IMPORTANCE CHOICE CRITERIA AS A BASIS FOR TOURISM MARKET
SEGMENTATION TECHNIQUES
Ourania Vitouladiti
Technological Educational Institute of Athens
Faculty of Business Administration
Department of Tourism Enterprises
Athens, Greece
Email: ranivito@hol.gr
ABSTRACT
This research paper aims to identify the most important criteria for a destination choice and to
use these criteria as a basis for innovative market segmentation, necessary in today’s competitive
environment.
The above objectives are met through primary quantitative research with a representative
sample of British tourists in Corfu. Cluster analysis techniques were performed in order to identify
different clusters of criteria and segments. Seven bundles of important choice criteria and benefits were
identified. Also five different segments, which revealed targeted marketing mix actions, were
identified, based on importance choice criteria.
Key words: Segmentation, benefit segmentation, importance choice criteria, cluster analysis, bundles
of choice criteria
1. Introduction
In our days, the traditional destinations, especially in the Mediterranean, face intense
competition. There is a growing need for increased knowledge about tourist consumers’ motivations,
needs and about influences on their decision making.
One traditional Mediterranean destination is the island of Corfu. Tourism is considered the
most important pillar of its economy. Germany and the UK are the leading generating markets from
Europe. The island tourism offer, is mostly associated with the sun, sea and sand concept. This
destination can be considered a miniature of the Greek tourism product. The island as an established
destination faces intense competition and decreasing arrivals of its traditional markets. There is a need
for insights in innovative segmentation techniques which allow improved knowledge on the demand
side concerning possible changes in motives, wants and benefits sought.
The use of the island, as a representative case study, for the implementation of primary
research with a probability sample of British visitors (one of its basic markets) will enable the
destination to explore its segments and to identify sub segments with different or changed needs and
benefits sought in their holidays and as a result to improve communication on destination attributes
that influence consumer behavior and loyalty.
Segmentation techniques are the most used tools in order to improve this knowledge. There
are a lot of ways of segmenting consumers. But as marketing increases in its sophistication the
segmentation ways have to increase their intelligence. Apart from the traditional techniques of
demographic, socioeconomic, geographic etc. characteristics which are very important variables, there
is a segmentation technique that has attracted an increasing interest throughout the years, this of benefit
segmentation.
Benefit segmentation has long been considered an effective means of grouping tourists based
on their attitudes towards services and products. In benefit segmentation tourists are characterized
according to the benefits they seek from their purchases. Benefit segmentation is a market oriented
approach consistent with the modern marketing concept (Lewis et al, 1995).
It is considered that individuals perceive of products and services in terms of bundles of
benefits or attributes (Kotler et all, 1996, Morgan, 1995). They buy a bundle of benefits (Mill and
Morrisson, 1985). The relative importance of each benefit or attribute varies (Mayo and Jarvis, 1981).
The likelihood of buying a product is determined by the extend to which the individuals
perceive the product to contain sufficient benefits to satisfy their felt needs and also to the extend that
the satisfaction of those felt needs are important to them (Mill and Morrisson, 1985). As Kotler
(2000)suggests, knowledge of benefits searched by tourists let us know what to offer and promote in
order to attract specific target markets.
Identifying homogenous subsets of customers for particular geographic markets or linking
particular product benefits for specific groups, allows managers to develop more strategically targeted
products and campaigns (Dickman, 1999). The target markets, in order to be effective must be
measurable, accessible, substantial, actionable and differentiable (Kotler et al, 2002).
When the authorities in the private and public sector understand, in depth, tourist consumers’
characteristics and decision process then the promotional activities, price settings, planning and product
development can be more consistent and effective. Therefore, this subdivision of tourists and potential
tourists into useful groups is a critical step in building competitive advantages of a destination (Evans
et all, 2003). As a result it is one of the most vital decisions a destination makes (Dolnicar, 2004).
2. Objectives of the study
Therefore, the objectives of the study are:
1.
2.
3.
4.
To identify the important benefits sought by the visitors of the island
To identify bundles, or sets of benefits
To determine segments of tourists based on the importance of benefits searched for in their holidays
To offer marketing and management implications
3. Methodology
3.1. Research design - Sampling and data collection
In order to achieve the targets of the study, it was necessary to carry out primary quantitative
research. And in order to fulfill best the goals of the study the sample members (British) was decided to
be first time visitors. The sample was decided to be stratified, because is probability sample and more
representative. The island is divided into three areas, North, Central and South. Each area has all
categories of hotels and accommodation. Since the boundaries of the areas were known, they were
defined as strata. In every one of these strata, accommodation of every category was chosen by random
sampling. The members of the sample were also chosen by random sampling in all the selected hotels
and accommodation types.
