This analysis has been done applying the knowledge developed during the course of statistic methods and applications to a real business. A conjoint analysis was conducted to estimate the partial worths of the different features of running shoes. This analysis helps to figure out which attribute and level is most important according to the constumer's view and costumize the offer of the firm considering the different interests of the market. It allows to orientate the product to a target market.

1. CONJOINT ANALYSIS APPLIED IN RUNNING SHOES
PRELIMINARY ANALYSIS
CONJOINT ANALYSIS & SEGMENTATION ANALYSIS
COMMENTS AND CONCLUSIONS
Aqeel Aslam
Paolo Balasso
Alberto Ballan
Alessandro De Lorenzi
ORTHOGONAL DESIGN & CONJOINT QUESTIONNAIRE

2. Masep is a shop that sells different kind of sport
clothing, shoes and other accessories, in Thiene
(VI)
2
INTRODUCTION
The analysis, focused in running shoes, is especially
Inherited to the products sold by Masep :

3. The data was collected using a questionnaire
through Internet. It has allowed to pick up a
sample with different demographic features
3
PRELIMINARY ANALYSIS
According to the first step, a survey has been performed for
an exploratory analysis. The goal was inhereted to
investigate the factors that the costumers are interested in.
This step wants to find the variables that will be
implemented in the conjoint analysis.
Preliminary Procedure

4. 4
PRELIMINARY ANALYSIS
Impermeability
Material
Weight
Suitable field
Exterior design
Life span
Brand
Cushioning
Age
Gender
Average weekly Runs
Weekly distance covered
Yearly shoes bought
Type of occupation
Diligence in the activity
Possible characteristics to analyze Demographic informations

5. 5
INTRODUCTION
The questionnaire was created using
Google survey

6. In order to rank the importance of the different
attributes an ANOVA test was performed but the
Levine test was not significant(p-value= 0.37904).
The attributes implemented in CA were choosen
considering the owner’s issues and other
considerations described later
6
PRELIMINARY ANALYSIS
The following slides want to describe the sample with
descriptive indicators such as Standard Deviation and
mean.
To sum up the demographic informations a pie charts is
used insted of the hystogramm used for summarizing
attribute informations.
Preliminary Analysis

7. 7
PRELIMINARY ANALYSIS
Descriptive analysis: Demographic Informations
The sample does not
rappresent the whole
population but mainly
male and young people

8. 8
PRELIMINARY ANALYSIS

9. 9
PRELIMINARY ANALYSIS
Descriptive analysis: Attributes Summery
𝑥 = 6,38
SD = 2,61
𝑥 = 7,87
SD = 2,42
𝑥 = 6,54
SD = 2,01

10. 10
PRELIMINARY ANALYSIS
𝑥 = 7,19
SD = 2,12
𝑥 = 8,67
SD = 2,14
𝑥 = 7,41
SD = 2,04

11. 11
PRELIMINARY ANALYSIS
𝑥 = 7,61
SD = 1,82
𝑥 = 6,19
SD = 2,59

12. 12
FACTORIAL DESIGN
Materials
Suitable field
Life span
Impermeability
ATTRIBUTES NOT
IMPLEMENTED IN
CONJOINT
ANALYSIS
Few runners interested in it
It does not influence buying intention, it
is related to the kind of running activity
Pro runners run more than others, this is the
reason why they buy more pairs yearly
It is not up to the kind of shoes ( ~ 800 km for
each shoes)
Runners were interested in them, but they were no
sensitive to the technical materials that running
shoes are made by

13. 13
FACTORIAL DESIGN
Cushioning
Brand
External design
Weight
ATTRIBUTES
IMPLEMENTED IN
CONJOINT
ANALYSIS
The most important attribute
according to runners
Runners do not consider it so much
but important to detect if there are
brand preference effects
Easy identification of three kinds of
design: Thin, neutral, bulky
Considered important by the
runners interviewed

14. 14
PRELIMINARY ANALYSIS
Frequency analysis on Yearly shoes bought vs Running club’s members
We have to reject the hypothesis that classification of rows and columns are indipendent
The rating of a running club’s member becomes more important because their buying frequency is greater
So we are interested in assessing if they evaluate attributes differently compered to no-members
Using chi-square test no significant dependence has been found between higher attribute’s values and running
club’s members

15. 15
PRELIMINARY ANALYSIS
Running club’s members vs. weekly distance covered
We have to reject the hypothesis that classification of rows and columns are indipendent.
In order to verify why members have an high buying frequency could be interesting
evaluating if there is a relation between members and high weekly distance covered
Since shoes have the same life span ( about 800 km) and the most members run more than 20 km a
week , they will buy more than 1 shoes a year.

