SOLUTION: St Edwards University Wk 5 Business Airline Cluster Analysis Question

The Bill Munday School of Business
St. Edward’s University
Airline Satisfaction: Market Segmentation using Cluster Analysis
Cluster analysis is a statistical process that encompasses a number of different algorithms and
methods for grouping objects of similar kind into respective categories. A question facing
marketing researchers is how to organize observed data into meaningful structures. In other
words, cluster analysis is an exploratory data analysis tool which aims at sorting different objects
into groups in a way that the degree of association between two objects is maximized if they
belong to the same group and minimal otherwise.
One method of cluster analysis is K-means. This procedure attempts to identify relatively
homogeneous groups of cases based on selected characteristics, using an algorithm that can
handle large numbers of cases. However, the algorithm requires the researcher to specify the
number of clusters. The process classifies the cases by updating cluster centers iteratively until a
solution is reached. The K-means method produces the specified k different clusters of greatest
possible distinction. The researcher can save cluster membership, distance information, and final
cluster centers. Analysis of variance F statistics in an ANOVA table can also be requested.
While these statistics are opportunistic (the procedure tries to form groups that do differ), the
relative size of the statistics provides information about each variable’s contribution to the
separation of the groups. Combined with frequency analysis, profiles for different clusters can
be generated.
Another method of cluster analysis is the TwoStep Cluster Analysis procedure. It is an
exploratory tool designed to reveal natural groupings (or clusters) within a dataset that would
otherwise not be apparent. The algorithm employed by this procedure has several desirable
features that differentiate it from traditional clustering techniques. The TwoStep cluster analysis
uses a scalable algorithm designed to handle very large datasets. Capable of handling both
continuous and categorical variables, it requires only one data pass in the procedure. In the first
step of the procedure, pre-clusters are formed of the records by forming them into many small
Professor Wesley Pollitte prepared this note to provide material for class discussion.
Copyright © 2018 by Wesley Pollitte.
The Bill Munday School of Business
St. Edward’s University
sub-clusters. Then, in the second step, the clustering algorithm forms the sub-clusters from the
pre-cluster step into the desired number of clusters. If the desired number of clusters is unknown,
then the TwoStep Cluster Component will find the proper number of clusters automatically.
As a marketing manager for a major airline, you are interested in targeting market segments with
marketing actions specifically designed for the segment. You have noticed, while working with
the data, that satisfaction, price sensitivity, and age tend to be related and wonder if these
variables could be used to produce distinct market segments. In addition, you have been hearing
that passengers that fly frequently and take long flights are complaining more than average, and
you are curious if these people are loyal customers, as measured by the number of loyalty cards
they have, and wondering if these passengers can be grouped by these variables and evaluated on
their satisfaction.
To answer the first question, you have decided to conduct a K-means cluster analysis with
frequency analysis to determine the profile of potential market segments. To investigate your
second question, you have decided to use TwoStep cluster analysis to determine if satisfaction is
different for passengers based on how much they fly, the length of their flight, and their
participation in loyalty programs. Please conduct the following analysis and submit as WORD
document. Please answer and label each of your answers with the question number. Please
include the requested SPSS output with your answers.
Part I K-means Cluster Analysis
1. Using airline satisfaction cluster analysis dataset.sav, standardize the variables Satisfaction,
Price Sensitivity, and Age using Descriptives Analysis and saving the standardized values
and variables.
2. Conduct a K-means cluster analysis using the standardized variables (zscores) for satisfaction,
price sensitivity, and age to produce 3 clusters. Be sure your analysis converges and
remember to save the cluster membership.




How many are in each cluster?
Include the Number of Cases in Each Cluster Table.
Do each of the 3 variables significantly contribute to the cluster solution?
Include the ANOVA table.
3. Produce a bar graph from the Final Cluster Center Table.


Interpret the results for each of the 3 clusters for each of the 3 variables.
Include the bar graph.
The Bill Munday School of Business
St. Edward’s University
4. Conduct a frequency analysis for each cluster using the variables Age Range, Gender, and
Type of Travel. Remember to use Select Cases to turn on desired cluster separately before
doing the frequency analysis for each cluster.






Interpret the results for cluster 1.
Include the frequency analysis for Age Range, Gender and Type of Travel for cluster 1.
Interpret the results for cluster 2.
Include the frequency analysis for Age Range, Gender and Type of Travel for cluster 2.
Interpret the results for cluster 3.
Include the frequency analysis for Age Range, Gender and Type of Travel for cluster 3.
5. What marketing actions would you recommend for cluster 1 and why?
6. What marketing actions would you recommend for cluster 2 and why?
7. What marketing actions would you recommend for cluster 3 and why?
Part II TwoStep Cluster Analysis
1. Using the variables Number of Flights per year, Number of other Loyalty Cards, and Flight
time in Minutes, with Satisfaction as the evaluation variable, conduct a TwoStep cluster
analysis. Have SPSS determine the number of clusters automatically using Log-likelihood
and AIC.