The final sample size obtained was 375 British first time visitors. This sample size (n=375)
gives a statistical error (e≈5%) which is considered very satisfactory (level of significance α=0, 05,
level of confidence 95%). This sample size and statistical error could permit the generalisation of
results.
3.2. Questionnaire design – analysis
The self administrated questionnaire comprised closed-end questions. The closed-end
questions had a five-point rating scale. All the rating scales were labeled. Nineteen (19) variables
concerning choice criteria or motives were listed. The final list of the variables was decided on the
basis of their use in past studies focusing on island destination attributes (Beerli and Martin, 2004,
Garcia et al, 2004). For the statistical analysis and the interpretation of the results the five-point scale of
the questions was coded from 5 to 1, considering 5 the most important criterion and 1 the less
important one, meaning the higher the score the more important the variable (5. very important, 4. quite
important, 3. neither important nor unimportant, 2. quite unimportant, 1. very unimportant)
.3.3. Profile and description of the sample
Concerning the distribution of the sample by sex both sexes had an almost equal participation
in the survey. Female respondents represented 57%, or 216 persons, male respondents represented
43%, or 160 persons out of a total of 376. Regarding age categories, 44% of the sample is between the
ages of 35 and 54 years. The other age categories are equally represented with percentages of 27% for
the under 34 age group and 29% for the 55+ group. This gives a balanced distribution. Distribution by
income demonstrates expected percentages. The income brackets “>£20.000” and “£20.000 - £40.000”
are represented equally (36% and 41% respectively). Their percentages are increased compared to the
income bracket of “£40.000+”. This is an expected element which reassures the reliability of the
sampling method, since it is known that the British market segment of Corfu belongs to the average
income levels.
.3.4. Statistical Analysis
Firstly, this research paper used descriptive statistics in order to indicate the most important
criteria. Secondly, hierarchical clustering analysis was undertaken to identify bundles of importance
choice criteria and benefits. Thirdly, k-means clustering algorithm was applied to identify market
segments based on their importance choice criteria when they choose a destination. Several cluster
solutions were specified and the best cluster solutions were chosen on the basis that they offered the
most meaningful, interpretable and distinguishable bundles and segments (Park and Yoon, 2009).
4. Data Analysis
In table 1 the means of the five point scales are presented in descending order. The criteria
which appear more important concern relaxing and services while as less important appear those
regarding entertainment, adventure and extroversion. Nevertheless, it is observed that the means have
high scores, 11 out of 19 criteria have mean >4, while only one criterion has mean < 3, which is the
central point of the scale. This is a common phenomenon in rating scales that tends to create difficulties
in discriminating among the more and less important criteria. In order to minimize this issue and
clearly identify the differences, centered values for each one of the 19 criteria were structured. The new
centered values reflect the differences among more and less important criteria and are considered as
more appropriate for the analysis, being continuous variables.
Table 1. Ranking of choice criteria by order of importance (total sample)
15
6
18
10
16
1
5
7
14
11
3
13
19
12
9
17
2
4
8
Choice Criteria
Relaxing physically and mentally
Cleanliness of sea and beaches
Escaping from daily routine
Safety
Affordable/reasonable prices overall
Availability of suitable accommodation
Quality of service personnel
Discovering new places/different cultures
Scenic beauty/natural attractions
Unspoiled physical environment
Local cuisine
Historical and cultural attractions
Sunbathing in the beach and doing nothing
Having fun, being entertained
Availability of entertainment
Being adventurous/being active
Giving a feeling of prestige
Developing friendships with others
Availability of facilities for sports and activities
Mean Centered
scale 5-1 Value
relaxing
4,67
0,66
clean sea
4,63
0,62
escape routine
4,59
0,58
safety
4,59
0,58
prices
4,51
0,50
accommodation
4,49
0,48
personnel
4,40
0,39
new/different
4,33
0,32
natural beauty
4,33
0,32
unspoiled environment
4,25
0,24
cuisine
4,01
0,00
historical attractions
3,86
-0,15
sunbathing
3,73
-0,28
fun
3,63
-0,38
entertainment
3,46
-0,55
adventure
3,35
-0,66
prestige
3,32
-0,69
friendships
3,13
-0,88
sport facilities
2,90
-1,11
The technique which was used (table 2) is that of hierarchical clustering, where individual
points are merged into clusters, based on the distances between them. The between groups average
linkage algorithm was the linkage method used and the measure of distance was the Pearson correlation
coefficient. Seven new variables were revealed that will be named bundles. Each bundle is calculated
as the mean score of the relevant attributes. The means of the bundles are presented in table 3. The
more important appear to be the bundles relax and service and less important the bundles action and
status.
