Every student has an individual taste when it comes to travelling. As a start up wanting to gain the market share, the main problem is identifying different segments of the population. It is extremely difficult to cater to an unsegmented population as the firm wants to provide specialised packages tailored to individual needs.

1. 3/3/2017 Report on Market
Research on
Travel Preference
Submitted By: Group 4
Group 4
XAVIER INSTITUTE OF MANAGEMENT, BHUBANESWAR

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CONTENT:
Sl.No. TITLE Page No.
1. EXECUTIVE SUMMARY 02
1.1 OBJECTIVE 02
1.2 ABSTRACT 02
1.3 MAJOR FINDINGS 02
1.4 RECOMMENDATION 02
2. PROBLEM DEFINITION 03
3. APPROACH TO PROBLEM 03
4. RESEARCH DESIGN 03
5. DATA ANALYSIS 03
6. RESULTS 09
7. CONCLUSION AND RECOMMENDATION 10
8. FUTURE SCOPE 10
9. QUESTIONNAIRE 11
10. EXHIBITS 13
10.1 HIERARCHICAL CLUSTER ANALYSIS 13
10.2 K-MEAN CLUSTER ANALYSIS 17

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1. EXECUTIVE SUMMARY
OBJECTIVE:
To segment the B school students according to their travel preferences for a vacation or a
getaway.
ABSTRACT:
Every year students of B schools go for leisure trips o relieve the stress. Students go for
weekend outings, legacy trips, formal and informal vacation. As a potential start-up venture we
need to segment the student population based on their travel preferences. We have identified
the following key parameters as the most important factors affecting the decisions of the
students:
 Price or Value for Money
 Location
 Food Habits available
 Lodging with amenities
 Services available (e.g. para-gliding etc.)
 Personal Safety
 Regional or Local culture
 Connectivity to location
MAJOR FINDINGS:
To conduct the research, the student population of XIMB was selected as the sample. The major
findings of the study are: -
 Students can be divided into three clusters
 The most important parameters are
o Location
o Personal Safety
o Connectivity
 One cluster is a group of enthusiastic travellers as presence or absence of any of these
parameters are not important for them.
RECOMMENDATION:
The findings from the research recommends to develop special travel packages targeted on the
different segments of students and further pricing experiments should be conducted before a
package can be sold.

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2. PROBLEM DEFINITION
Every student has an individual taste when it comes to travelling. As a start up wanting to gain
the market share, the main problem is identifying different segments of the population. It is
extremely difficult to cater to an unsegmented population as the firm wants to provide
specialised packages tailored to individual needs.
3. APPROACH TO PROBLEM
A questionnaire was developed and circulated among the students. The primary data collected
was analysed using Cluster analysis. Students were divided into respective clusters each of
which had different preference.
4. RESEARCH DESIGN
The research carried out in this study is a problem-solving research as the problem has already
been identified by the researcher. Data collection was done by use of close ended questions in
the questionnaire. The sampling was convenience sampling due to time constraints.
Data collected has been collected scientifically to develop the basic demographic profile of
each cluster. The questionnaire also collects data about individual preferences among the given
set of options.
Likert scale had been implemented for scaling the psychometric response that had been
collected. Non random sampling was used in this study.
5. DATA ANALYSIS
The data collected had been verified to remove biased and wrong entries. The edited data was
uploaded in SPSS and analysed. To segment the sample, we have carried out cluster analysis.
All the data had been entered as ordinal scale in the variable view pf SPSS.
The Link for the Response Sheet:
https://docs.google.com/spreadsheets/d/1FsSs6_NoGpVRMC_T1507ozJcNaHgWucXuTv8A
RDQif8/edit?usp=sharing
The Link for the Online Questionnaire:
https://docs.google.com/a/stu.xub.edu.in/forms/d/e/1FAIpQLSd0Jaco9cidre9dDALQFt-
xD9v0PiWkOuUvHOTMtNkdytdj3Q/viewform