16. STAGES FOR CONJOINT ANALYSIS
1. Identification of attributes and levels using the results of
explorative questionnaire.
2. Definition of profiles and conjoint analysis method
3. Drawing an appropriate paper and pencil format, with
demografical information and labels with the different profiles
4. Estimates of part-worth utilities and relative importance.
5. Segmentation analysis
6. Results
16

17. 1. Identification of ATTRIBUTES and levels
Cushioning
Brand
Design
Weight
The most important attribute according to
runners
Runners do not consider it so much but
the owner of the shop was interested in
testing this attribute deeper
Easy identification of three kind of design:
Thin, neutral, bulky
Considered important by the runners
interviewed
CHOSEN
ATTRIBUTES
17

18. 1. Identification of attributes and LEVELS
Cushioning
Brand
Design
Weight
How:
1. Complete
2. Partial
3. Only under the heel
1. Mizuno
2. New Balance
3. Asics
1. Tapered
2. Medium
3. Bulky
1. 225 gr.
2. 288 gr.
3. 335 gr.
3 types on
the market
The greatest
market share
Common
shapes
Statistical
analysis
18

19. A sample randomly collected from the
internet was analyzed using Statgraphics
Different classes
were individuated
The central
point of the
intervals are:
1. 225 gr.
2. 288 gr.
3. 335 gr.
Frequency
Weight (gr.)
1. Identification of attributes and LEVELS
19

20. Full Profile Approach
Too many factors
Fractional Factorial
Orthogonal Design
It eliminates the interaction
between levels of different factors
evaluating only main effects
Design is orthogonal if each factor
can be evaluated independently
from all other factors
Hierarchical assumption
2. Definition of profiles and conjoint analysis method
Each combination of the
factors’ levels generates one
profile that is evaluated by
responders
It consists in a Full
Factorial Design
20

21. Attributes Cushioning Weight (gr.) Brand Design
Levels
1 Complete 225 Mizuno Tapered (A)
2 Partial 280 New Balance Medium (B)
3 Only heel 335 Asics Bulky (C)
4 attributes with 3 levels each
Total number of combinations:
3x3x3x3= 81 profiles !
“Orthoplan” procedure
of SPSS
81 9 profiles
2. Definition of profiles and conjoint analysis method
21

22. Caracteristic of our Conjoint Analysis:
• Metric C. A.
• Part-worth model
• Orthogonal plan
2. Definition of profiles and conjoint analysis method
22

23. 3. Drawing an appropriate paper and pencil format
23

24. 28 runners answered the
conjoint questionnaire
3. Drawing an appropriate paper and pencil format
24

25. 3. Drawing an appropriate paper and pencil format
Disaggregate overall results Aggregate overall results
Collected data were elaborated by SPSS software, obtaining
different types of results:
25

26. CONJOINT QUESTIONNAIRE
General info
Runner’s
attitudes
26

27. CONJOINT QUESTIONNAIRE
28 runners answered the
conjoint questionnaire
27

28. CONJOINT QUESTIONNAIRE
student
46%
employee
29%
retired
3%
enterpreneur
11%
housewife
7%
manager
4%
male
75%
female
25%
Frequency of the age
In the following graphs are
described the general
information about the sample
that responded to the
conjoint questionnaire
Mean of the age: 33,2
Median of the age: 31
8
6
9
5
age < 24 24<= age <34 34<=age<44 age >= 44
28