Table 2. Cluster analysis – bundles of choice criteria
1
accommodation
5
personnel
services
10
safety
2
prestige
status
4
friendships
3
cuisine
7
new/different
11
unspoiled environment
13
historical attractions
explore
14
natural beauty
19
sunbathing
sea
6
clean sea
8
sport facilities
action
17
adventure
9
entertainment
fun
12
fun
15
relaxing
relax and
16
prices
value for
money
18
escape routine
Table 3. Cluster group means for the seven (7) bundles
service
status
explore
sea
,48
-,78
,15
,17
action
-,88
fun
-,47
relax
,58
Table 4. Means of the 19 variables (5 segments) maximum values, minimum values
centered values
rating scales (1-5)
Total
1
2
3
4
5
Total
1
2
3
4
1.
accommodation
0,48 0,47 0,61 0,81 0,65 -0,15 4,49 4,61 4,52 4,70 4,64
2.
prestige
-0,69 -0,05 -1,20 -0,78 -0,63 -1,12 3,32 4,10 2,71 3,11 3,35
3.
cuisine
0,01 -0,08 0,35 0,27 -0,21 -0,21 4,02 4,07 4,26 4,16 3,78
4.
friendships
-0,88 -0,36 -0,77 -1,23 -1,09 -1,21 3,13 3,79 3,14 2,66 2,90
5.
personnel
0,39 0,42 0,58 0,61 0,31 0,05 4,40 4,56 4,48 4,50 4,30
6.
clean sea
0,62 0,45 0,61 0,94 0,68 0,54 4,63 4,59 4,52 4,83 4,66
7.
new/different
0,32 0,16 0,85 0,55 -0,14 0,36 4,33 4,31 4,76 4,44 3,84
8.
sport facilities
-1,11 -0,81 -1,47 -1,61 -1,32 -0,48 2,90 3,34 2,44 2,28 2,66
9.
entertainment
-0,56 -0,12 -1,29 -1,50 0,14 -0,40 3,45 4,03 2,62 2,39 4,13
10.
safety
0,57 0,52 0,55 0,77 0,69 0,37 4,58 4,66 4,45 4,66 4,68
11.
unspoiled environment
0,25 0,04 0,58 0,56 0,04 0,17 4,26 4,19 4,48 4,45 4,03
12.
fun
-0,39 -0,12 -0,95 -1,38 0,25 -0,03 3,62 4,03 2,95 2,52 4,23
13.
historical attractions
-0,15 -0,28 0,36 0,09 -0,73 -0,01 3,86 3,87 4,27 3,98 3,26
14.
natural beauty
0,32 0,13 0,77 0,56 -0,14 0,43 4,33 4,28 4,68 4,45 3,84
15.
relaxing
0,66 0,31 0,80 0,95 0,76 0,69 4,67 4,46 4,71 4,84 4,74
16.
prices
0,50 0,27 0,61 0,59 0,65 0,48 4,51 4,42 4,52 4,48 4,64
17.
adventure
-0,66 -0,53 -0,42 -1,20 -1,05 -0,12 3,35 3,61 3,48 2,69 2,94
18.
escape routine
0,58 0,23 0,76 0,73 0,74 0,63 4,59 4,38 4,67 4,63 4,73
19.
sunbathing
-0,28 -0,63 -1,32 0,27 0,39 0,00 3,73 3,51 2,59 4,16 4,38
5
3,90
2,93
3,84
2,84
4,09
4,58
4,40
3,57
3,64
4,42
4,21
4,01
4,03
4,48
4,73
4,52
3,93
4,67
4,04
Based on k-means clustering algorithm and using the 19 centered values we ended to 5
segments. Differentiations of the mean scores of the bundles clearly discriminate into clusters. Also the
demographic synthesis of segments differs (age, income, education level). This feature is a measure to
the validity of the clustering. For the exploration of the segments both the 19 centered values as well as
the original scale 5 - 1) were used. Methodologically, the task of naming the segments embodies a level
of subjectivity.