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Price OR
Value for
Money
Location
Food
Habits
Availabe
Lodging
with
amenitie
s (e.g.
WIFI,
Bar,etc.)
Services
Availabl
e (e.g.
Para-
gliding,j
et
skiing,et
c.)
Personal
Safety
Regional
or Local
Culture
Connecti
vity to
Location
3 4 2 3 4 4 3 4
3 3 5 1 3 5 4 5
3 3 1 3 2 3 3 3
3 3 2 3 2 3 2 3
1 1 2 2 3 1 3 2
4 5 4 3 3 4 2 3
5 4 4 2 2 5 2 4
4 5 4 4 3 4 4 5
4 5 5 5 4 5 4 5
3 3 1 2 3 2 1 1
4 5 3 3 3 4 3 4
2 1 3 2 5 3 5 4
4 5 3 4 4 3 3 4
1 2 2 2 3 3 3 3
4 5 3 4 3 5 3 4
5 5 4 3 4 4 4 4
5 4 3 3 3 5 5 5
3 5 2 1 1 4 5 5
5 5 4 4 2 4 3 5
4 5 4 5 2 5 2 4
3 5 4 3 4 4 5 4
5 5 3 3 4 4 4 4
2 3 2 3 2 3 3 2
2 1 2 2 1 2 3 2
5 5 4 4 3 4 3 3
5 5 3 4 3 5 3 5
3 4 4 4 3 4 4 4
5 5 5 5 3 5 3 5
4 4 3 2 1 4 4 5
3 5 4 2 1 5 2 5
4 5 3 4 3 5 4 4
4 5 4 4 3 5 4 5
4 5 3 3 3 5 4 5
3 4 3 4 4 5 3 4
4 5 4 5 3 4 4 3
5 5 5 3 4 4 4 2
3 5 2 2 4 4 5 4
5 5 2 4 5 5 4 4
2 1 2 2 2 3 2 1
Table 1: Validated Data
Hierarchical cluster analysis was carried out by limiting the number of clusters to 5. The
analysis produced the following output.

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Agglomeration Schedule
Stage
Cluster Combined
Coefficients
Stage Cluster First Appears
Next StageCluster 1 Cluster 2 Cluster 1 Cluster 2
1 8 32 1.000 0 0 5
2 15 31 1.000 0 0 6
3 16 22 1.000 0 0 15
4 3 4 2.000 0 0 10
5 8 33 2.500 1 0 11
6 15 26 2.500 2 0 11
7 25 35 3.000 0 0 19
8 1 34 3.000 0 0 23
9 9 28 3.000 0 0 25
10 3 23 3.000 4 0 29
11 8 15 3.000 5 6 16
12 11 13 3.000 0 0 15
13 24 39 4.000 0 0 24
14 21 27 4.000 0 0 23
15 11 16 4.000 12 3 18
16 8 19 4.500 11 0 18
17 18 29 5.000 0 0 32
18 8 11 5.286 16 15 21
19 6 25 5.500 0 7 21
20 5 14 6.000 0 0 24
21 6 8 6.606 19 18 26
22 7 30 7.000 0 0 34
23 1 21 7.000 8 14 26
24 5 24 7.500 20 13 31
25 9 20 7.500 9 0 28
26 1 6 7.571 23 21 27
27 1 17 8.722 26 0 28
28 1 9 9.649 27 25 30
29 3 10 10.000 10 0 31
30 1 38 10.545 28 0 33
31 3 5 10.875 29 24 37
32 18 37 12.500 17 0 35
33 1 36 12.957 30 0 36
34 2 7 13.500 0 22 35
35 2 18 16.667 34 32 36
36 1 2 17.153 33 35 38
37 3 12 24.500 31 0 38
38 1 3 34.022 36 37 0
Table 2: Agglomeration Schedule

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From the agglomeration schedule we were able to manually divide the entries into three
clusters. The dendrogram of the analysis is shown :
Figure 1: Dendrogram

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The data set was subjected to K mean cluster analysis to verify the number of clusters. In k
mean cluster analysis the number of clusters were entered as 3 and the analysis was done with
a maximum 10 iterations.
Final Cluster Centers
Cluster
1 2 3
Price OR Value for Money 4.2 3.0 2.1
Location 4.8 3.7 2.1
Food Habits Availabe 3.7 2.8 1.8
Lodging with amenities (e.g.
WIFI, Bar,etc.)
3.7 1.8 2.4
Services Available (e.g.
Para-gliding,jet skiing,etc.)
3.2 3.0 2.3
Personal Safety 4.5 4.0 2.5
Regional or Local Culture 3.4 4.3 2.5
Connectivity to Location 4.2 4.5 2.1
Table 3: Final Cluster centres
The ANOVA table of the analysis is:
ANOVA
Cluster Error
F Sig.Mean Square df Mean Square df
Price OR Value for Money 14.898 2 .540 36 27.596 .000
Location 23.011 2 .599 36 38.407 .000
Food Habits Availabe 11.652 2 .605 36 19.265 .000
Lodging with amenities (e.g.
WIFI, Bar,etc.)
11.105 2 .615 36 18.051 .000
Services Available (e.g.
Para-gliding,jet skiing,etc.)
2.519 2 .968 36 2.601 .088
Personal Safety 11.880 2 .396 36 30.034 .000
Regional or Local Culture 5.869 2 .708 36 8.287 .001
Connectivity to Location 14.312 2 .604 36 23.705 .000
Table 4: ANOVA table

