/fairs 8.6 15.2 50.0 7.6 17.1
Visiting sites commemorating people 7.9 32.8 54.7 11.0 23.2
Visiting casinos and other gambling 7.1 10.4 11.6 9.7 9.5
Visiting theme parks or amusement parks 7.1 17.6 37.2 20.7 18.9
Attending spectator sporting events 6.4 11.2 17.4 1.4 8.1
Bicycle riding 6.4 9.6 15.1 4.8 8.3
Visiting places of archaeological interest 3.6 10.4 53.5 5.5 14.5
Average Number of Activity (Unit: Activity) 16.1 16.9 26.3 8.7 15.9
Note: 1. *Multiple responses.
2. **Rows are arrayed in descending order according to the participation rates in Cluster 1.
3. The bold-faced number means the highest percentage among the clusters.

9. Jang, Morrison, and O’Leary 25
Cluster 3: Culture and Nature Enthusiasts distributions of the segments were similar and
had no statistical differences. The majority of
The people in this segment showed the people in most of the clusters were married or
greatest interest in participating in a broad living together, but almost half of Cluster 2 (City
range of activities, especially in cultural and Sightseers) were single. Cluster 4 (Visiting
nature activities. Their highest participation Friends and Relatives) included more married
rates were for getting to know local people travelers (37.9%). Most of travelers had sec-
(96.5%), sampling local foods (95.4%), and ondary school educations or higher, suggesting
visiting small towns and villages (95.4%). that these non-package French outbound trav-
Compared to the other three segments, these elers were a well-educated group. Moreover,
travelers had higher participation in nature- over 40% of all four clusters had received a col-
based activities such as visiting national, state lege education or above; the proportion was
or provincial parks and forests (82.6%). This particularly high in Cluster 2 (53.6%). The
group had the most active travelers, participat- largest occupational group for all four activity
ing on the average in 26.3 of the 44 specified clusters was white-collar workers.
activities. Cluster 3 had the highest proportion in the
non-working housewife/retired category, which
Cluster 4: Visiting Friends and Relatives may partially explain why these travelers were
involved in the greatest number of activities
and spent the most nights away from home
This was the largest (29.2% of the total (27.4 nights) as shown in Table 3. The highest
market) of the four groups and had the highest proportion of blue-collar workers (11.4%)
participation rate for visiting friends or rela- was in Cluster 1 (Beach and Sunshine Lovers).
tives. The French travelers in this group were This may be due to the nature of labor-ori-
the least active in terms of participation rates ented jobs, where physical fatigue at work
for most of the travel activities. Only a few ac- tends to require rest and relaxation on vaca-
tivities, such as sampling local foods (55.2%) tion. About 50% or more of respondents in all
and shopping (50.3%), were over the 50% par- four clusters had monthly incomes ranging be-
ticipation rate. On the average, they only par- tween 6,500 French Francs (FF) and 20,000
ticipated in 8.7 of the 44 activities. FF. Less than 10% of the travelers in each of
the four segments reported monthly incomes
Socio-Demographic and Trip-Related of 30,000 FF or more.
Profiles of Activity Clusters There were statistically significant differ-
ences at the 0.1 level for all of the trip-related
The profile of the clusters’ socio-demogra- variables (Table 3). As mentioned earlier, Clus-
phic and trip-related characteristics provides ter 3 spent the greatest number of nights (27.4
useful information for destination marketers nights) away from home, demonstrating their
on the members of each segment and how distinctiveness as active travelers, whereas
they behave when traveling overseas. The Cluster 4 took the shortest trips (19 nights).
profiles were generated using cross-tabulation All the clusters except for Cluster 2 had the
analyses. Chi-square analyses or ANOVAs highest proportion in traveling with wife/hus-
were conducted to determine whether signifi- band/boy or girl friend and an especially high
cant differences existed among the segments. percentage (57%) was found in Cluster 3.