How many clusters were generated?
What is the ratio of sizes of the largest and smallest clusters?
Include the Cluster Sizes view from Model View.
2. Are the four variables important in predicting the clusters? Include the Predictor Importance
view for Model Viewer.
3. Compare each of the clusters. This can be done by setting the left view to Clusters and the
right view to Custer Comparison in Model Viewer.




Which cluster has the highest satisfaction?
Which cluster has the lowest satisfaction?
Of the three input variables, which one has the greatest impact on satisfaction?
Include the Cluster Comparison for the Model Viewer.
4. Evident from the analysis, people who on average have a low number of other loyalty cards
and take the most flights per year are the least satisfied. What would you recommend your
airline do to improve the satisfaction of this group?
SatisfactionAge
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PriceSensitivity
NoofFlightsp.y
NoofFlightsp.y.grouped
TypeofTravel
No.ofotherLoyaltyCards
2
41 41 to 50 Business travel
0
1
9 1 to 10
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2
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2
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0
1
29 21 to 30 Business travel
1
2
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1
2
32 31 to 40 Mileage tickets
0
3
34 31 to 40 Mileage tickets
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45 41 to 50 Personal Travel
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2
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0
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0
2
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1
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4
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26
26
6
10
6
0
25
45
22
9
14
8
24
13
30
30
30
9
10
18
24
16
35
14
38
56
14
14
21
47
36
20
8
30
21
31
38
9
16
31
13
8
4
17
21
31 to 40
1 to 10
21 to 30
21 to 30
1 to 10
11 to 20
1 to 10
0
21 to 30
41 to 50
21 to 30
1 to 10
11 to 20
1 to 10
21 to 30
11 to 20
21 to 30
21 to 30
21 to 30
1 to 10
11 to 20
11 to 20
21 to 30
11 to 20
31 to 40
11 to 20
31 to 40
51 to 60
11 to 20
11 to 20
21 to 30
41 to 50
31 to 40
11 to 20
1 to 10
31 to 40
21 to 30
31 to 40
31 to 40
1 to 10
11 to 20
31 to 40
11 to 20
1 to 10
1 to 10
11 to 20
21 to 30
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Business travel
Mileage tickets
Mileage tickets
Mileage tickets
Mileage tickets
Mileage tickets
Mileage tickets
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Personal Travel
Business travel
Business travel
Business travel
Business travel
Business travel
Mileage tickets
0
0
2
0
3
0
1
0
2
0
0
0
2
1
0
1
0
0
0
0
1
0
0
6
0
3
1
0
3
2
0
0
1
0
0
2
2
3
3
2
0
2
1
0
0
1
1
5
2
3
4
4
5
5
5
3
2
3
3
3
3
5
2
3
2
3
2
4
4
4
5
3
3
2
2
2
4
4
4
5
3
5
4
3
2
3
3
4
4
4
2
2
4
2
48
43
74
51
50
35
30
46
85
28
37
71
19
25
51
69
31
68
55
70
34
50
35
34
20
70
43
21
15
38
57
51
34
52
22
67
43
40
61
69
17
43
40
69
85
77
41
40-49
40-49
70-79
50-59
50-59
30-39
30-39
40-49
80+
20-29
30-39
70-79
0-19
20-29
50-59
60-69
30-39
60-69
50-59
70-79
30-39
50-59
30-39
30-39
20-29
70-79
40-49
20-29
0-19
30-39
50-59
50-59
30-39
50-59
20-29
60-69
40-49
40-49
60-69
60-69
0-19
40-49
40-49
60-69
80+
70-79
40-49
Female
Female
Male
Male
Male
Female
Male
Male
Male
Male
Female
Male
Female
Male
Male
Female
Female
Female
Female
Female
Female
Male
Female
Female
Female
Male
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Male
Female
Male
Male
Female
Male
Female
1
1
1
1
1
1
1
2
2
2
1
1
1
1
2
1
1
1
1
2
1
2
1
1
1
1
1
2
1
1
1
1
1
2
1
1
2
1
1
2
0
2
1
1
1
2
2
0
20
42
13
13
5
0
31
23
48
40
11
15
16
40
52
5
14
27
17
5
16
8
9
10
35
11
43
23
8
28
17
4
24
15
50
17
23
25
34
42
29
18
56
11
27
10
0
21 to 30
41 to 50
11 to 20
11 to 20
1 to 10
0
31 to 40
21 to 30
41 to 50
31 to 40
11 to 20
11 to 20
11 to 20
41 to 50
51 to 60
1 to 10
11 to 20
21 to 30
11 to 20
1 to 10
11 to 20
1 to 10
1 to 10
11 to 20
31 to 40
11 to 20
41 to 50
21 to 30
1 to 10
21 to 30
11 to 20
1 to 10
21 to 30
11 to 20
41 to 50
11 to 20
21 to 30
21 to 30
31 to 40
41 to 50
21 to 30
11 to 20
51 to 60
11 to 20
21 to 30
11 to 20
Personal Travel
Business travel
Personal Travel
Business travel
Business travel
Business travel
Business travel
Business tr …
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