29. CONJOINT QUESTIONNAIRE
less than 3 times
in a week
57%
3 or 4 times in a
week
32%
more than 4
times in a week
11%
less than 8 km
in a week
32%
8 or 20 km in a
week
43%
more than 20
km
25%
How many times
do you go
running in a
week?
How many
kilometres do you
run in a week?
29

30. CONJOINT QUESTIONNAIRE
Not members 64%
Members
36%
less than 1 pair
of shoes
26%
1 pair of shoes
37%
more than 1
pair of shoes
37%
How many people
joined a club:
How many pair
of running
shoes do you
buy in a year?
30

31. CONJOINT ANALYSIS
Conjoint analysis results for
subject1 :
-Student
-Male
-Run 3 or 4 times a week
-Run between 8 and 20 km in a week
-Not member
-One running shoes in a year
31

32. 5
7
INDIVIDUAL UTILITY FUNCTION
utility (brand* Asics ) + utility (weigth*335gr)+ utility
(cushioning*solo tallone) + utility (design*B ) +
constant= 5 predicted score
actual score
utility (brand* New Balance) + utility
(weigth*225gr)+ utility
(cushioning*parziale) + utility
(design*A) + constant= 8
actual score
1th respondent
predicted score
32

33. Conjoint analysis – Conclusions
RESULTS AND CONCLUSION
33

34. Conjoint analysis – Overall Results
week run commitment age buy in 1 year Job
overall < 3 3 or 4 > 4 not joined joined < 24 24<=x<34 34<=x<44 >= 44 < 1 1 > 1 Student Employee Manager
imp cushion 33.18 27.51 35.7 36.15 30.95 27.37 30.94 40.77 24 40.5 32.65 30.79 38.81 35.5 25.54 28.57
imp weigth 15.41 20.87 13.69 11.33 18.29 11.46 20.42 18.49 11.99 11.89 25.22 11.33 12.76 20.61 11.22 8.44
imp brand 26.94 23.73 28 29.25 27.49 32.04 24.91 16.8 38.61 23.35 24.81 30.49 23.66 23.07 36.25 28.3
imp design 24.47 27.9 22.61 23.27 23.27 29.14 23.73 23.94 25.4 24.26 17.33 27.4 24.78 20.83 26.99 34.69
cushion1 0.9563 0.8025 1.0278 1.0317 0.8827 0.8 0.9012 1.2667 0.4286 1.3889 1.0159 0.9394 1.0606 1.0855 0.5694 0.7222
cushion2 -0.2698 -0.0123 -0.1944 -0.7302 -0.0432 -0.1 0.0123 0.0667 -0.381 -0.778 0.0159 -0.1212 -0.6061 -0.0171 -0.3056 -0.5278
cushion3 -0.6865 -0.7901 -0.8333 -0.3016 -0.8395 -0.7 -0.9136 -1.3333 -0.0476 -0.6111 -1.0317 -0.8182 -0.4545 -1.0684 -0.2639 -0.1944
weigth1 0.0873 0.0617 0.1389 0.0317 0.0864 0.1 0.0864 0.2 0.0476 0.0556 0.1111 0.0909 0.0606 0.1111 0.1111 0.0556
weigth2 0.2063 0.4691 0.0278 0.1746 0.2716 0.0667 0.2716 0.4667 0 0.1667 0.5873 0.0909 0.0909 0.3675 0.0694 -0.0278
weigth3 -0.2937 -0.5309 -0.1667 -0.2063 -0.358 -0.1667 -0.358 -0.667 -0.0476 -0.2222 -0.6984 -0.1818 -0.1515 -0.4786 -0.1806 -0.0278
brand1 -0.127 -0.1605 0.0556 -0.3968 -0.0432 -0.1 0.0123 -0.1333 -0.2381 -0.1111 0.0635 -0.0606 -0.2727 -0.0427 -0.3889 0.0556
brand2 0.4802 0.358 0.5833 0.4603 0.4938 0.5667 0.5679 0.5333 0.381 0.3333 0.4444 0.5455 0.4242 0.5726 0.4028 0.3889
brand3 -0.3532 -0.1975 -0.6389 -0.0635 -0.4506 -0.4667 -0.5802 -0.4 -0.1429 -0.2222 -0.5079 -0.4848 -0.1515 -0.5299 -0.0139 -0.4444
design1 -0.1389 -0.0864 0 -0.4444 -0.0062 0.0667 -0.0247 0.2667 -0.1905 -0.6111 -0.0317 0.0303 -0.3939 0.0598 -0.3472 -0.1944
design2 0.2778 0.4321 0.1667 0.2698 0.2716 0.2333 0.2346 0.7333 0.2381 0.1667 0.254 0.2424 0.3333 0.265 0.5278 0.0556
design3 -0.1389 -0.3457 -0.1667 0.1746 -0.2654 -0.3 -0.2099 -1 -0.0476 0.4444 -0.2222 -0.2727 0.0606 -0.3248 -0.1806 0.1389
34