These analyses showed that marital status and Compared with the other three groups, the mem-
occupation differed significantly across the bers of Cluster 2 were more likely to travel
four clusters, while age, gender, education, alone or travel with friends. Travelers in all
and household income were not significantly four clusters tended to travel more in the sum-
different. mer and less in the fall. Cluster 1 showed the
Table 2 shows that all four clusters had the most consistent travel patterns across the four
highest proportions in the 30s, suggesting that seasons. Just over 50% of the persons in Clus-
French travelers 30-year age group are the larg- ter 2 were summer vacationers and this may be
est outbound travel market. The age and gender related to their travel destinations. The major

10. 26 JOURNAL OF TRAVEL & TOURISM MARKETING
TABLE 2. Socio-Demographic Characteristics of Clusters
C1 C2 C3 C4
(Beach and (City (Culture (Visiting
Characteristics Ú2
Sunshine Sightseers) and Nature Friends
Lovers) Enthusiasts) and Relatives)
Age 15.1
18-19 1.4% 3.2% 2.3% 0.0%
20-29 22.1 24.0 20.9 17.2
30-39 32.1 37.6 30.2 30.3
40-49 23.6 9.6 18.6 22.8
50-59 15.7 14.4 16.3 18.6
60 or older 5.0 11.2 11.6 11.0
Gender 2.4
Male 52.1% 45.6% 51.2% 44.1%
Female 47.9 54.4 48.8 55.9
Marital Status 19.6*
Single 30.7 47.2 34.9 27.6
Married 28.6 25.6 31.4 37.9
Living Together 27.1 17.6 23.3 19.3
Divorced/Widowed/Other 13.6 9.6 10.5 14.5
Education 14.3
Primary School 5.7 5.6 4.7 9.0
High School Without Diploma 25.7 16.0 24.4 17.9
High School or Equivalent 25.0 23.2 30.2 30.3
College or Above 42.1 53.6 40.7 42.8
Occupation 43.5***
University/College Student 5.7 10.4 8.1 5.5
White-Collar Worker 35.7 36.8 40.7 32.4
Blue-Collar Worker 11.4 2.4 2.3 1.4
Administrator/Manager 10.0 8.0 12.8 17.2
Self-Employed/Freelancer 22.1 22.4 12.6 21.4
Non-Working Housewife/Retired 10.7 16.0 21.2 15.1
Unemployed/Other 4.3 4.0 2.3 6.9
Household Income 22.6
Less than 6,500 FF 9.3 12.8 7.0 6.9
6,500-9,999 FF 17.1 13.6 14.0 13.8
10,000-12,999 FF 14.3 16.0 11.6 12.4
13,000-15,999 FF 12.9 13.6 12.8 13.1
16,000-19,999 FF 11.4 7.2 14.0 9.7
20,000-24,999 FF 8.6 8.0 10.5 11.7
25,000-29,999 FF 9.3 3.2 9.3 5.5
30,000 or more 4.3 8.0 8.1 6.9
Refused 12.9 17.6 12.8 20.0
Note: 1. *p < 0.1, **p < 0.05, *** p < 0.01.
2. C1-C4 means Cluster 1-Cluster 4.
3. FF stands for French Franc. French Francs was the French currency at the time of the data collection.
destinations of Cluster 1 included the West In- SEGMENT ATTRACTIVENESS
dies/Caribbean and Other Africa where sun- AND TARGET MARKET SELECTION
bathing and swimming are possible, irrespec-
tive of season. Canada and U.S. were the main Mean Expenditure and Expenditure Risk
destinations for Cluster 2 where many travel
activities are limited due to weather condi- As presented in Table 4, the expenditures
tions in winter. These two North American on international travel were divided into inter-
countries were also popular in Cluster 4. The national air transportation cost and within-
travel regions for Cluster 3 were relatively destination expenses. The within-destination
evenly distributed, but Asian countries at- mean expenditures per party were used in this
tracted the largest percentage from this group research as one of the key measures of market
(25.4%). potential. With the highest mean expenditures

11. Jang, Morrison, and O’Leary 27
TABLE 3. Travel-Related Characteristics of Clusters
C1 C2 C3 C4
(Beach (City (Culture (Visiting
Characteristics Ú2 or F
and Sunshine Sightseers) and Nature Friends
Lovers) Enthusiasts) and Relatives)
No of People in Travel Party 1.74 1.62 1.97 1.66 F: 2.5*
No of Nights Away from Home 23.9 24.3 27.4 19.0 F: 5.3***
Travel Companion c2: 41.7**
Alone 35.7% 40.0% 23.3% 38.6%
Wife/Husband/Boy or Girl Friend 48.6 28.0 57.0 43.4
Father/Mother/Children 2.9 8.8 2.3 4.1
Other Relatives 2.1 2.4 1.2 4.1
Friends 10.0 18.4 15.1 8.3
Organized Group/Other 0.7 2.4 1.2 1.4
Season of Travel c2: 51.7**
Spring 27.9 20.0 17.4 28.3
Summer 27.9 50.4 39.5 31.7
Fall 20.7 11.2 14.0 13.1
Winter 23.6 18.4 29.1 26.9
Travel Region c2: 196.9***
Canada 5.7 40.8 18.6 23.4
U.S. 9.3 32.8 9.3 32.4
Mexico/Central and South America 8.5 5.6 16.3 7.6
The West Indies/Caribbean 32.1 3.2 17.4 12.4
South Africa 8.6 0.8 4.7 1.4
Other Africa 21.4 5.6 4.7 6.9
Oceania 2.8 1.6 3.6 3.4
Asia 11.6 9.6 25.4 12.5
Note: 1. *p < 0.1, **p < 0.05, ***p < 0.01.