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The F values in the ANOVA table show that the individual factors are significant and contribute
towards the formation of clusters. We have been able to classify the data into three clusters: -
 Cluster 1: Pleasure Tourist
 Cluster 2: Quick Benefit tourist
 Cluster 3: The Wanderer
Figure 2: The graphical Representation of the Final Cluster centres

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6. RESULTS
Cluster 1: Leisure Tourist
• These tourists want to rejuvenate and revitalize with comfort, while enjoying a break
from mundane routine of life
• Examples of this type of tourism are cruising while vacationing or simple relaxing on
a beach
• High preference to Location, Safety & Value for Money
• Low preference to Availability of activities & local culture
Cluster 2: Quick Benefit Tourist
• As a bit of an escapist, simplicity is searched for
• Worry-free travel and spending time with family and friends is preferred
• Things are rarely planned in advance; so locations with good connectivity is preferred
• High preference to Local Cultural Experience
• Low preference to monetary issue.
Cluster 3: Wanderer
• They get the "I've been in one place too long" itch
• They can have or make an adventure anywhere they go
• Don’t need any reason to travel, driven by Wanderlust
• Mostly embark on road-trips
• Though not a deciding factor, a bit higher preference is given to better staying
conditions
From the cluster analysis, the sample has been segmented into three segments each with its
own preference. It is now upon the firm to decide on which segment to target. The Wanderer
is a group of students who like to travel a lot. For them the thrill of travelling is more important
than any other factor. Such people will always grab an opportunity to travel.
The second cluster or quick benefit traveller is a person who wishes to go on short trips to
places that are well connected. They are not interested in hotel and lodging facilities. The third
category or pleasure tourist will go to places that are safe and are value for money. Such tourists
will visit places that are well known destinations.

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7. CONCLUSION AND RECOMMENDATION
The firm should target the segment of quick benefit travellers as these students with places that
have great cultural heritage and not on amusement values. These students will be happier with
packages that take them to places like Konark in Orissa.
For the pleasure tourists, the firm will have to offer prime locations and will have to offer very
good deals to them to bring them on board. Promotional offers will have to be provided to these
tourists.
• The Wanderer is a group of students who like to travel a lot. For them the thrill of
travelling is more important than any other factor. Such people will always grab an
opportunity to travel.
• The second cluster or quick benefit traveller is a person who wishes to go on short trips
to places that are well connected. They are not interested in hotel and lodging facilities.
• The third category or pleasure tourist will go to places that are safe and are value for
money. Such tourists will visit places that are well known destinations.
• As the firm is a new start-up it would be recommendable to target the Quick benefit
segment as these people do not care to spend money, but want a good experience. They
are also on the lookout for quick short vacations like weekend getaways.
8. FUTURE SCOPE:
• A set of packages can be developed and a subsequent research can be performed, to
develop the optimum package for each segment
• Since there is a clear demarcation, targeted advertising can be done, hence improving
chances of a transaction
• Similar segmentation can be done for any B-School or college environments across the
country

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9. QUESTIONNAIRE:

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10. EXHIBITS
Appendix:
10.1Hierarchical Cluster Analysis:
a.
Case Processing Summarya,b
Cases
Valid Missing Total
N Percent N Percent N Percent
39 100.0 0 .0 39 100.0
a. Squared Euclidean Distance used
b. Average Linkage (Between Groups)
b.
Stage
Cluster Combined
Coefficients
Stage Cluster First Appears
Next StageCluster 1 Cluster 2 Cluster 1 Cluster 2
1 8 32 1.000 0 0 5
2 15 31 1.000 0 0 6
3 16 22 1.000 0 0 15
4 3 4 2.000 0 0 10
5 8 33 2.500 1 0 11
6 15 26 2.500 2 0 11
7 25 35 3.000 0 0 19
8 1 34 3.000 0 0 23
9 9 28 3.000 0 0 25
10 3 23 3.000 4 0 29
11 8 15 3.000 5 6 16
12 11 13 3.000 0 0 15
13 24 39 4.000 0 0 24
14 21 27 4.000 0 0 23
15 11 16 4.000 12 3 18
16 8 19 4.500 11 0 18
17 18 29 5.000 0 0 32
18 8 11 5.286 16 15 21
19 6 25 5.500 0 7 21
20 5 14 6.000 0 0 24
21 6 8 6.606 19 18 26
22 7 30 7.000 0 0 34
23 1 21 7.000 8 14 26
24 5 24 7.500 20 13 31