Table 5. Segments and bundles performance across segments
segments
service
1
0,47
status
-0,20
explore
sea
-0,01
-0,09
action
-0,67
fun
-0,12
relax /value
for money
0,27
2
0,58
-0,98
0,58
-0,36
-0,95
-1,12
0,72
3
0,73
-1,01
0,41
0,60
-1,41
-1,44
0,76
4
0,55
-0,86
-0,23
0,53
-1,19
0,20
0,72
5
Total
0,09
0,48
-1,16
-0,78
0,15
0,15
0,27
0,17
-0,30
-0,88
-0,21
-0,47
0,60
0,58
Table 6. Demographic characteristics of segments
total
1
n
375
101
18 - 34
35-44
45-54
>55
Secondary/Technical
Higher Technical
University
under 20000 £
20000-30000 £
30000-40000 £
40000 £+
%
%
%
%
%
%
%
%
%
%
%
26,4
21,6
22,9
29,1
37,1
33,6
29,3
36,3
25,9
15,7
22,1
25,7
22,8
19,8
31,7
51,5
34,7
13,9
44,6
20,8
12,9
21,8
2
66
3
64
4
77
5
67
9,1
19,7
24,2
47,0
25,8
34,8
39,4
27,3
19,7
18,2
34,8
14,1
18,8
25,0
42,2
28,1
34,4
37,5
21,9
23,4
21,9
32,8
37,7
24,7
22,1
15,6
46,8
29,9
23,4
44,2
29,9
15,6
10,4
43,3
20,9
25,4
10,4
23,9
34,3
41,8
37,3
37,3
11,9
13,4
5. Results, comments and implications
5.1. Profile of segments and comments
Segment 1 – Service oriented at a value
The more sizable group, with 27% of respondents, comprises mostly from middle aged,
middle class and average to low income vacationers, with secondary level education or higher technical
education. They are very interested in service, relax and value for money, although they rate these
clusters at the lower importance in comparison with the other segments.
Segment 2 – Exploration, service and tranquility oriented
A group that represents 18 % of respondents consists mainly of older vacationers, 55+, and
together with segment 5 displays the higher percentages of university level and higher technical
education. They usually have middle to high incomes.
They are the most exploration oriented, they want to know and explore their vacation
destination. Fun, action and status are low importance criteria for them. They are quiet, have little
interest in sunbathing, seek tranquility and are interested in services.
Segment 3 – Service, tranquility and nature oriented
A segment that represents 17% of respondents consists mostly of middle age to older ages,
45 – 54 and 55+, displays high percentages of university level and higher technical education, together
with segment 2 represent the more affluent segments of visitors.
They seek tranquility more than the others, are the most service oriented, they consider the
unspoiled physical environment and clean seas the more important criteria. Together with segment 2
they are interested in exploring the destination.
Segment 4 – Fun and relaxation oriented at a budget
This segment represents 21% of respondents, they consist mostly of younger vacationers,
mostly under 34, and almost half of them, 47%, has lower/secondary education. They are considered
working class youths, low income, budget bound, special offers seekers, probably the least loyal
clientele. Their main goal is fun and relaxation with little interest in exploration and sport activities.
However, the bundle of service is important for this segment as well.
Segment 5 – Relaxation, sunbathing and activity oriented at a budget
This segment represents18% of respondents, they consist mostly of younger vacationers,
mostly under 34, and almost half of them, 41,8%, has University education. They are considered
middle class youths, with a high percentage of university and college students, budget bound and
special offers seekers but with a small interest to explore and possibly engage in some sport activity.
5.2. Discussion and marketing management implications
From the above analysis specific conclusions emerge. Initially, the findings of this research
paper, affirm the existence of the established markets, as it would be expected. Specifically segments 1,
4 and 5 that are considered the norm for Mediterranean destinations. At the same time this
affirmation validates the reliability of the methodological approach.
However, from the above description of these segments appears though, that this
clientele has evolved in its lifestyle characteristics and demands and, as a result, in its priority
criteria (such as service) while the prevailing view concerning these segments has remained the
same over the decades, meaning simplistic, somewhat banal and stuck on the sea, sun and sand
model.
Moreover, the findings of this research paper revealed two new and distinct segments,
namely 2 and 3. These two new segments comprise a substantial percentage of visitors (35%) who
are the more affluent, well educated and clearly service oriented. Apart from these very
interesting segmentation characteristics and high percentages, that make them a desirable target
group, additional differences from the established markets are observed. Specifically, they rate as
important benefits the exploration of their vacation destination, the nature and tranquility. The
benefits of the typical Mediterranean model of the 3S are considered of less importance.
The existence of this kind of segments is actually unknown, never targeted and
approached through specific marketing efforts. In a similar fashion the evolved and
differentiated characteristics of the classical markets remain unnoticed and therefore, have not
been addressed by marketing mix variables. Last but not least, another rising issue is that of
service, the bundle of which emerges among the most important criteria for all the identified
segments.
The findings of this study support the existing views which underline that improvements of
education and welfare levels in Europeans countries have diversified the profiles of the consumers,
their wants, motives and preferences and have increased the quality standard demanded (Poon, 1994).
The findings and the revealed issues are of great importance because firstly, they can direct
and enable destination marketers to combine the marketing mix variables in order to approach the new
segments and satisfy the evolved demands of the classical clientele. Secondly, to best allocate the
always restricted marketing budget in effective campaigns.
This research was conducted in an established Greek destination using representative sample
of a main tourist market. Replicating this study for other similar destinations within the Mediterranean
could help to maximize the generalization of the findings.
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