2. C1-C4 means Cluster 1-Cluster 4.
TABLE 4. Mean Expenditures and Expenditure Risk of Clusters
Expenditures and Risk C1 C2 C3 C4
(Beach (City (Culture & Nature (Visiting Friends
and Sunshine Lovers) Sightseers) Enthusiasts) and Relatives)
Mean Expenditures/Travel Party 14,320 FF 14,740 FF 19,396 FF 13,633 FF
International Air Transportation 6,797 FF 5,821 FF 8,586 FF 6,521 FF
Mean Expenditure within Destination 7,523 FF 8,919 FF 10,810 FF 7,112 FF
Expenditure Risk 6,021 8,841 7,995 5,979
REI 125 101 135 119
Note: 1. Mean expenditure/travel party was divided into two components: International air transportation and mean expenditure within destinations.
2. Expenditure Risk is the standard deviation of expenditures within destinations.
3. REI was calculated as mean expenditure within destinations divided by expenditure risk times one hundred.
4. C1-C4 means Cluster 1-Cluster 4.
(19,396 FF), Cluster 3 was considered to be tures, risk needed to be considered to check for
the segment with the greatest economic con- potential variability of the expenditures. Risk
tribution, while Cluster 4 made the least con- was measured by the standard deviation of
tribution (13,633 FF). The members of Cluster mean expenditures within destinations. Clus-
3 also spent more money on international air ter 4 emerged as the least risky segment and
transportation. The lowest spending group for Cluster 2 had the highest expenditure risk. To
air fares was Cluster 2, which may be related determine the best segment from an expendi-
with the main travel regions of that group: ture-risk viewpoint, it was necessary to com-
Canada and the U.S. bine mean expenditures and expenditure risk.
Although Cluster 3 appeared to be the best Thus, the Risk-adjusted Expenditure Index (REI)
in terms of the within-destination expendi- was calculated, and the results are presented in

12. 28 JOURNAL OF TRAVEL & TOURISM MARKETING
Table 4. Representing the best market seg- Cluster 1 had the lowest risk. To derive a
ment among the four from an expenditure simultaneous measure of segment size and
level standpoint, Cluster 3 also had the highest segment size risk, the Risk-adjusted Segment
REI at 135, whereas Cluster 2 had the lowest Size Index (RSSI) was created by dividing
REI (at 101) and was the least attractive seg- segment size by the segment size risk times
ment. Therefore, Cluster 3 was judged to con- one hundred. As shown in Table 5, Cluster 1
tribute the greatest economic benefits to travel clearly emerged as the best market segment
destinations. after adjusting for the effect of seasonality
with an RSSI of 329. Cluster 4 also had a good
Segment Size and Segment Size Risk market size index (RSSI = 273), but both
Cluster 3 and Cluster 2 did not perform well in
Another important consideration in segment this respect.
selection was the market size. Cluster 3 was the
segment with the highest expenditure level. Market Potential of Activity Segments
However, even though expenditure levels may
be high, the actual scale of economic benefits The REI and RSSI results provided useful
will not be as great if the segment size is small. information for identifying potentially valuable
Thus, it was necessary to consider the segment market segments. To better visualize the attrac-
size together with expenditure level. The monthly tiveness of the four activity segments in terms
mean frequency counts for each cluster were of REI and RSSI, Z-standardized REIs and
used as the measure of segment size. Table 5 RSSIs were plotted on an X-Y axis as presented
shows that Cluster 4 had the largest market size in Figure 3. Clusters in the first (top-right)
(12.1 per month), while Cluster 3 had the small- quadrant represented relatively attractive mar-
est number (7.2). As explained earlier, the un- kets for both mean expenditures and market
certainty or risk associated with the market size size after risk adjustment. Cluster 1 seemed to
is seasonality and this is one of the most critical be the most appealing to marketers. Indicating
issues in tourism. The standard deviation in the the least desirable segment, Cluster 2 was posi-
monthly frequency distributions was employed tioned in the third (bottom-left) quadrant where
as the segment size risk. The results showed both the mean expenditures and the market size
that Cluster 2 was the most risky market and were small. Despite a high expenditure level,
TABLE 5. Segment Size and the Risk of Clusters
C1 C2 C3 C4
(Beach (City (Culture and Nature (Visiting Friends
and Sunshine Lovers) Sightseers) Enthusiasts) and Relatives)