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25 9 20 7.500 9 0 28
26 1 6 7.571 23 21 27
27 1 17 8.722 26 0 28
28 1 9 9.649 27 25 30
29 3 10 10.000 10 0 31
30 1 38 10.545 28 0 33
31 3 5 10.875 29 24 37
32 18 37 12.500 17 0 35
33 1 36 12.957 30 0 36
34 2 7 13.500 0 22 35
35 2 18 16.667 34 32 36
36 1 2 17.153 33 35 38
37 3 12 24.500 31 0 38
38 1 3 34.022 36 37 0
c.
Cluster
Membership
Case 5 Clusters 4 Clusters 3 Clusters 2 Clusters
1 1 1 1 1
2 2 2 1 1
3 3 3 2 2
4 3 3 2 2
5 3 3 2 2
6 1 1 1 1
7 2 2 1 1
8 1 1 1 1
9 1 1 1 1
10 3 3 2 2
11 1 1 1 1
12 4 4 3 2
13 1 1 1 1
14 3 3 2 2
15 1 1 1 1
16 1 1 1 1
17 1 1 1 1
18 5 2 1 1
19 1 1 1 1
20 1 1 1 1
21 1 1 1 1
22 1 1 1 1
23 3 3 2 2
24 3 3 2 2

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25 1 1 1 1
26 1 1 1 1
27 1 1 1 1
28 1 1 1 1
29 5 2 1 1
30 2 2 1 1
31 1 1 1 1
32 1 1 1 1
33 1 1 1 1
34 1 1 1 1
35 1 1 1 1
36 1 1 1 1
37 5 2 1 1
38 1 1 1 1
39 3 3 2 2

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d. Dendrogram

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10.2 K-Mean Cluster Analysis
a.
Iteration Historya
Iteration
Change in Cluster Centers
1 2 3
1 2.349 3.167 2.429
2 .121 .816 .260
3 .000 .000 .000
a. Convergence achieved due to no or small change in cluster centers. The
maximum absolute coordinate change for any center is .000. The current
iteration is 3. The minimum distance between initial centers is 6.245.
b.
Cluster Membership
Case Number Cluster Distance
1 2 2.279
2 2 2.682
3 3 1.953
4 3 1.820
5 3 2.411
6 1 2.079
7 1 2.815
8 1 1.250
9 1 2.359
10 3 2.562
11 1 1.218
12 2 3.723
13 1 1.940
14 3 1.820
15 1 1.079
16 1 1.563
17 1 2.376
18 2 2.804
19 1 1.812
20 1 2.376
21 1 2.350
22 1 1.674
23 3 1.346
24 3 1.887
25 1 1.613
26 1 1.537
27 1 1.744

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28 1 2.299
29 2 2.351
30 1 3.493
31 1 1.133
32 1 1.266
33 1 1.524
34 1 2.001
35 1 1.960
36 1 2.954
37 2 2.048
38 1 2.743
39 3 1.820
Final Cluster Centers
Cluster
1 2 3
Price OR Value for Money 4.2 3.0 2.1
Location 4.8 3.7 2.1
Food Habits Availabe 3.7 2.8 1.8
Lodging with amenities (e.g.
WIFI, Bar,etc.)
3.7 1.8 2.4
Services Available (e.g. Para-
gliding,jet skiing,etc.)
3.2 3.0 2.3
Personal Safety 4.5 4.0 2.5
Regional or Local Culture 3.4 4.3 2.5
Connectivity to Location 4.2 4.5 2.1

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c.
ANOVA
Cluster Error
F Sig.
Mean
Square df
Mean
Square df
Price OR Value for
Money
14.898 2 .540 36 27.596 .000
Location 23.011 2 .599 36 38.407 .000
Food Habits Availabe 11.652 2 .605 36 19.265 .000
Lodging with amenities
(e.g. WIFI, Bar,etc.)
11.105 2 .615 36 18.051 .000
Services Available
(e.g. Para-gliding,jet
skiing,etc.)
2.519 2 .968 36 2.601 .088
Personal Safety 11.880 2 .396 36 30.034 .000
Regional or Local
Culture
5.869 2 .708 36 8.287 .001
Connectivity to
Location
14.312 2 .604 36 23.705 .000

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The F tests should be used only for descriptive purposes because the clusters have been chosen
to maximize the differences among cases in different clusters. The observed significance levels
are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the
cluster means are equal.
Number of Cases in each Cluster
Cluster 1 25.000
2 6.000
3 8.000
Valid 39.000
Missing .000