35. Conclusioni
Considering the overall
results:
The most important factor is
cushioning with the value of
33,18
The less important factor is
weight, with the value of
15,41
35

36. Conclusions:
Overall utilities
The preferred levels of the factors are: COMPLETE cushioning,
MEDIUM weight, NEW BALANCE as brand, design B
36

37. Conclusioni
37

38. Conclusioni
32,65 30,79
38,81
25,22
11,33 12,76
24,81
30,49
23,66
17,33
27,4
24,78
0
10
20
30
40
50
< 1 1 > 1
Importance vs. buying frequency
imp cushion
imp weigth
imp brand
imp design
30,94
40,77
24
40,5
20,42
18,49
11,99 11,89
24,91
16,8
38,61
23,3523,73 23,94
25,4 24,26
0
5
10
15
20
25
30
35
40
45
< 24 24<=x<34 34<=x<44 >= 44
Importance vs. Age
imp cushion
imp weigth
imp brand
imp design
• Mostly customers are inspired by the importance of cushion except the age
between 34 and 44 and they prefer importance of brand.
38

39. Conclusioni
27,51
35,7 36,15
20,87
13,69
11,33
23,73
28
29,25
27,9
22,61 23,27
0
5
10
15
20
25
30
35
40
< 3 3 or 4 > 4
Importance vs. weekly running
imp cushion
imp weigth
imp brand
imp design
• Most important factor is cushioning but design also influence people who
run less then 3 days.
39

40. Conclusioni
35,5
20,61
23,07
20,83
25,54
11,22
36,25
26,99
28,57
8,44
28,3
34,69
0
5
10
15
20
25
30
35
40
imp cushion imp weigth imp brand imp design
Importance vs. Occupations
Student
Employee
Manager
30,95
18,29
27,49
23,27
27,37
11,46
32,04
29,14
0
5
10
15
20
25
30
35
imp cushion imp weigth imp brand imp design
Importance vs. not joined/joined
not joined
joined
• In first graph, cushioning and weight are important factors for students. On the other hand,
Employees prefer brand and design influence Managers.
• In second graph, Brand and design have great importance for the members of the clubs.
Cushioning and weight attract non-members.
40

41. Conclusioni
1,0606
-0,6061
-0,4545
0,0606 0,0909
-0,1515
-0,2727
0,4242
-0,1515
-0,3939
0,3333
0,0606
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
1,2
cushion1 cushion2 cushion3 weigth1 weigth2 weigth3 brand1 brand2 brand3 design1 design2 design3
Utilities vs. buying frequency > 1
> 1
• This slide is important to evaluate the attributes, that person with high
buying frequency consider more important.
• The complete cushioning is preferred as compared to others.
• The weight utilities is slightly higher in the light and neutral weights
instead of heavy ones. New balance and Design B have is also
preferred.
41

42. Conclusion
Summary
• According to the overall importance of attributes, the most preferred
attribute is cushioning. And the least preferred is weight.
• After the analysis of segmentation, there is clear evidence that
cushioning is the most important attribute.
• The summary of utility for the different levels of each attribute suggests
that the best profile is;
Complete cushioning + 288gr + Newbalance + Design B
• The above design is perfectly matched with the utilities of members and
the respondents with “buying frequency>1”.
42

43. THANK YOU FOR YOUR ATTENTION
43