Monthly Frequency
January 10 7 8 13
February 15 5 10 17
March 14 7 5 11
April 18 9 2 14
May 7 9 8 16
June 12 16 5 14
July 12 23 8 15
August 15 24 21 17
September 7 7 5 6
October 12 5 4 3
November 10 2 3 10
December 8 11 7 9
Segment Size 11.7 10.4 7.2 12.1
Segment Size Risk 3.5 7.0 4.9 4.4
RSSI 329 148 145 273
Note: 1. Segment Size is the monthly mean frequency of cluster
2. Segment Size Risk is the standard deviation in monthly frequency distribution of cluster
3. RSSI was calculated as Segment Size divided by Segment Size Risk times one hundred
4. C1-C4 means Cluster 1-Cluster 4.

13. Jang, Morrison, and O’Leary 29
FIGURE 3. Cluster Positions activity segments to assist with target market
selection. Four distinct groups of French travel-
II. High Expenditures
I. High Expenditures
Large Market Size
ers were found: Cluster 1 (Beach and Sunshine
Small Market Size 2.0
Lovers), Cluster 2 (City Sightseers), Cluster 3
REI
Cluster 3 1.5
Cluster 1
(Culture and Nature Enthusiasts), and Cluster 4
1.0 (Visiting Friends and Relatives). Since activ-
Cluster 4
0.5
ity-based segmentation information indicates
RSSI
what French travelers want to do on their over-
0.0
1.0 1.0 2.0
seas trips, different positioning messages can
2.0 0.0
0.5 be used to individually appeal to each of the
III. Low Expenditures
1.0 IV. Low Expenditures segments. For example, promoting the beauty
Small Market Size Large Market Size
1.5
of a beach would be appropriate to Beach and
Cluster 2 Sunshine Lovers. However, when communi-
Note: REI (Risk-adjusted Expenditure Index) = (Mean Expenditures/Expenditure
Risk) x100.
cating to Culture and Nature Enthusiasts, who
RSSI (Risk-adjusted Segment Size Index) = (Segment Size/Segment Size
Risk) x 100.
want to pursue new experiences in local culture
and nature appreciation, the promotional con-
tent should focus on new knowledge and the
Cluster 3 was a relatively small segment size adventure orientation of trips. Significant dif-
positioned in the second (top-left) quadrant. ferences were found among the four market
The information generated from the REIs and segments for marital status, occupation, travel
RSSIs can be applied in different ways accord- party size, number of nights away from home,
ing to the marketer’s situation. If the organiza- travel companions, season of travel, and travel
tion or destination is large and wants a major regions. These differences in socio-demo-
market share, Cluster 1 would be the most at- graphic and trip-related characteristics can help
tractive segment. However, if travel marketers marketers decide on how each segment can be
are planning a niche marketing strategy, Clus- approached and served. The findings of this re-
ter 3 may be a better target. search indicated direction to destination mar-
Due to the diverse nature of different organi- keters in formulating marketing strategy to-
zations’ marketing objectives, it is difficult to wards French outbound travelers.
determine which segment is the best for every Using mean expenditure, expenditure risk,
tourism marketer. As a combined evaluation of segment size, and segment size risk as the
the market segmentation, Risk-adjusted Market evaluation criteria, the Beach and Sunshine
Potential Index (RMPI) provides a useful quan- Lovers group emerged as the most attractive
titative tool to determine the overall levels of of the four segments and City Sightseers
attractiveness (Table 6). Using RMPI, Cluster group was the least attractive. However, the
1 again appeared to be the most appealing seg- study’s outcomes can be interpreted in differ-
ment with the highest risk-adjusted market po- ent ways according to what each destination
tential (RMPI = 423) among the four segments. has to offer and the marketers’ objectives. Ma-
The least attractive segment was Cluster 2 ture beach and sun destinations with a priority
(RMPI = 150). Although the final target mar- on volume markets may be most attracted by
ket selection usually requires some subjective the Beach and Sunshine Lovers group. Other
decision criteria, the process described and destinations, especially those with an empha-
demonstrated in this study should help deci- sis on nature- or culture-based niche markets,
sion-makers with target market selection. would find the Culture and Nature Enthusiasts
group to be a better fit with their objectives.
Prior studies evaluating market segment at-
DISCUSSION AND CONCLUSION tractiveness have used ranking systems to de-
termine the best markets. However, due to a
The main objectives of this research were to lack of precision in these ranking procedures,
identify distinct segments of French outbound they have not effectively gauged the degree to
pleasure travelers based upon activity partici- which one segment is more attractive than the
pation and to evaluate the attractiveness of the others. This research is expected to contribute

14. 30 JOURNAL OF TRAVEL & TOURISM MARKETING
TABLE 6. Relative Market Potential Index (RMPI) of Clusters and Overall Ranking
C1 C2 C3 C4
(Beach (City (Culture and Nature (Visiting Friends
and Sunshine Lovers) Sightseers) Enthusiasts) and Relatives)
REI 125 101 135 119
RSSI 339 148 145 273
RMPI 423 150 196 325
Ranking 1 4 3 2
Note:1. RMPI was calculated as REI times RSSI divided by one hundred.
2. C1-C4 means Cluster 1-Cluster 4.
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